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). The design of swarm robotics systems is guided by swarm intelligence principles, which promote the realization of robot swarms that are robust, scalable and flexible.

Characteristics of swarm robotics

The main characteristics of a swarm robotics system are:

  • the robotics system consists of a large group of autonomous robots;
  • robots cooperate to tackle a given task;
  • robots are relatively homogeneous both hardware- and software-wise (i.e., they are either all identical or they belong to a few typologies);
  • robots are situated in the environment and can act to modify it;
  • robots’ sensing and communication capabilities are local;
  • robots do not have access to centralized control and/or to global knowledge;
  • 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

The characteristics of swarm robotics are deemed to promote the realization of systems which are robust, scalable and flexible.

By robustness, we mean that robot swarms are 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. Robustness is promoted by redundancy, that is, by having many homogeneous robots, and by the lack of a leader or centralized control.

By scalability, we mean that robot swarms are 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.

By flexibility, we mean that robot swarms are able to cope with a broad spectrum of different environment and tasks. Flexibility is promoted by redundancy, simplicity of the behaviors, the lack of global knowledge and mechanisms such as task allocation.

Potential applications of swarm robotics systems

The desired properties of swarm robotics systems make them suitable for several fields of application.

Dangerous applications

Dangerous applications expose humans to the risk of injuries or casualties. An example of such applications is demining, where human operators have to search and defuse manually land mines.

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.

Example of dangerous applications that can be tackled using robot swarms are: demining, search and rescue, toxic cleaning, military applications.

Applications with unknown or varying size

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.

Applications with unknown or varying size must be tackled with solutions whose scale can easily adapt such as robot swarms: robots can be added or removed to fit different requirements of the applications.

Example of applications with unknown or varying size: search and rescue, transportation of large objects, tracking, cleaning, military applications.

Applications in large and unstructured environments

Some applications take place over large extensions of space. 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.

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.

Examples of applications in unstructured and large environments are planetary or underwater exploration, military applications, surveillance, demining, cleaning, search and rescue.

Applications in dynamic environments

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. 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.

Example of applications in dynamic environments are surveillance, disaster recovery, search and rescue, cleaning, military applications.

Scientific implications of swarm robotics

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. TODO: add a couple of examples.

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 (Francesca et al., 2012).

Current research axis

Current research in swarm robotics is mainly focused on three axis: design, modeling and collective behaviors.

Design

Modeling

Collective behaviors


  • Definition
  • Characteristics
  • Desired properties
  • Potential applications
  • Scientific implications
  • Current research axis
    • Design
    • Modeling
    • Collective behaviors
  • Open issues
















OLD

Swarm robotics studies how a large number of embodied agents can be designed and controlled so that a desired collective behavior results from local interactions among the agents and between the agents and the environment in which they act (Dorigo and Sahin 2004; Sahin 2005). The design of swarm robotics systems is guided by swarm intelligence principles. The main goal pursued is the development of physical swarms exhibiting collective behaviors that are robust, scalable and flexible.


What Characterizes Swarm Robotics

Research in swarm robotics focuses on robotics systems characterized by the following properties:

  • the robotics system consists of a large group of autonomous robots, called a swarm;
  • the robots in the swarm are relatively homogeneous (i.e., they are either all identical or they belong to a few typologies);
  • the interactions among the robots in the swarm are based on simple behavioral rules that exploit only local information that the robots exchange directly or via the environment (stigmergy);
  • the overall behavior of the system results from the interactions of the robots with each other and with their environment, that is, the swarm behavior self-organizes;
  • the robots in the swarm are relatively incapable with respect to the tasks that they are asked to perform;
  • physical and/or logical cooperation of the robots allows the swarm to overcome the limitations of the single robots.

Desirable properties of swarm robotics systems

Some of the most desirable properties that are searched for in a swarm robotics system are scalability, robustness and fault tolerance. Important design choices that help in achieving these system level properties are the above mentioned use of local sensing and communication and of distributed control.

Fault tolerance

Because in swarm robotics there are many instances of each robot type, and because the control is typically decentralized, the failure of one or more individuals does not necessarily determine the failure of the system as a whole. In general, in a properly designed swarm robotics system failures are associated to either no change in performance, when the number of available robots is higher than the number of robots necessary for performing the considered task, or to a graceful degradation of the system performance. Additionally, the relative simplicity of the robots, in their sensing/control/actuation aspects, make fault less probable than in more complex robots.

Flexibility

By flexibility we mean that the swarm robotic system should have the ability to tackle different task by only changing the coordination strategy. Ants provide impressive examples of flexibility. On one hand, during foraging, ants move independently of each other and coordinate their search through pheromones laid on the ground. On the other hand, prey retrieval task requires the ants to generate a force much larger than that of a single individual to drag a prey to the nest. When a large prey is discovered, each ant grip the prey with its mandible and pull it in different directions. The seemingly random pulls of ants are observed to be coordinated through the force integrated over the prey. Swarm robotic systems should also have the flexibility to offer solutions to the tasks at hand by utilizing different coordination strategies in response to the changes in the environment.

Scalability

By scalability, we mean that the swarm robotic system should be able to operate under a wide range of group sizes. That is, the coordination mechanisms that ensure the operation of the swarm should be relatively undisturbed by changes in the group sizes. This requirement implicitly poses the local interaction and communication constraint for coordination. Stigmergy, that is the use of environment as a medium of interaction, naturally leads towards scalable coordination methods.

Domains of application

The application of swarm robotics methods to real-world problems will depend on the mass production technologies of robots. Advances in mechatronics technology have already started shrinking the size and costs of autonomous robots\cite{iSwarm}. MEMS (Micro-Electro-Mechanical System)technology has started to make impressive progress towards the integration of sensing, actuation and computation on silicon substrate paving way to the mass production of micro and nano robots.

We believe that swarm robotics could be used for many real-world tasks. However, instead of giving specific examples, we will discuss what types of tasks swarm robotics approach would be most appropriate.

Tasks that cover a region or space

Swarm robotic systems are completely distributed and would be well suited for tasks that are concerned with the monitoring of a region or space. Environmental monitoring, or even cleaning would constitute a good domain of application.

Tasks that are too dangerous

In swarm robotic systems, individuals are dispensible and the performance of the system degrades gracefully even when it loses its members. Hence tasks such as de-mining a mine field or the surveillance of a battlefield from sky would be better addressed by swarm robotic systems.

Tasks that scale-up or scale-down in time

Swarm robotic systems rely on coordination mechanisms that are relatively independent of the swarm size. Hence, it allows the user to scale-up or scale-down the size the swarm based on the size of the task.

Tasks that require redundancy

Swarm robotic systems are inherently redundant, since the loss of an individual can be automatically compensated by others. Such a redundancy would make swarm robotic systems less prone to catastrophic failures.

Research axes

The research in swarm robotics can be roughly categorized into four broad categories:

Design

Most of the studies on swarm robotic systems are concerned with the development of coordination methods to solve certain tasks. Some of these studies are concerned with principled/automatized approaches to develop such behaviors, such as evolutionary approaches, whereas others are concerned with the manual development of behaviors to address certain tasks.

Modelling and Analysis

Swarm robotics systems, by their very nature, are difficult to control rely on self-organization. Hence, whereever possible modelling and analysis of swarm robotic systems can provide guarantees regarding the system-level properties.

Tools

As with any other field, swarm robotics depends highly on advances in both experimental hardware and simulation tools that aid the development of swarm robotic systems.

Applications

Finally, there are a few studies that deal with the actual or proposed applications of swarm robotics to real-world problems.

References

E. Bonabeau, M. Dorigo, and G. Theraulaz. Swarm Intelligence: From Natural to Artificial System. Oxford University Press, New York, 1999.

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.

G. Di Caro and M. Dorigo. AntNet: Distributed stigmergetic control for communications networks. Journal of Artificial Intelligence Research, 9:317–365, 1998.

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.

M. Dorigo and E. Şahin (Eds.). Special Issue on Swarm Robotics. Autonomous Robots, 17:111–246.

M. Dorigo and T. Stützle. Ant Colony Optimization. MIT Press, Cambridge, MA, 2004.

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.

J. Kennedy, R. C. Eberhart, and Y. Shi. Swarm Intelligence. Morgan Kaufmann, San Francisco, CA, 2001.

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.

R. Schoonderwoerd, O. Holland, J. Bruten and L. Rothkrantz. Ant-based Load Balancing in Telecommunications Networks. Adaptive Behavior, 5(2):169–207, 1996.

External Links

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