Particle Swarm Optimization Based Source Seeking

TL;DR

This paper presents a method for using Particle Swarm Optimization with mobile robots to locate electromagnetic signal sources, addressing real-world challenges like noise, obstacles, and collision avoidance through practical modifications and experimental validation.

Contribution

It introduces adaptations of PSO for physical robots in noisy, obstacle-rich environments, including strategies for real-world implementation and validation.

Findings

Successful source seeking in experimental testbeds

Effective obstacle and collision avoidance strategies

Validated PSO-based approach in real-world scenarios

Abstract

Signal source seeking using autonomous vehicles is a complex problem. The complexity increases manifold when signal intensities captured by physical sensors onboard are noisy and unreliable. Added to the fact that signal strength decays with distance, noisy environments make it extremely difficult to describe and model a decay function. This paper addresses our work with seeking maximum signal strength in a continuous electromagnetic signal source with mobile robots, using Particle Swarm Optimization (PSO). A one to one correspondence with swarm members in a PSO and physical Mobile robots is established and the positions of the robots are iteratively updated as the PSO algorithm proceeds forward. Since physical robots are responsive to swarm position updates, modifications were required to implement the interaction between real robots and the PSO algorithm. The development of modifications necessary to implement PSO on mobile robots, and strategies to adapt to real life environments such as obstacles and collision objects are presented in this paper. Our findings are also validated using experimental testbeds.