Strategic Sacrifice: Self-Organized Robot Swarm Localization for Inspection Productivity

TL;DR

This paper presents a biologically inspired cooperative localization method for robot swarms that enhances inspection productivity by self-organizing sacrifice, reducing computational costs while maintaining accuracy.

Contribution

It introduces a novel self-organized sacrifice mechanism for robot localization that adaptively maximizes inspection productivity in dynamic environments.

Findings

The approach reduces computational resources needed for localization.

The method is robust across different environmental conditions.

Hardware experiments validate the effectiveness of the proposed mechanism.

Abstract

Robot swarms offer significant potential for inspecting diverse infrastructure, ranging from bridges to space stations. However, effective inspection requires accurate robot localization, which demands substantial computational resources and limits productivity. Inspired by biological systems, we introduce a novel cooperative localization mechanism that minimizes collective computation expenditure through self-organized sacrifice. Here, a few agents bear the computational burden of localization; through local interactions, they improve the inspection productivity of the swarm. Our approach adaptively maximizes inspection productivity for unconstrained trajectories in dynamic interaction and environmental settings. We demonstrate the optimality and robustness using mean-field analytical models, multi-agent simulations, and hardware experiments with metal climbing robots inspecting a 3D cylinder.