@inproceedings{6d137dc955e44598ab8636643830dcba,
title = "Strategic Sacrifice: Self-organized Robot Swarm Localization for Inspection Productivity",
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.",
keywords = "Collaborative localization, mean-field models, task-allocation",
author = "Sneha Ramshanker and Hungtang Ko and Radhika Nagpal",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.; 17th International Symposium on Distributed Autonomous Robotic Systems, DARS 2024 ; Conference date: 28-10-2024 Through 30-10-2024",
year = "2026",
doi = "10.1007/978-3-032-04584-3\_35",
language = "English (US)",
isbn = "9783032045836",
series = "Springer Proceedings in Advanced Robotics",
publisher = "Springer Nature",
pages = "519--535",
editor = "Alexandra Nilles and Petersen, \{Kirstin H.\} and Lam, \{Tin Lun\} and Amanda Prorok and Michael Rubenstein and Michael Otte",
booktitle = "Distributed Autonomous Robotic Systems - 17th International Symposium",
address = "United States",
}