Resilient source seeking with robot swarms

TL;DR

This paper introduces a resilient swarm-based method for source seeking that relies on density and relative position measurements rather than gradient information, ensuring robustness against robot loss and environmental noise.

Contribution

The paper proposes a novel swarm algorithm for source seeking that maintains functionality through density-based analysis and shape adaptation, enhancing resilience and robustness.

Findings

The swarm can accurately locate the source using density and relative position data.

The approach remains effective despite robot loss and sensor noise.

Numerical validation with hundreds of robots demonstrates robustness.

Abstract

We present a solution for locating the source, or maximum, of an unknown scalar field using a swarm of mobile robots. Unlike relying on the traditional gradient information, the swarm determines an ascending direction to approach the source with arbitrary precision. The ascending direction is calculated from measurements of the field strength at the robot locations and their relative positions concerning the centroid. Rather than focusing on individual robots, we focus the analysis on the density of robots per unit area to guarantee a more resilient swarm, i.e., the functionality remains even if individuals go missing or are misplaced during the mission. We reinforce the robustness of the algorithm by providing sufficient conditions for the swarm shape so that the ascending direction is almost parallel to the gradient. The swarm can respond to an unexpected environment by morphing its shape and exploiting the existence of multiple ascending directions. Finally, we validate our approach numerically with hundreds of robots. The fact that a large number of robots always calculate an ascending direction compensates for the loss of individuals and mitigates issues arising from the actuator and sensor noises.