Biologically inspired swarm robotic network ensuring coverage and connectivity

Abstract

Swarm robots provide greater flexibility and robust performance in tasks such as sensing and monitoring of unstructured and unpredictable environments. They need to self-deploy in these environments maximizing coverage and maintaining network connectivity for efficient operation. Inspired from nature, we design a new algorithm based on simple local rules, which achieves coverage and connectivity as an emergent property of the algorithm. We adopt a force-based variant of the local rules seen in animal aggregation behaviours such as flocking of birds and schooling of fish in our design. Each robot is subject to three forces: a) A separation force that pushes it away from its neighbours and increases the size of the swarm. b) A cohesion force that maintains the connectivity of the swarm. c) An alignment force that keeps it aligned to its neighbours and makes relocation to uncovered areas faster. Empirical analysis shows that this swarm-based algorithm outperforms most prominent state-of-the-art algorithms by achieving better and faster coverage.