Grouping-based adaptive spatial formation of swarm robots in a dynamic environment

Abstract

Spatial formations of swarm robots are increasingly applied in many domains in which the environments are dynamic and unpredictable. The autonomy of the individual robots and decentralization of the entire system increase the complexity of the response to environmental changes, which could prolong the formation convergence and significantly increase the communication cost. To address these issues, we propose an adaptive mechanism with three basic behaviours for each individual robot and design a grouping-based spatial formation algorithm for swarm robots to respond to changes and accomplish shape formation. Specifically, the robots are automatically partitioned into several groups based on their spatial neighbours. In this manner, the interactions and self-organization of robots are primarily performed at the intra-group rather than inter-group level, leading to decreased communication costs. Furthermore, this grouping mechanism naturally supports parallel formation and therefore improves the convergence speed. Our simulation and experimental results demonstrate that the proposed method significantly improves the convergence speed and decreases the communication cost, thus validating the effectiveness of the proposed adaptive mechanism.