Inspiring and Modeling Multi-Robot Search with Particle Swarm Optimization

TLDR

Multi‑robot search is a rapidly growing research area that seeks efficient algorithms for teams of robots to locate targets, often borrowing techniques from particle swarm optimization. The authors propose a multi‑search algorithm inspired by particle swarm optimization to improve coordinated target finding. They adapt PSO by altering its dynamics to emulate multi‑robot search, enabling abstract modeling of how robot count and communication range influence performance.

Abstract

Within the field of multi-robot systems, multi-robot search is one area which is currently receiving a lot of research attention. One major challenge within this area is to design effective algorithms that allow a team of robots to work together to find their targets. Techniques have been adopted for multi-robot search from the particle swarm optimization algorithm, which uses a virtual multi-agent search to find optima in a multi-dimensional function space. We present here a multi-search algorithm inspired by particle swarm optimization. Additionally, we exploit this inspiration by modifying the particle swarm optimization algorithm to mimic the multi-robot search process, thereby allowing us to model at an abstracted level the effects of changing aspects and parameters of the system such as number of robots and communication range