Difference between revisions of "SRSP"

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'''Swarm robotics''' studies how a large number of robots can be designed and controlled so that a desired collective behavior results from local interactions among the robots and between the robots and the environment in which they act (Dorigo and Sahin 2004; Sahin 2005).
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'''Swarm robotics''' studies how to design groups of robots that operate without relying on any external infrastructure and on any form of centralized control. In a robot swarm, the collective behavior of the robots results from local interactions between the robots and between the robots and the environment in which they act. The design of robot swarms is guided by [[swarm intelligence]] principles. Such principles promote the realization of systems that are fault tolerant, scalable and flexible.
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Swarm robotics appears to be a promising approach when different activities must be performed concurrently, when high redundancy and the lack of a single point of failure are desired, and when
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The design of robot swarms is guided by swarm intelligence principles, which promote the realization of systems that are robust, scalable and flexible.
  
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Swarm robotics appears to be a promising approach for tasks such as demining, search and rescue, planetary or underwater exploration, and surveillance.
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it is technically unfeasible to setup an infrastructure to control the robots in a centralized way. Examples of tasks that could be profitably tackled using swarm robotics are demining, search and rescue, planetary or underwater exploration, and surveillance.
   
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==Origins==
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==Characteristics of swarm robotics==
  
   
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Swarm robotics has its origins in [[swarm intelligence]] and, in fact, could be defined as "embodied swarm intelligence". Initially, the main focus of swarm robotics research was to study and validate biological research (Beni, 2005). Collaboration between roboticists and biologists was vital to make swarm robotics a relevant research field. However, in recent years the focus of swarm robotics has been shifting: from a bio-inspired field of robotics, swarm robotics is becoming more and more an engineering field whose focus is on the development of tools and methods to solve real problems (Brambilla et al., 2013).
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The main characteristics of a swarm robotics system are:
  
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==Characteristics of swarm robotics==
   
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A robot swarm is a multi-robot system characterized by high redundancy and [[self-organization]]. Robots’ sensing and communication capabilities are local and robots do not have access to global information. The collective behavior of the robot swarm emerges from the interactions of each individual robot with its neighboring peers and with the environment. Typically, a robot swarm is composed of homogeneous robots, but examples of heterogeneous robot swarms exist (Dorigo et al., 2013).
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* robot swarms consist of a large group of autonomous robots;
  
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* robots cooperate to tackle a given task;
  
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* robots are situated in the environment and can act to modify it;
  
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* robots’ sensing and communication capabilities are local;
  
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* robots do not have access to centralized control and/or to global knowledge;
  
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* robots are redundant;
  
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* the behavior of the robot swarms results from the interactions of the robots with each other and with their environment, that is, the robot swarm self-organizes.
  
   
 
==Desirable properties of swarm robotics systems==
  
==Desirable properties of swarm robotics systems==
   
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The characteristics of swarm robotics are deemed to promote the realization of systems which are robust, scalable and flexible.
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The aforementioned characteristics of swarm robotics are deemed to promote the realization of systems that are fault tolerant, scalable and flexible.
   
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A robot swarm is able to cope well with the failure of one or more of its individuals: the loss of individuals does not result in the failure of the whole swarm. This robustness to failures is promoted by redundancy, that is, by having many homogeneous robots, and by the lack of a leader or centralized control.
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Swarm robotics promotes the development of systems that are able to cope well with the failure of one or more of their individuals: the loss of individuals does not imply the failure of the whole swarm. Fault tolerance is enabled by the high redundancy of the swarm: the swarm does not rely on any centralized control entity, leaders, or any individual robot playing a predefined role.
   
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A robot swarms is also able to cope well with changes in its group size: the introduction or removal of individuals does not result in a drastic change in the performance of the swarm. Scalability is promoted by local sensing and communication and by the lack of a centralized control.
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Swarm robotics promotes also the development of systems that are able to cope well with changes in their group size: ideally, the introduction or removal of individuals does not cause a drastic change in the performance of the swarm. Scalability is enabled by local sensing and communication: provided that the introduction and removal of robots does not dramatically modify the density of the swarm, each individual robot will keep interacting with approximately the same number of peers, those that are in its sensing and communication range.
   
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Finally, swarm robotics promotes the development of systems that are able to deal with a broad spectrum of environments and operating conditions. Flexibility is enabled by the distributed and self-organized nature of a robot swarm: in a swarm, robots dynamically allocate themselves to different tasks to match the requirements of the specific environment and operating conditions; moreover, robots operate on the basis of local sensing and communication without the need of pre-existing infrastructures and of global information on the environment.
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Finally, robot swarms are flexible, that is, are able to cope with a broad spectrum of different environment and tasks. Flexibility is promoted by redundancy, simplicity of the behaviors, lack of global knowledge and behavioral mechanisms as, for example, task allocation.
  
   
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== Potential applications of swarm robotics systems==
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== Potential applications of swarm robotics ==
   
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The desired properties of swarm robotics systems make them suitable for several fields of application.
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The properties of swarm robotics systems make them appealing in several potential fields of application.
   
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The use of robots for tackling dangerous tasks is clearly appealing as it eliminates or reduces risks for humans. The dangerous nature of these tasks implies a high risk of losing robots. Therefore, a fault-tolerant approach is required, making dangerous tasks an ideal field of application for robot swarms. Example of dangerous tasks that could be tackled using robot swarms are demining, search and rescue, and toxics cleanup.
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===Dangerous applications===
  
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Dangerous applications expose humans to the risk of injuries or casualties.
  
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An example of such applications is demining, where human operators have to search and defuse manually land mines.
 
   
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Potential applications for robot swarms are those in which it is difficult or even impossible to estimate in advance the resources needed to accomplish the task. For instance, allocating resources to manage an oil leak can be very hard because it is often difficult to estimate the oil output and to foresee its development. In these cases, a solution is needed that is scalable and flexible. A robot swarm could be an appealing solution: robots can be added or removed in time to provide the appropriate amount of resources and meet the requirements of the specific task. Example of tasks that might require an ''a priori'' unknown amount of resources are search and rescue, transportation of large objects, tracking, and cleaning.
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Tackling such applications with robots is a well suited solution, as this approach eliminates or reduces the risks for humans. However, since the risk of losing robots is high, a solution that is robust to failures is necessary, making dangerous applications an ideal field of application for robot swarms.
  
   
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Another potential field of application for swarm robotics are tasks that have to be accomplished in large or unstructured environments, in which there is no available infrastructure that can be used to control the robots - e.g., no available communication network or global localization system. Robot swarms could be employed for such applications because they are able to act autonomously without the need of any infrastructure or any form of external coordination. Examples of tasks in unstructured and large environments are planetary or underwater exploration, surveillance, demining, cleaning, and search and rescue.
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Example of dangerous applications that can be tackled using robot swarms are: demining, search and rescue, toxic cleaning, military applications.
  
   
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Some environments might change rapidly over time. For instance, in a post earthquake situation, buildings might collapse changing the layout of the environment and creating new hazards. In these cases, it is necessary to adopt solutions that are flexible and can react fast to events. Swarm robotics could be used to develop flexible systems that can rapidly adapt to new operating conditions. Example of tasks in environments that change over time are patrolling, disaster recovery, search and rescue, cleaning.
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===Applications with unknown or varying size===
  
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Very often it is not possible to determine in advance the resources necessary to tackle a problem. For instance, in case of an oil leak it is very often difficult to estimate the oil output and foreseen its development. On the one hand, developing a solution for a small leak could be useless if the leak is discovered to be large or if it increases over time. On the other hand, a solution engineered for a large leak might be a waste of resources if the leak remains small.
 
   
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==Scientific implications of swarm robotics==
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Applications with unknown or varying size must be tackled with solutions whose scale can easily adapt. This is the case of robot swarms: robots can be added or removed to fit different requirements of the applications.
 
   
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Beside being relevant to engineering applications, swarm robotics is also a valuable scientific tool. Indeed, several models of natural swarm intelligence systems have been refined and validated using robot swarms. For example, Garnier et al. (2005) validated the model of a collective decision-making behavior in cockroaches using robot swarms.
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Example of applications with unknown or varying size: search and rescue, transportation of large objects, tracking, cleaning, military applications.
  
   
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Swarm robotics has also been used to investigate, via controlled experiments, the conditions under which some complex social behaviors might result out of an evolutionary process. For example, robot swarms have been used to study the evolution of communication (Mitri et al., 2009) and collective decision making (Halloy et al., 2007).
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===Applications in large and unstructured environments===
  
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Some applications take place over large extensions of space.
 
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Other applications deal with unstructured environments. Unstructured environments are usually characterized by the absence of pre-available communication networks, global localization mechanisms or detailed maps. In such cases, it is necessary to adopt solutions that do not rely on pre-available infrastructures or information.
 
   
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==Current research axes==
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Swarm robotics systems are well suited for applications in large and unstructured environments, since such systems can tackle them faster than single robots and without relying on local information and or any a priori infrastructure.
  
   
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In this section, we present the main research axes of current research in swarm robotics. We follow the taxonomy presented in Brambilla et al. (2013).
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Examples of applications in unstructured and large environments are planetary or underwater exploration, military applications, surveillance, demining, cleaning, search and rescue.
  
   
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====Design====
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===Applications in dynamic environments===
  
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Some applications take place in environments that change over time. For instance, in a post earthquake situation, buildings can collapse changing the usable paths and creating new hazards to avoid.
 
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In applications with dynamic environments it is necessary to adopt solutions which are flexible and can react fast to events. Swarm robotics principles promote the development of flexible systems, making applications in dynamic environments an ideal field of application for robot swarms.
  
   
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The design of a robot swarm is a difficult endeavor: requirements are usually expressed at the collective level, but, eventually, the designer needs to define what the individual robots should do so that their interactions result in the desired collective behavior.
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Example of applications in dynamic environments are surveillance, disaster recovery, search and rescue, cleaning, military applications.
  
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Approaches to the design problem in swarm robotics can be divided into two categories: manual design and automatic design.
   
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In '''manual design''', the designer follows a trial-and-error process in which the behaviors of the individual robot are developed, tested and improved until the desired collective behavior is obtained. The software architecture that is most commonly adopted in swarm robotics is the ''probabilistic finite state machine''. Probabilistic finite state machines have been used to obtain several collective behaviors, including aggregation (Soysal and Sahin, 2005), chain formation (Nouyan et al., 2009), and task allocation (Liu and Winfield, 2010). Another common approach is based on ''virtual physics''. In this approach, robots and environment interact through virtual forces. This approach is particularly suited for spatially organizing collective behaviors, such as pattern formation (Spears et al. 2004) and collective motion (Ferrante et al., 2012). Currently, the main limit of manual design is that it completely relies on the ingenuity and expertise of the human designer: designing a robot swarm is more of an art than a science. A systematic and general way to design robot swarms is still missing, even though a few preliminary proposals have been made (Hamann and Worn, 2008; Berman et al., 2011; Brambilla et al., 2012).
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==Scientific implications of swarm robotics==
  
   
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In swarm robotics, '''automatic design''' has been mostly performed using the evolutionary robotics approach (Nolfi and Floreano, 2004). Typically, individual robots are controlled by a neural network whose parameters are obtained via [[Genetic algorithms|artificial evolution]] (Trianni and Nolfi, 2011). Evolutionary robotics has been used to develop several collective behaviors including collective transport (Groß and Dorigo, 2008) and development of communication networks (Huaert et al., 2008). One of the main limits of evolutionary robotics is that it is often difficult to define all the elements of the evolutionary process that will produce control software that allows the robot swarm to effectively accomplish the given task.
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Beside engineering applications, swarm robotics is also used as a scientific tool: in particular, many models derived from the analysis of natural swarm intelligence systems have been refined and validated using robot swarms. For example, Garnier et al. (2005) validated the model of a collective decision-making behavior in cockroaches using robot swarms.
 
   
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====Analysis====
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Swarm robotics has been used also to investigate the evolutionary conditions for the emergence of adaptive behaviour in groups of interacting individuals. The use of robot swarms allow the researcher to identify, in a controlled environment, the evolutionary pressures that lead to complex social behaviors, such as communication (Ampatzis et al., 2008) or collective decision-making (Francesca et al., 2012).
  
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The analysis of a robot swarm usually relies on models. A model of a robot swarm can be realized at two levels: the microscopic level, that is modeling the behaviors of the individual robots; or the macroscopic level, that is modeling the collective behavior of the swarm.
   
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Modeling the microscopic level allows one to represent in a detailed way the actions of all the individual robots composing the swarm. Unfortunately, microscopic modeling is problematic due to the large number of robots involved. Often, microscopic modeling relies on computer-based simulations (Kramer and Scheutz, 2007; Pinciroli et al., 2012).
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==Current research axis==
  
   
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Macroscopic models avoid the complexity and scalability issues of having to model each individual robot by considering only the collective behavior of the swarm. One of the most common macroscopic modeling approaches is the use of ''rate'' or ''differential equations'' (Martinoli et al., 2004; Lerman et al., 2005). Rate equations describe the time evolution of the ratio of robots in a particular state, that is, of robots that are performing a specific action or are in a specific area of the environment. Rate equations have been used to model many collective behaviors, including object clustering (Martinoli et al., 1999) and adaptive foraging (Liu and Winfield, 2010). Another common approach is the use of ''Markov chains'', which allow researchers to formally verify properties of a robot swarm (Dixon et al., 2012; Konur et al., 2012; Massink et al., 2013). Control theory has also been used to analyze whether a robot swarm eventually converges to a desired macroscopic state (Liu and Passino, 2004; Hsieh et al., 2008).
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In this section, some examples of current research in swarm robotics are presented, focusing on design, modeling and collective behaviors.
  
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For a full review of the literature, see Brambilla et al. (2013).
  
   
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A hybrid way of modeling robot swarms is based on ''Fokker-Plank'' and ''Langevin'' equations (Hamann and Worn, 2008; Berman et al., 2009; Prorok et al., 2011). Using these equations, one can model both the behavior of the individual robot, in the form of a deterministic component of the model; and the collective level of the swarm, in the form of a stochastic component of the model.
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===Design===
  
   
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====Collective behaviors====
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The design of a robot swarm is a difficult task: requirements are usually expressed at the collective level, but, eventually, the developer needs to define what the individual robots should do so that their interactions result in the desired collective behavior.
 
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A large part of the research effort in swarm robotics is directed towards the study of collective behaviors.
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Collective behaviors can be categorized into five main groups: spatially organizing behaviors, navigation behaviors, decision-making behaviors, human interaction behaviors, and other behaviors.
   
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Spatially-organizing behaviors focus on how to organize and distribute robots and objects in space.
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Approaches to the design problem in swarm robotics can be divided in two categories: manual design methods and automatic design methods.
  
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Examples of such behaviors are aggregation (Soysal and á¹¢ahin, 2005), pattern formation (Spears et al. 2004), chain formation (Nouyan et al. 2009), self-assembly (O'Grady et al., 2010), and object clustering/assembling (Werfel et al., 2011).
   
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Navigation behaviors focus on how to coordinate the movement of a robot swarm.
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=====Manual design methods=====
  
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Examples of such behaviors are collective exploration (Ducatelle et al., 2014), collective motion (Turgut et al., 2008), and collective transport (Baldassarre et al., 2006).
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Manual design methods are methods in which the designer, in a trial-and-error process, develops, tests and improves the reactive behaviors of the individual robot until the desired collective behavior is obtained.
 
   
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Collective decision-making focuses on how robots influence each other in making decisions.
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The most common behavioral architecture used is the ''probabilistic finite state machine'', where simple behaviors are connected through conditions based on the sensory inputs of the robots. Probabilistic finite state machines have been used to develop several collective behaviors, such as aggregation (Soysal and Sahin 2005), chain formation (Nouyan et al. 2009) and task allocation (Liu et al. 2007).
  
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In particular, collective decision-making can be used to achieve consensus on a single alternative (Garnier et al., 2005; Campo et al. 2011) or allocation to different alternatives (Pini et al., 2011).
   
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Human-swarm interaction focus on how a human operator can control a swarm and receive feedback information from it. For example, robots can distributedly recognize the gestures of a human operator (Giusti et al., 2012) or form groups based on visual and vocal inputs (Pourmehr et al., 2013).
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Another common approach is based on ''virtual physics'', in which each robot is considered a virtual particle that interacts with the environment and other robots through virtual forces.
  
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Virtual physics-based design approaches are particularly suited for spatially organizing collective behaviors, such as pattern formation (Spears er al. 2004) and collective motion (Ferrante et al., 2012).
  
   
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Other behaviors that do not fall in the previously mentioned categories are collective fault detection (Christensen et al. 2009) and group size regulation (Pinciroli et al, 2013).
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The main limit of manual design methods is that, being bottom-up, the quality of the developed robot swarm depends completely on the ingenuity and expertise of the human designers. A systematic and general top-down design approach is still missing, even though some problem-specific approaches have been recently proposed (Hamann and Worn 2008, Berman et al. 2011, Brambilla et al. 2012).
  
   
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==Open issues==
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=====Automatic design methods=====
  
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Automatic design methods are used to develop the behavior of a robot swarm, without the need for the manual development of the behaviors of the individual robots.
  
   
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Despite its potential to promote robustness, scalability and flexibility, swarm robotics has yet to be adopted for solving real-world problems. This is possibly due to the hardware limitations of the currently available robots, to the lack of effective ways to let a human operator interact with a robot swarm and to the lack of an engineering approach for swarm robotics. A further issue is the lack of any compelling demonstrators for outdoor swarm robotic systems (e.g., waste collection), and the lack of any business case or business model that demonstrates that the swarm robotics approach would be more cost effective than other approaches. In particular, the main open issues are the lack of a general methodology for designing robot swarms, the lack of well defined metrics and testbeds to assess their performance, and the lack of formal ways to verify and guarantee their properties.
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The main automatic design method is evolutionary robotics (Nolfi and Floreano, 2004). Typically, in evolutionary robotics, artificial evolution is used to configure the parameters of a neural networks that controls the behavior of the robots in the swarm (Trianni and Nolfi, 2011).
  
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== References ==
   
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G. Baldassarre, D. Parisi, and S. Nolfi. Distributed coordination of simulated robots based on self-organization. ''Artificial Life'', 12(3):289–311, 2006.
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Evolutionary robotics has been applied to develop behaviors for various collective behaviors, such as aggregation (Trianni et al., 2003) and collective transport (Gross and Dorigo, 2008).
  
   
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G. Beni. From swarm intelligence to swarm robotics. In ''Swarm Robotics'', LNCS 3342, pp. 1–9, 2005. Springer.
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One of the main limits of evolutionary robotics is that it is often difficult to define the experimental setup and fitness function that will produce an effective collective behavior. Moreover, since collective behaviors are obtained using simulations, often they are not able to overcome the reality gap, that is, despite having good performance in simulation, they do not perform well once instantiated on robots.
  
   
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S. Berman, Á. M. Halász, M. A. Hsieh, and V. Kumar. Optimized stochastic policies for task allocation in swarms of robots. ''IEEE Transactions on Robotics'', 25(4):927–937, 2009.
   
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S. Berman, V. Kumar, and R. Nagpal. Design of control policies for spatially inhomogeneous robot swarms with application to commercial pollination. ''IEEE International Conference on Robotics and Automation (ICRA)'', pp 378–385. 2011. IEEE press.
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===Modeling===
  
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The analysis and verification of the properties of a robot swarm are usually done by means of models.
  
   
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M. Brambilla, C. Pinciroli, M. Birattari, and M. Dorigo. Property-driven design for swarm robotics. In ''Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)'', pp 139–146, 2012. IFAAMAS press.
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A model of a robot swarm can be realized in two ways: by modeling the behaviors of the individual robots, what it is called, modeling the microscopic level, or by modeling the collective behavior of the swarm, what it is called, modeling the macroscopic level.
  
   
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M. Brambilla, E. Ferrante, M. Birattari, and M. Dorigo. Swarm robotics: a review from the swarm engineering perspective. ''Swarm Intelligence'', 7(1):1–41, 2013.
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Modeling the microscopic level is a difficult task, due to the very large number of robots involved. Microscopic models in which the elements composing a system are simulated with the
  
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use of a computer are traditionally called simulations. Examples of simulators used for swarm robotics can be found in Kramer and Scheutz (2007) or in Pinciroli et al. (2012)
  
   
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A. Campo, S. Garnier, O. Dédriche, M. Zekkri & M. Dorigo (2011). Self-organized discrimination of resources. ''PLOS One'', 6(5):e19888.
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On the contrary, macroscopic models avoid the complexity and scalability issues of having to model each individual robots, by considering only the collective behavior of the swarm.
 
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<!-- ====Macroscopic modeling====
 
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Many different approaches are possible to develop macroscopic models of robot swarms. -->
  
   
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A. L. Christensen, R. O’Grady, and M. Dorigo. From fireflies to fault-tolerant swarms of robots. ''IEEE Transactions on Evolutionary Computation'', 13(4):754–766, 2009.
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One of the most common macroscopic modeling approach is the use of ''rate'' or ''differential equations'' (Lerman et al. 2005).
 
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Rate equations describe the time evolution of the proportion of robots in a particular state, that is, performing a specific action or in a specific area of the environment, over the total number of robots.
 
   
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C. Dixon, A. F. T. Winfield, M. Fisher, and C. Zheng. Towards temporal verification of swarm robotic systems. ''Robotics and Autonomous Systems'', 60(11):1429–1441, 2012.
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Another common approach is the use of ''Markov chains'', which allow researchers to formally verify properties of a robot swarm (Dixon et al., 2011, Massink et al., 2013).
  
   
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M. Dorigo, D. Floreano, L. M. Gambardella, F. Mondada, S. Nolfi, T. Baaboura, M. Birattari, M. Bonani, M. Brambilla, A. Brutschy, D. Burnier, A. Campo, A. Christensen, A. Decugnière, G. A. Di Caro, F. Ducatelle, E. Ferrante, A. Förster, J. Guzzi, V. Longchamp, S. Magnenat, J. Martinez Gonzales, N. Mathews, M. Montes de Oca, R. O'Grady, C. Pinciroli, G. Pini, P. Rétornaz, J. Roberts, V. Sperati, T. Stirling, A. Stranieri, T. Stützle, V. Trianni, E. Tuci, A. E. Turgut, and F. Vaussard. Swarmanoid: A novel concept for the study of heterogeneous robotic swarms. ''IEEE Robotics & Automation Magazine'', 20(4):60–71, 2013.
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An alternative mesoscopic approach is the use of the ''Fokker-Plank'' and ''Langevin'' equations, which allows the researcher to model both the behavior of the individual robot, in the form of a deterministic component of the model, and the collective level of the swarm, in the form of a stochastic component of the model. Examples of such models can be found in Hamann and Worn (2008), Berman et al. (2011b) and Prorok et al. (2011).
  
   
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F. Ducatelle, G. A. Di Caro, C. Pinciroli, F. Mondada, and L. M. Gambardella. Cooperative navigation in robotic swarms. ''Swarm Intelligence'', 8(1), in press, 2014.
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===Collective behaviors===
  
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A large part of the research efforts in swarm robotics are directed towards the study of collective behaviors.
  
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Most collective behaviors can be categorized into three main groups: spatially organizing behaviors, navigation behaviors, collective decision-making behaviors.
  
   
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E. Ferrante, A. E. Turgut, C. Huepe, A. Stranieri, C. Pinciroli, and M. Dorigo. Self-organized flocking with a mobile robot swarm: a novel motion control method. ''Adaptive Behavior'', 20(6):460–477, 2012.
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<!-- =====Spatially-organizing behaviors===== -->
  
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Spatially-organizing behaviors focus on how to organize and distribute robots and objects in space.
  
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Examples of such behaviors are: aggregation (Trianni et al., 2003), pattern formation (Spears et al. 2004), chain formation (Nouyan et al. 2009), self-assembly (O'Grady et al., 2010) and object clustering and assembling (Werfel et al., 2001).
  
   
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S. Garnier, C. Jost, R. Jeanson, J. Gautrais, M. Asadpour, G. Caprari, and G. Theraulaz. Aggregation behaviour as a source of collective decision in a group of cockroach-like robots. In ''Advances in Artificial Life'', LNAI 3630, pp. 169–178, 2005. Springer.
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<!-- =====Navigation behaviors===== -->
  
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Navigation behaviors focus on how to coordinate the movements of a robot swarm.
  
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Examples of such behaviors are: collective exploration (Ducatelle et al., 2011a), collective motion (Ferrante et al., 2012) and collective transport (Gross and Dorigo, 2009).
  
   
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A. Giusti, J. Nagi, L. Gambardella, S. Bonardi, and G. A. Di Caro. Human-swarm interaction through distributed cooperative gesture recognition. 7th ACM/IEEE International Conference on Human-Robot Interaction (Video Session), 2012.
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<!-- =====Collective decision-making===== -->
  
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Collective decision-making focuses on how robots influence each other in making decisions over multiple choices.
 
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In particular collective decision-making can be used to achieve consensus, such as deciding where to aggregate (Garnier et al., 2005), or specialization, such as task allocation (Pini et al., 2009)
  
   
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R. Groß, and M. Dorigo. Evolution of solitary and group transport behaviors for autonomous robots capable of self-assembling. ''Adaptive Behavior'', 16(5):285–305, 2008.
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==Open issues==
  
   
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J. Halloy, G. Sempo, G. Caprari, C. Rivault, M. Asadpour, F. Tâche, I. Said, V. Durier, S. Canonge, J.M. Amé, C. Detrain, N. Correll, A. Martinoli, F. Mondada, R. Siegwart, J.-L. Deneubourg. Social integration of robots into groups of cockroaches to control self-organized choices. ''Science'', 318(5853):1155–1158, 2007.
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Swarm robotics, despite its potential to promote robustness, scalability and flexibility, have yet to be used to tackle a real-world application.
  
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There are many reasons for this lack of applications, such as, the limitations of the currently available robots or the lack of an engineering approach to swarm robotics.
 
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<!--''Swarm enginnering'' is the systematic application of scientific and technical knowledge to model and specify requirements, design and realize, verify and validate, and operate and maintain robot swarms.-->
  
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In particular, the lack of a general top-down methodology for the design of robot swarms, the lack of well defined metrics and testbed applications, and the lack of formal ways to verify and guarantee properties of a robot swarm are all open challenges that must be tackled before swarm robotics can be successfully applied to real-world applications.
  
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H. Hamann and H. Wörn. A framework of space-time continuous models for algorithm design in swarm robotics. ''Swarm Intelligence'', 2(2–4):209–239, 2008.
   
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S. Hauert, J.-C. Zufferey, and D. Floreano. Evolved swarming without positioning information: an application in aerial communication relay. ''Autonomous Robots'', 26(1):21–32, 2008.
   
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M. A. Hsieh, Á. Halász, S. Berman, and V. Kumar. Biologically inspired redistribution of a swarm of robots among multiple sites. ''Swarm Intelligence'', 2(2–4):121–141, 2008.
   
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S. Konur, C. Dixon, and M. Fisher. Analysing robot swarm behaviour via probabilistic model checking. ''Robotics and Autonomous Systems'', 60(2):199–213, 2012.
   
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J. Kramer and M. Scheutz. Development environments for autonomous mobile robots: a survey. ''Autonomous Robots'', 22(2):101–132, 2007.
   
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K. Lerman, A. Martinoli, and A. Galstyan. A review of probabilistic macroscopic models for swarm robotic systems. In ''Swarm Robotics'', LNCS 3342, pp 143–152, 2005. Springer.
   
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Y. Liu and K. M. Passino. Stable social foraging swarms in a noisy environment. ''IEEE Transactions on Automatic Control'', 49(1):30–44, 2004.
   
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W. Liu, A. F. T. Winfield. A macroscopic probabilistic model for collective foraging with adaptation. ''International Journal of Robotics Research'', 29(14):1743–1760, 2010.
   
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M. Massink, M. Brambilla, D. Latella, M. Dorigo, and M. Birattari. On the use of Bio-PEPA for modelling and analysing collective behaviours in swarm robotics. ''Swarm Intelligence'', 7(2-3):201–228, 2013.
−
== References ==
  
   
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A. Martinoli, A. J. Ijspeert, and F. Mondada. Understanding collective aggregation mechanisms: from probabilistic modelling to experiments with real robots. ''Robotics and Autonomous Systems'', 29(1):51–63, 1999.
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E. Bonabeau, M. Dorigo, and G. Theraulaz. ''Swarm Intelligence: From Natural to Artificial System''. Oxford University Press, New York, 1999.
  
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J.-L. Deneubourg, S. Aron, S. Goss, and J.-M. Pasteels. The self-organizing exploratory pattern of the Argentine ant. ''Journal of Insect Behavior'', 3:159–168, 1990.
  
   
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A. Martinoli, K. Easton, and W. Agassounon. Modeling swarm robotic systems: a case study in collaborative distributed manipulation. ''The International Journal of Robotics Research'', 23(4–5):415–436, 2004.
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G. Di Caro and M. Dorigo. AntNet: Distributed stigmergetic control for communications networks. ''Journal of Artificial Intelligence Research'', 9:317–365, 1998.
 
   
  +
S. Mitri, D. Floreano, and L. Keller. The evolution of information suppression in communicating robots with conflicting interests. ''PNAS'', 106(37):15786–15790, 2009.
−
M. Dorigo, V. Maniezzo, and A. Colorni. ''Positive feedback as a search strategy''. Technical Report 91-016, Dipartimento di Elettronica, Politecnico di Milano, Milan, Italy, 1991. Revised version published as: M. Dorigo, V. Maniezzo, and A. Colorni. Ant System: Optimization by a colony of cooperating agents. ''IEEE Transactions on Systems, Man, and Cybernetics – Part B'', 26(1):29–41, 1996.
 
   
  +
S. Nolfi and D. Floreano. ''Evolutionary robotics: intelligent robots and autonomous agents''. MIT Press, 2000.
−
M. Dorigo and E. Şahin (Eds.). Special Issue on Swarm Robotics. ''Autonomous Robots'', 17:111–246.
  
   
  +
S. Nouyan, R. Groß, M. Bonani, F. Mondada, and M. Dorigo. Teamwork in self-organized robot colonies. ''IEEE Transactions on Evolutionary Computation'', 13(4):695–711, 2009.
−
M. Dorigo and T. Stützle. ''Ant Colony Optimization''. MIT Press, Cambridge, MA, 2004.
 
   
  +
R. O’Grady, R. Groß, A. L. Christensen, and M. Dorigo. Self-assembly strategies in a group of autonomous mobile robots. ''Autonomous Robots'', 28(4):439–455, 2010.
−
J. Kennedy and R. C. Eberhart. Particle swarm optimization. ''Proceedings of IEEE International Conference on Neural Networks'', IEEE Press, Piscataway, NJ, pp. 1942-1948, 1995.
  
   
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C. Pinciroli, V. Trianni, R. O'Grady, G. Pini, A. Brutschy, M. Brambilla, N. Mathews, E. Ferrante, G. A. Di Caro, F. Ducatelle, M. Birattari, L. M. Gambardella and M. Dorigo. ARGoS: a modular, parallel, multi-engine simulator for multi-robot systems. ''Swarm Intelligence'', 6(4):271–295, 2012.
−
J. Kennedy, R. C. Eberhart, and Y. Shi. ''Swarm Intelligence''. Morgan Kaufmann, San Francisco, CA, 2001.
  
−
 
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E. Lumer and B. Faieta. Diversity and adaptation in populations of clustering ants. ''Proceedings of the Third International Conference on Simulation of Adaptive Behavior: From Animals to Animats 3'', MIT Press, Cambridge, CA, pp. 501-508, 1994.
  
   
  +
C. Pinciroli, R. O'Grady, A. L. Christensen, M. Birattari and M. Dorigo. Parallel formation of differently sized groups in a robotic swarm. ''SICE Journal of the Society of Instrument and Control Engineers'', 52(3):213–226, 2013.
−
R. Schoonderwoerd, O. Holland, J. Bruten and L. Rothkrantz. Ant-based Load Balancing in Telecommunications Networks. ''Adaptive Behavior'', 5(2):169–207, 1996.
  
   
  +
G. Pini, A. Brutschy, M. Frison, A. Roli, M. Dorigo, and M. Birattari. Task partitioning in swarms of robots: An adaptive method for strategy selection. ''Swarm Intelligence'', 5(3-4):283–304, 2011.
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== External Links ==
  
  +
  +
S. Pourmehr, V. M. Monajjemi, R. T. Vaughan, and G. Mori. "You two! Take off!": Creating, modifying and commanding groups of robots using face engagement and indirect speech in voice commands. In'' Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS)'', pp. 137–142, 2013. IEEE press.
  +
  +
A. Prorok, N. Correll, and A. Martinoli. Multi-level spatial modeling for stochastic distributed robotic systems. ''The International Journal of Robotics Research'', 30(5):574–589, 2011.
  +
  +
O. Soysal and E. Şahin. Probabilistic aggregation strategies in swarm robotic systems. In ''Proceedings of the IEEE Swarm Intelligence Symposium (SIS)'', pp. 325–332, 2005. IEEE press.
  +
  +
W. M. Spears, D. F. Spears, J. C. Hamann, and R. Heil. Distributed, physics-based control of swarms of vehicles. ''Autonomous Robots'', 17(2–3):137–162. 2004.
  +
  +
V. Trianni and S. Nolfi. Engineering the evolution of self-organizing behaviors in swarm robotics: A case study. ''Artificial Life'', 17(3):183–202, 2011.
  +
  +
A. E. Turgut, H. Çelikkanat, F. Gökçe, and E. Ṣahin. Self-organized flocking in mobile robot swarms. ''Swarm Intelligence'', 2(2–4):97–120, 2008.
  +
  +
J. Werfel, K. Petersen, and R. Nagpal. Distributed multi-robot algorithms for the TERMES 3D collective construction system. In ''Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)'', 2011. IEEE press.
  +
  +
== External links ==
   
 
* [http://www.springer.com/11721 Swarm Intelligence]: The main journal in the field.
  
* [http://www.springer.com/11721 Swarm Intelligence]: The main journal in the field.
−
* [http://iridia.ulb.ac.be/~ants ''ANTS - From Ant Colonies to Artificial Ants: A Series of International Workshops on Ant Algorithms'']: This series of conferences, held for the first time in 1998, is the oldest in the swarm intelligence field.
+
* [http://iridia.ulb.ac.be/~ants ''ANTS - International Conference on Swarm Intelligence'']: This series of conferences, held for the first time in 1998, is the oldest in the swarm intelligence field.
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* [http://www.computelligence.org/sis/2006/past.php ''IEEE Swarm Intelligence Symposia'']: Another more recent, started in 2003, series of conferences dedicated to swarm intelligence.
 +
* [http://www.computelligence.org/sis/2006/past.php ''IEEE Swarm Intelligence Symposium'']: Another series of conferences dedicated to swarm intelligence, started in 2003.
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* [http://www.swarm-bots.org/ Swarm-bots]: research project 2001-2005
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* [http://iridia.ulb.ac.be/argos/ ARGoS]: A multi-robot, multi-engine simulator for heterogeneous swarm robotics
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* [http://www.swarms.org/ Swarms]: research project 2003-2007
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* [http://www.i-swarm.org/ i-Swarm project]: research project 2005-2008
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* [http://www.swarmanoid.org/ Swarmanoid]: research project 2006-2010
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* [http://symbrion.org/tiki-index.php Symbrion]: research project 2008-2013
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* [http://www.e-swarm.org/ E-Swarm]: research project 2010-2015
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* [http://www.eecs.harvard.edu/ssr/projects/progSA/kilobot.html Kilobots]: a low-cost robot for swarm robotics
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[[Category:Artificial Life]]
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[[Category:Robotics]]
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[[Category:Computational intelligence]]
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==Review==
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Here are my review comments on the article Swarm Robotics.
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===Comment of Reviewer - Overall definition.===
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I think this is fine, except for the third sentence.
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"The design of robot swarms is guided by swarm intelligence principles, which promote the realization of systems that are fault tolerant, scalable and flexible. "
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This doesn't make sense, since swarm intelligence principles are in essence as observed/deduced from biology, whereas the 2nd part of the sentence is about possible engineering benefits. I recommend to split the sentence, i.e.
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"The design of robot swarms is guided by swarm intelligence principles. Such principles may lead to engineering benefits including artificial systems that are fault tolerant, scalable and flexible. "
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===Authors' answer===
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The phrase was restructured as suggested. Note that we kept "promote the realization" as we think that "may lead to" is too weak and does not convey the fact that the swarm intelligence principles are followed to obtain these engineering benefits; "may lead to" seems to convey more the idea that the engineering benefits are just an uncontrolled/unwanted effect.
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===Comment of Reviewer - Characteristics===
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Recommend removal of 'large and', so "A robot swarm is a highly redundant group of..." This avoids problems of how robots many is large, etc?
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Recommend replacing the work results with 'emerges', in the final sentence of this para.
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===Authors' answer===
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Reworded as suggested.
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===Comment of Reviewer - Desirable properties===
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Recommend replacing 'are deemed to' with 'may' in the first sentence.
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Recommend adding the word 'ideally' in 1st sentence of 3rd para, i.e. "...in their group size: ideally the introduction of..."
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===Authors' answer===
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Regarding the first comment, we kept "are deemed to", for the same reason explained in the answer to 1.
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We followed the second comment as suggested.
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===Comment of Reviewer - Potential applications===
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Recommend rewording 2nd sentence in 2nd para:
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Therefore, a solution that is fault tolerant is necessary, ...
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as
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Therefore, a fault-tolerant approach is required, ...
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===Authors' answer===
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Reworded as suggested.
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===Comment of Reviewer - Current Research Axes===
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Since you start this section by referring the reader to Brambilla et al, you *must* ensure that this paper is accessible to all and not behind a paywall, preferably with a link from here, to a pdf.
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===Authors' answer===
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Unfortunately, we cannot ensure that the paper is open access. Therefore, we rephrased the way the paper is introduced.
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===Comment of Reviewer - Analysis===
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Recommend remove 'very' from 2nd line of 2nd para, i.e. "...due to the large number of robots involved"
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In the section on Macroscopic models section you might consider adding a reference to
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Liu W and Winfield AFT, 'A Macroscopic Probabilistic Model for Collective Foraging with Adaptation', International Journal of Robotics Research, 29 (14), 1743-1760, 2010.
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Since this work is one of very few examples of successfully modelling an *adaptive* swarm.
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===Authors' answer===
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Done, thanks for the suggested literature.
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===Comment of Reviewer - Collective behaviours===
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Although you briefly mention human-swarm interaction, I *strongly* recommend that this merits a section of its own within Current Research Axes. I believe one of the important missing elements in swarm robotics in human-swarm interaction -since even though the indvidual robots may be autonomous the swarm, there still needs to be an effective means for commanding, monitoring and intervening (should things go wrong) with the swarm as a whole, and recommend you highlight the excellent work of both Vaughan et al, and Gambardella et al. I.e.
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Shokoofeh Pourmehr and Valiallah Mani Monajjemi and Richard T. Vaughan and Greg Mori. "You two! Take off!": Creating, modifying and commanding groups of robots using face engagement and indirect speech in voice commands Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS'13), Tokyo, Japan 2013
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A. Giusti, J. Nagi, L. Gambardella, S. Bonardi, G. A. Di Caro, Human-Swarm Interaction through Distributed Cooperative Gesture Recognition 7th ACM/IEEE International Conference on Human-Robot Interaction (Video Session) (HRI), Boston, MA, USA, March 5-8, 2012
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===Authors' answer===
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We enlarged the part dedicated to human-swarm interaction and added the suggested literature.
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===Comment of Reviewer - Open Issues===
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I think this section needs to be strengthened. For instance I think that effective Human Swarm Interaction (HSI) is an impediment to real world application. Others are the lack of any compelling demonstrators for outdoor swarm robotic systems (i.e. waste collection), and the lack of any business case or business model that demonstrates the swarm robotics approach would be more cost effective that any conventional robotics - or none robotics - approaches.
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===Authors' answer===
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We modified the section as suggested.
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===Comment of Reviewer - References===
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Please replace this:
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C. Dixon, A. Winfield, and M. Fisher. Towards temporal verification of emergent behaviours in swarm robotic systems. In Towards Autonomous Robotic Systems, LNCS 6856, pp. 336–347, 2011. Springer.
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With a more recent paper:
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Dixon C, Winfield A, Fisher M and Zheng C, Towards Temporal Verification of Swarm Robotic Systems, Robotics and Autonomous Systems, 60 (11), 1429-1441, Nov 2012.
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===Authors' answer===
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Done, thanks for the suggestion.
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===Comment of Reviewer - Additional General Comments===
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i. Someone familiar with conventional multi-robot systems would be puzzled to find no mention here. It would be good to constrast the swarm robotics approach with traditional multi-robot systems (which are sometimes mistakenly called swarm systems).
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ii. I'm surprised there is no mention of homogeneity and heterogeneity, i.e. that most existing lab swarm robotics systems are homogeneous, but that the approach does encompass heterogeneous systems. Of course Swarmanoids is a great example.
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iii. It may be interesting to include a section on the history of swarm robotics.
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iv. the article could be improved by some explanation of the rationale, i.e. the close and symbiotic relationship between the study of social insects/animals and swarm robotics - perhaps this could be included in the Scientific Implications section..?
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Note: I was assisted in this review by Dr W Liu.
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===Authors' answer===
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i. We added a mention of multi-robot systems in Section 1. We think that a formal comparison between swarm robotics and other multi-robot systems would be out of the scope of this article. Our intent is to describe swarm robotics through its characteristics, which are also the characteristics that distinguish swarm robotics from other robotics systems
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ii. We added a mention to this in Section 1.
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iii. and iv. We added a section on the origins of swarm robotics in which we also presented briefly the historical relationship between biology and swarm robotics.

Latest revision as of 15:31, 10 January 2014

Swarm robotics studies how to design groups of robots that operate without relying on any external infrastructure and on any form of centralized control. In a robot swarm, the collective behavior of the robots results from local interactions between the robots and between the robots and the environment in which they act. The design of robot swarms is guided by swarm intelligence principles. Such principles promote the realization of systems that are fault tolerant, scalable and flexible. Swarm robotics appears to be a promising approach when different activities must be performed concurrently, when high redundancy and the lack of a single point of failure are desired, and when it is technically unfeasible to setup an infrastructure to control the robots in a centralized way. Examples of tasks that could be profitably tackled using swarm robotics are demining, search and rescue, planetary or underwater exploration, and surveillance.

Origins

Swarm robotics has its origins in swarm intelligence and, in fact, could be defined as "embodied swarm intelligence". Initially, the main focus of swarm robotics research was to study and validate biological research (Beni, 2005). Collaboration between roboticists and biologists was vital to make swarm robotics a relevant research field. However, in recent years the focus of swarm robotics has been shifting: from a bio-inspired field of robotics, swarm robotics is becoming more and more an engineering field whose focus is on the development of tools and methods to solve real problems (Brambilla et al., 2013).

Characteristics of swarm robotics

A robot swarm is a multi-robot system characterized by high redundancy and self-organization. Robots’ sensing and communication capabilities are local and robots do not have access to global information. The collective behavior of the robot swarm emerges from the interactions of each individual robot with its neighboring peers and with the environment. Typically, a robot swarm is composed of homogeneous robots, but examples of heterogeneous robot swarms exist (Dorigo et al., 2013).

Desirable properties of swarm robotics systems

The aforementioned characteristics of swarm robotics are deemed to promote the realization of systems that are fault tolerant, scalable and flexible.

Swarm robotics promotes the development of systems that are able to cope well with the failure of one or more of their individuals: the loss of individuals does not imply the failure of the whole swarm. Fault tolerance is enabled by the high redundancy of the swarm: the swarm does not rely on any centralized control entity, leaders, or any individual robot playing a predefined role.

Swarm robotics promotes also the development of systems that are able to cope well with changes in their group size: ideally, the introduction or removal of individuals does not cause a drastic change in the performance of the swarm. Scalability is enabled by local sensing and communication: provided that the introduction and removal of robots does not dramatically modify the density of the swarm, each individual robot will keep interacting with approximately the same number of peers, those that are in its sensing and communication range.

Finally, swarm robotics promotes the development of systems that are able to deal with a broad spectrum of environments and operating conditions. Flexibility is enabled by the distributed and self-organized nature of a robot swarm: in a swarm, robots dynamically allocate themselves to different tasks to match the requirements of the specific environment and operating conditions; moreover, robots operate on the basis of local sensing and communication without the need of pre-existing infrastructures and of global information on the environment.

Potential applications of swarm robotics

The properties of swarm robotics systems make them appealing in several potential fields of application.

The use of robots for tackling dangerous tasks is clearly appealing as it eliminates or reduces risks for humans. The dangerous nature of these tasks implies a high risk of losing robots. Therefore, a fault-tolerant approach is required, making dangerous tasks an ideal field of application for robot swarms. Example of dangerous tasks that could be tackled using robot swarms are demining, search and rescue, and toxics cleanup.

Potential applications for robot swarms are those in which it is difficult or even impossible to estimate in advance the resources needed to accomplish the task. For instance, allocating resources to manage an oil leak can be very hard because it is often difficult to estimate the oil output and to foresee its development. In these cases, a solution is needed that is scalable and flexible. A robot swarm could be an appealing solution: robots can be added or removed in time to provide the appropriate amount of resources and meet the requirements of the specific task. Example of tasks that might require an a priori unknown amount of resources are search and rescue, transportation of large objects, tracking, and cleaning.

Another potential field of application for swarm robotics are tasks that have to be accomplished in large or unstructured environments, in which there is no available infrastructure that can be used to control the robots - e.g., no available communication network or global localization system. Robot swarms could be employed for such applications because they are able to act autonomously without the need of any infrastructure or any form of external coordination. Examples of tasks in unstructured and large environments are planetary or underwater exploration, surveillance, demining, cleaning, and search and rescue.

Some environments might change rapidly over time. For instance, in a post earthquake situation, buildings might collapse changing the layout of the environment and creating new hazards. In these cases, it is necessary to adopt solutions that are flexible and can react fast to events. Swarm robotics could be used to develop flexible systems that can rapidly adapt to new operating conditions. Example of tasks in environments that change over time are patrolling, disaster recovery, search and rescue, cleaning.

Scientific implications of swarm robotics

Beside being relevant to engineering applications, swarm robotics is also a valuable scientific tool. Indeed, several models of natural swarm intelligence systems have been refined and validated using robot swarms. For example, Garnier et al. (2005) validated the model of a collective decision-making behavior in cockroaches using robot swarms.

Swarm robotics has also been used to investigate, via controlled experiments, the conditions under which some complex social behaviors might result out of an evolutionary process. For example, robot swarms have been used to study the evolution of communication (Mitri et al., 2009) and collective decision making (Halloy et al., 2007).

Current research axes

In this section, we present the main research axes of current research in swarm robotics. We follow the taxonomy presented in Brambilla et al. (2013).

Design

The design of a robot swarm is a difficult endeavor: requirements are usually expressed at the collective level, but, eventually, the designer needs to define what the individual robots should do so that their interactions result in the desired collective behavior. Approaches to the design problem in swarm robotics can be divided into two categories: manual design and automatic design.

In manual design, the designer follows a trial-and-error process in which the behaviors of the individual robot are developed, tested and improved until the desired collective behavior is obtained. The software architecture that is most commonly adopted in swarm robotics is the probabilistic finite state machine. Probabilistic finite state machines have been used to obtain several collective behaviors, including aggregation (Soysal and Sahin, 2005), chain formation (Nouyan et al., 2009), and task allocation (Liu and Winfield, 2010). Another common approach is based on virtual physics. In this approach, robots and environment interact through virtual forces. This approach is particularly suited for spatially organizing collective behaviors, such as pattern formation (Spears et al. 2004) and collective motion (Ferrante et al., 2012). Currently, the main limit of manual design is that it completely relies on the ingenuity and expertise of the human designer: designing a robot swarm is more of an art than a science. A systematic and general way to design robot swarms is still missing, even though a few preliminary proposals have been made (Hamann and Worn, 2008; Berman et al., 2011; Brambilla et al., 2012).

In swarm robotics, automatic design has been mostly performed using the evolutionary robotics approach (Nolfi and Floreano, 2004). Typically, individual robots are controlled by a neural network whose parameters are obtained via artificial evolution (Trianni and Nolfi, 2011). Evolutionary robotics has been used to develop several collective behaviors including collective transport (Groß and Dorigo, 2008) and development of communication networks (Huaert et al., 2008). One of the main limits of evolutionary robotics is that it is often difficult to define all the elements of the evolutionary process that will produce control software that allows the robot swarm to effectively accomplish the given task.

Analysis

The analysis of a robot swarm usually relies on models. A model of a robot swarm can be realized at two levels: the microscopic level, that is modeling the behaviors of the individual robots; or the macroscopic level, that is modeling the collective behavior of the swarm.

Modeling the microscopic level allows one to represent in a detailed way the actions of all the individual robots composing the swarm. Unfortunately, microscopic modeling is problematic due to the large number of robots involved. Often, microscopic modeling relies on computer-based simulations (Kramer and Scheutz, 2007; Pinciroli et al., 2012).

Macroscopic models avoid the complexity and scalability issues of having to model each individual robot by considering only the collective behavior of the swarm. One of the most common macroscopic modeling approaches is the use of rate or differential equations (Martinoli et al., 2004; Lerman et al., 2005). Rate equations describe the time evolution of the ratio of robots in a particular state, that is, of robots that are performing a specific action or are in a specific area of the environment. Rate equations have been used to model many collective behaviors, including object clustering (Martinoli et al., 1999) and adaptive foraging (Liu and Winfield, 2010). Another common approach is the use of Markov chains, which allow researchers to formally verify properties of a robot swarm (Dixon et al., 2012; Konur et al., 2012; Massink et al., 2013). Control theory has also been used to analyze whether a robot swarm eventually converges to a desired macroscopic state (Liu and Passino, 2004; Hsieh et al., 2008).

A hybrid way of modeling robot swarms is based on Fokker-Plank and Langevin equations (Hamann and Worn, 2008; Berman et al., 2009; Prorok et al., 2011). Using these equations, one can model both the behavior of the individual robot, in the form of a deterministic component of the model; and the collective level of the swarm, in the form of a stochastic component of the model.

Collective behaviors

A large part of the research effort in swarm robotics is directed towards the study of collective behaviors. Collective behaviors can be categorized into five main groups: spatially organizing behaviors, navigation behaviors, decision-making behaviors, human interaction behaviors, and other behaviors.

Spatially-organizing behaviors focus on how to organize and distribute robots and objects in space. Examples of such behaviors are aggregation (Soysal and á¹¢ahin, 2005), pattern formation (Spears et al. 2004), chain formation (Nouyan et al. 2009), self-assembly (O'Grady et al., 2010), and object clustering/assembling (Werfel et al., 2011).

Navigation behaviors focus on how to coordinate the movement of a robot swarm. Examples of such behaviors are collective exploration (Ducatelle et al., 2014), collective motion (Turgut et al., 2008), and collective transport (Baldassarre et al., 2006).

Collective decision-making focuses on how robots influence each other in making decisions. In particular, collective decision-making can be used to achieve consensus on a single alternative (Garnier et al., 2005; Campo et al. 2011) or allocation to different alternatives (Pini et al., 2011).

Human-swarm interaction focus on how a human operator can control a swarm and receive feedback information from it. For example, robots can distributedly recognize the gestures of a human operator (Giusti et al., 2012) or form groups based on visual and vocal inputs (Pourmehr et al., 2013).

Other behaviors that do not fall in the previously mentioned categories are collective fault detection (Christensen et al. 2009) and group size regulation (Pinciroli et al, 2013).

Open issues

Despite its potential to promote robustness, scalability and flexibility, swarm robotics has yet to be adopted for solving real-world problems. This is possibly due to the hardware limitations of the currently available robots, to the lack of effective ways to let a human operator interact with a robot swarm and to the lack of an engineering approach for swarm robotics. A further issue is the lack of any compelling demonstrators for outdoor swarm robotic systems (e.g., waste collection), and the lack of any business case or business model that demonstrates that the swarm robotics approach would be more cost effective than other approaches. In particular, the main open issues are the lack of a general methodology for designing robot swarms, the lack of well defined metrics and testbeds to assess their performance, and the lack of formal ways to verify and guarantee their properties. __AUTOLINKER{0}

References

G. Baldassarre, D. Parisi, and S. Nolfi. Distributed coordination of simulated robots based on self-organization. Artificial Life, 12(3):289–311, 2006.

G. Beni. From swarm intelligence to swarm robotics. In Swarm Robotics, LNCS 3342, pp. 1–9, 2005. Springer.

S. Berman, Á. M. Halász, M. A. Hsieh, and V. Kumar. Optimized stochastic policies for task allocation in swarms of robots. IEEE Transactions on Robotics, 25(4):927–937, 2009.

S. Berman, V. Kumar, and R. Nagpal. Design of control policies for spatially inhomogeneous robot swarms with application to commercial pollination. IEEE International Conference on Robotics and Automation (ICRA), pp 378–385. 2011. IEEE press.

M. Brambilla, C. Pinciroli, M. Birattari, and M. Dorigo. Property-driven design for swarm robotics. In Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp 139–146, 2012. IFAAMAS press.

M. Brambilla, E. Ferrante, M. Birattari, and M. Dorigo. Swarm robotics: a review from the swarm engineering perspective. Swarm Intelligence, 7(1):1–41, 2013.

A. Campo, S. Garnier, O. Dédriche, M. Zekkri & M. Dorigo (2011). Self-organized discrimination of resources. PLOS One, 6(5):e19888.

A. L. Christensen, R. O’Grady, and M. Dorigo. From fireflies to fault-tolerant swarms of robots. IEEE Transactions on Evolutionary Computation, 13(4):754–766, 2009.

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Review

Here are my review comments on the article Swarm Robotics.

Comment of Reviewer - Overall definition.

I think this is fine, except for the third sentence.

"The design of robot swarms is guided by swarm intelligence principles, which promote the realization of systems that are fault tolerant, scalable and flexible. "

This doesn't make sense, since swarm intelligence principles are in essence as observed/deduced from biology, whereas the 2nd part of the sentence is about possible engineering benefits. I recommend to split the sentence, i.e.

"The design of robot swarms is guided by swarm intelligence principles. Such principles may lead to engineering benefits including artificial systems that are fault tolerant, scalable and flexible. "

Authors' answer

The phrase was restructured as suggested. Note that we kept "promote the realization" as we think that "may lead to" is too weak and does not convey the fact that the swarm intelligence principles are followed to obtain these engineering benefits; "may lead to" seems to convey more the idea that the engineering benefits are just an uncontrolled/unwanted effect.

Comment of Reviewer - Characteristics

Recommend removal of 'large and', so "A robot swarm is a highly redundant group of..." This avoids problems of how robots many is large, etc?

Recommend replacing the work results with 'emerges', in the final sentence of this para.

Authors' answer

Reworded as suggested.

Comment of Reviewer - Desirable properties

Recommend replacing 'are deemed to' with 'may' in the first sentence.

Recommend adding the word 'ideally' in 1st sentence of 3rd para, i.e. "...in their group size: ideally the introduction of..."

Authors' answer

Regarding the first comment, we kept "are deemed to", for the same reason explained in the answer to 1.

We followed the second comment as suggested.

Comment of Reviewer - Potential applications

Recommend rewording 2nd sentence in 2nd para:

Therefore, a solution that is fault tolerant is necessary, ... as Therefore, a fault-tolerant approach is required, ...

Authors' answer

Reworded as suggested.

Comment of Reviewer - Current Research Axes

Since you start this section by referring the reader to Brambilla et al, you *must* ensure that this paper is accessible to all and not behind a paywall, preferably with a link from here, to a pdf.

Authors' answer

Unfortunately, we cannot ensure that the paper is open access. Therefore, we rephrased the way the paper is introduced.

Comment of Reviewer - Analysis

Recommend remove 'very' from 2nd line of 2nd para, i.e. "...due to the large number of robots involved"

In the section on Macroscopic models section you might consider adding a reference to Liu W and Winfield AFT, 'A Macroscopic Probabilistic Model for Collective Foraging with Adaptation', International Journal of Robotics Research, 29 (14), 1743-1760, 2010. Since this work is one of very few examples of successfully modelling an *adaptive* swarm.

Authors' answer

Done, thanks for the suggested literature.

Comment of Reviewer - Collective behaviours

Although you briefly mention human-swarm interaction, I *strongly* recommend that this merits a section of its own within Current Research Axes. I believe one of the important missing elements in swarm robotics in human-swarm interaction -since even though the indvidual robots may be autonomous the swarm, there still needs to be an effective means for commanding, monitoring and intervening (should things go wrong) with the swarm as a whole, and recommend you highlight the excellent work of both Vaughan et al, and Gambardella et al. I.e.

Shokoofeh Pourmehr and Valiallah Mani Monajjemi and Richard T. Vaughan and Greg Mori. "You two! Take off!": Creating, modifying and commanding groups of robots using face engagement and indirect speech in voice commands Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS'13), Tokyo, Japan 2013

A. Giusti, J. Nagi, L. Gambardella, S. Bonardi, G. A. Di Caro, Human-Swarm Interaction through Distributed Cooperative Gesture Recognition 7th ACM/IEEE International Conference on Human-Robot Interaction (Video Session) (HRI), Boston, MA, USA, March 5-8, 2012

Authors' answer

We enlarged the part dedicated to human-swarm interaction and added the suggested literature.

Comment of Reviewer - Open Issues

I think this section needs to be strengthened. For instance I think that effective Human Swarm Interaction (HSI) is an impediment to real world application. Others are the lack of any compelling demonstrators for outdoor swarm robotic systems (i.e. waste collection), and the lack of any business case or business model that demonstrates the swarm robotics approach would be more cost effective that any conventional robotics - or none robotics - approaches.

Authors' answer

We modified the section as suggested.

Comment of Reviewer - References

Please replace this: C. Dixon, A. Winfield, and M. Fisher. Towards temporal verification of emergent behaviours in swarm robotic systems. In Towards Autonomous Robotic Systems, LNCS 6856, pp. 336–347, 2011. Springer. With a more recent paper: Dixon C, Winfield A, Fisher M and Zheng C, Towards Temporal Verification of Swarm Robotic Systems, Robotics and Autonomous Systems, 60 (11), 1429-1441, Nov 2012.

Authors' answer

Done, thanks for the suggestion.

Comment of Reviewer - Additional General Comments

i. Someone familiar with conventional multi-robot systems would be puzzled to find no mention here. It would be good to constrast the swarm robotics approach with traditional multi-robot systems (which are sometimes mistakenly called swarm systems).

ii. I'm surprised there is no mention of homogeneity and heterogeneity, i.e. that most existing lab swarm robotics systems are homogeneous, but that the approach does encompass heterogeneous systems. Of course Swarmanoids is a great example.

iii. It may be interesting to include a section on the history of swarm robotics.

iv. the article could be improved by some explanation of the rationale, i.e. the close and symbiotic relationship between the study of social insects/animals and swarm robotics - perhaps this could be included in the Scientific Implications section..?

Note: I was assisted in this review by Dr W Liu.

Authors' answer

i. We added a mention of multi-robot systems in Section 1. We think that a formal comparison between swarm robotics and other multi-robot systems would be out of the scope of this article. Our intent is to describe swarm robotics through its characteristics, which are also the characteristics that distinguish swarm robotics from other robotics systems

ii. We added a mention to this in Section 1.

iii. and iv. We added a section on the origins of swarm robotics in which we also presented briefly the historical relationship between biology and swarm robotics.

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