IRIDIA - Supplementary Information (ISSN: 2684-2041)

Supplementary material for the paper:

Concurrent Design of Control Software and Configuration of the Hardware for a Robot Swarm

Muhammad Salman, Antoine Ligot, Mauro Birattari,
IRIDIA, Université Libre de Bruxelles, Belgium


Abstract

Designing a robot swarm is challenging due to its self-organized and distributed nature: complex relations exist between the behavior of the individual robots and the collective behavior that results from their interactions. In this paper, we study the concurrent automatic design of control software and the automatic configuration of the hardware of robot swarms. We introduce Waffle, a new instance of the AutoMoDe family of automatic design methods that produces control software in the form of a probabilistic finite state machine, configures the robot hardware, and selects the number of robots in the swarm. We test Waffle under economic constraints on the total monetary budget available and on the battery capacity of each individual robot comprised in the swarm. Experimental results obtained via realistic computer-based simulation on three collective missions indicate that different mission require different hardware and software configuration, and that Waffle is able to produce effective and meaningful solutions under all the experimental conditions considered.


Extended Range-&-Bearings

We consider some hypothetical hardware modules that enable a robot to detect and locate its neighboring peers. These hypothetical modules are based on infrared transceivers and are variants of an existing hardware module for the e-puck platform known as the range-&-bearing. We define the set of these hypothetical modules so that some of them are more-capable and some are less-capable than the existing one in terms of perception range and detection abilities. We assume that the more capable hardware modules are more expensive and consume more power. These hypothetical modules are realistic and possibly implementable. The technical specification of each range-&-bearing is available to download here:

We also present here two scenarios to determine the aggregate position vector V.


Missions

The instances of control software, hardware configuration, and the prominent behaviors of all experiments are available for download here:

Anytime Selection

The swarm must select one of the two black areas and aggregate there. The objective function is computed at every control step, and is maximum when all robots spend maximum time on one zone.

NC

M80

M60

P20

P15

M80 P20

M80 P15

M60 P20

M60 P15

End-Time-Aggregation

The swarm must select one of the two black zones and aggregate there. The objective function is computed at the end of the experimental run, and is maximized when all robots are on one zone.

NC

M80

M60

P20

P15

M80 P20

M80 P15

M60 P20

M60 P15

Foraging

The arena contains two source areas (black circles) and a nest (white area). A light is placed behind the nest to help the robots to navigate. In this idealized version of foraging, a robot is deemed to retrieve an object when it enters a source and then the nest. The goal of the swarm is to retrieve as many objects as possible.

NC

M80

M60

P20

P15

M80 P20

M80 P15

M60 P20

M60 P15