Adaptive Control in Swarm Robotics

TL;DR

This paper surveys adaptive control methods in swarm robotics, highlighting their role in enhancing robustness and efficiency despite limited individual capabilities and decentralized operation.

Contribution

It provides a comprehensive overview of adaptive control techniques in swarm robotics, including detailed discussion of a specific division of labor method and potential improvements.

Findings

Adaptive control improves swarm robustness to robot failures.

Decentralized systems achieve complex tasks without centralized coordination.

Division of labor techniques enhance task efficiency.

Abstract

Swarm robotic systems are mainly inspired by swarms of socials insects and the collective emergent behavior that arises from their cooperation at the lower lever. Despite the limited sensory ability, computational power, and communication means of each swarm member, the swarm as a group manages to achieve difficult tasks such as searching for food in terrains with obstacles that individual robots cannot achieve in isolation of the other group members. Moreover, such tasks are usually achieved without having information sharing capabilities at the swarm level or having a centralized decision making system. In this report, I survey the state of the field of applying adaptive control method to increase swarm robotic systems robustness to the failure of individual robots, and increase its efficiency in performing its task. A few techniques for the division of labor problem are briefly presented while one of them is given in more detail. A discussion of the advantages and disadvantages of this system is given and suggestions of potential improvements that can be made to the system are presented.