# Fault-Tolerant Multi-Robot Coordination with Limited Sensing within Confined Environments

arXiv: https://arxiv.org/abs/2505.15036 · HTML: https://mlsanity.com/paper/2505.15036/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 45 references that are in the map.
- First posted on arXiv 21 May 2025.

## Builds on (most cited first, 2 of 2)

- [Coordinated Robot Navigation via Hierarchical Clustering](https://mlsanity.com/paper/1507.01637/) (2015)
- [Voluntary Retreat for Decentralized Interference Reduction in Robot Swarms](https://mlsanity.com/paper/1812.02193/) (2018)

## Built on by (most cited first, 1 of 1)

- [Deep Reinforcement Learning for Multi-Agent Coordination](https://mlsanity.com/paper/2510.03592/) (2025)

## Abstract (arXiv)

As robots are increasingly deployed to collaborate on tasks within shared workspaces and resources, the failure of an individual robot can critically affect the group's performance. This issue is particularly challenging when robots lack global information or direct communication, relying instead on social interaction for coordination and to complete their tasks. In this study, we propose a novel fault-tolerance technique leveraging physical contact interactions in multi-robot systems, specifically under conditions of limited sensing and spatial confinement. We introduce the "Active Contact Response" (ACR) method, where each robot modulates its behavior based on the likelihood of encountering an inoperative (faulty) robot. Active robots are capable of collectively repositioning stationary and faulty peers to reduce obstructions and maintain optimal group functionality. We implement our algorithm in a team of autonomous robots, equipped with contact-sensing and collision-tolerance capabilities, tasked with collectively excavating cohesive model pellets. Experimental results indicate that the ACR method significantly improves the system's recovery time from robot failures, enabling continued collective excavation with minimal performance degradation. Thus, this work demonstrates the potential of leveraging local, social, and physical interactions to enhance fault tolerance and coordination in multi-robot systems operating in constrained and extreme environments.