Evolutionary Robotics Approach to Autonomous Task Allocation for a Multi-Robot System


The field of multi-robot systems is sometimes called swarm robotics when the systems consist of many simple autonomous robots. However, each robot is usually assumed to have no learning mechanism for adapting to an embedded changing environment. Therefore, collective behavior is expected to emerge in the system only through interactions among the robots. This implies that they cannot be coordinated as a group. In this study, an evolutionary robotics approach is applied empirically to a multi-robot system to realize autonomous task allocation behavior as a kind of intelligent swarm robotics. Although artificial evolution has proven to be a promising approach to coordinate the controller of an autonomous robot, its effectiveness in developing beneficial collective behavior in a multi-robot system has not been verified. Several computer simulations are conducted in order to examine how artificial evolution contributes to conduct autonomous task allocation in a multi-robot system.

  • Abstract
  • Introduction
  • MBEANN for Evolving Artificial Neural Networks
  • Cooperative Package Pushing Problem
  • Settings of Computer Simulations
  • Results of Computer Simulations
  • Conclusions
  • References

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