Multi-target trapping with swarm robots based on pattern formation

A significant limitation of most previous target trapping algorithms for swarm robots is that the target shape needs to be predefined and only some regular Euclidean shapes can be applied. Besides, splitting and merging of multiple shapes depending on moving targets have not been considered by previous methods. This may be inadequate for dealing with the problem of entrapment in dynamic targets. This paper proposes a flexible shape formation algorithm by using Radial Basis Implicit Function (RBIF) to realize the multi-target trapping task that needs the transformation of trapping shape response to dynamic targets. With this flexible shape formation method, we improve previous methods by allowing most distribution of group targets to be entrapped without a predefined shape and robots to split/merge with regards to the moving targets. The previous bound on the number of reference points for a target shape is triple the number of targets, while it becomes less by considering the convex hulls of targets in the new method. Numerical simulations of static/dynamic scenarios, obstacle avoidance, noise and self-reorganization have been performed to validate the effectiveness and flexibility of the proposed approach for multi-target trapping. (C) 2018 Elsevier B.V. All rights reserved.