Decentralized Connectivity-Preserving Deployment of Large-Scale Robot Swarms
This addresses the challenge of scalable and connectivity-preserving deployment for large-scale robot swarms, which is incremental as it builds on existing decentralized methods with new algorithmic variations.
The paper tackles the problem of deploying robot swarms to multiple targets while maintaining network connectivity, presenting two decentralized algorithms (outwards and inwards) that form a logical tree topology and achieve this with periodic reconfigurations, validated through simulations and real-robot experiments.
We present a decentralized and scalable approach for deployment of a robot swarm. Our approach tackles scenarios in which the swarm must reach multiple spatially distributed targets, and enforce the constraint that the robot network cannot be split. The basic idea behind our work is to construct a logical tree topology over the physical network formed by the robots. The logical tree acts as a backbone used by robots to enforce connectivity constraints. We study and compare two algorithms to form the logical tree: outwards and inwards. These algorithms differ in the order in which the robots join the tree: the outwards algorithm starts at the tree root and grows towards the targets, while the inwards algorithm proceeds in the opposite manner. Both algorithms perform periodic reconfiguration, to prevent suboptimal topologies from halting the growth of the tree. Our contributions are (i) The formulation of the two algorithms; (ii) A comparison of the algorithms in extensive physics-based simulations; (iii) A validation of our findings through real-robot experiments.