2004BaldassarreParisiNolfi-CoordinationInCooperatingSimulatedRobots

2004BaldassarreParisiNolfi-CoordinationInCooperatingSimulatedRobots

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Coordination and Behaviour Integration in Cooperating Simulated Robots Gianluca Baldassarre Domenico Parisi Stefano Nolfi Institute of Cognitive Sciences and Technologies, National Research Council, Viale Marx 15, 00137 Rome, Italy baldassarre@ip.rm.cnr.it parisi@ip.rm.cnr.it nolfi@ip.rm.cnr.it Abstract This paper shows how a group of evolved physically- linked robots are able to display a variety of highly coordinated basic behaviours (coordinated motion, coordinated obstacle avoidance, coordinated light approaching) and to integrate such behaviours into a single coherent behaviour. In this way the group is capable of searching and approaching a light target in an environment scattered with obstacles, furrows, and holes and of dynamically changing its shape in order to pass through narrow passages. The paper analyses in detail the emerged basic behaviours and shows how the coordination of the group relies upon robust self-organising principles based on a traction sensor that allows the single robots to perceive the “aver- age” direction of motion of the rest of the group. 1. Introduction Consider the scenario shown in Figure 1 and Figure 8. A group of robots is placed in a maze with obstacles, furrows and holes. The mission of the robots is to explore the maze and search for a light target. Some robots form a swarm-bot (i.e. a group of physically linked robots with a particular topological structure) in order to pass over furrows and holes in which they would fall by moving alone. The chal- lenges proposed by this scenario are several: how can the assembled robots move in the same direction? How can they avoid obstacles? How can they approach the light once it is in sight? To face these challenges, the robots should be able to display coordinated behaviours (e.g. coordinated move- ment, coordinated obstacle avoidance, and coordinated light approaching) and to integrate these behavioural capabilities into a single coherent behaviour. This paper will show how these problems can be solved (a) by suitably designing the hardware of the robots and by providing them with a traction sensor that allows them to feel the direction and the intensity of the traction caused by the group, and (b) by developing the controllers of the robots through an evolutionary method that allows evolving robots to exploit behavioural properties that emerge from the fine-grained interactions between the robots and between the robots and the environment. Figure 1: A group of eight assembled robots searches a light target (white sphere at the top left of the picture) in a maze with obsta- cles, furrows, and holes. Notice how single robots, as the three robots right behind the group, get stuck in holes and furrows. Collective robotics is a growing research area within the broader field of robotics (Bonabeau et al., 1999; Grabowski et al., 2003). Part of this research area focuses on distributed coordination of groups of cooperating robots that have to accomplish common tasks (Baldassarre et al., 2002; Ijspeert et al., 2001). Distributed coordination implies that the char-
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2004BaldassarreParisiNolfi-CoordinationInCooperatingSimulatedRobots

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