Abstract
This paper presents a novel flocking strategy for a swarm of robots that enables the robots to autonomously navigate in an environment populated with obstacles. Robot swarms are often required to move toward an assigned goal while adapting to environmental changes in many applications. Specifically, we pay attention to the swimming behavior of a school of fishes and implement three basic behavior models such as maintenance, partition, and unification subject to the environment condition. The proposed approach is based on the local interaction among individual robots, which requires one robot to dynamically select two neighbors within its sensing range and maintain a uniform distance with each other. To provide full generality, all robots are not allowed to have individual identification numbers, a pre-determined leader, the memory of past actions, and the communication capability. We verify the validity of the proposed algorithm using our in-house simulator. It is demonstrated that a large swarm of robots can be split into multiple groups and/or merged into one passing through multiple narrow passageways.
