Resilient robot teams: a review integrating decentralised control, change-detection, and learning

TL;DR

This review explores how decentralized control, change detection, and learning techniques can enhance the resilience of robot teams, addressing current challenges and future research directions for robust multi-robot systems.

Contribution

It synthesizes recent advances in resilient robot team control, change detection, and learning, highlighting integration challenges and proposing future research avenues.

Findings

Exogenous fault detection enables generic and specific diagnosis.

Distributed sensing detects environmental changes and anomalies.

Resilient control methods include multi-agent reinforcement learning and stigmergy.

Abstract

Purpose of review: This paper reviews opportunities and challenges for decentralised control, change-detection, and learning in the context of resilient robot teams. Recent findings: Exogenous fault detection methods can provide a generic detection or a specific diagnosis with a recovery solution. Robot teams can perform active and distributed sensing for detecting changes in the environment, including identifying and tracking dynamic anomalies, as well as collaboratively mapping dynamic environments. Resilient methods for decentralised control have been developed in learning perception-action-communication loops, multi-agent reinforcement learning, embodied evolution, offline evolution with online adaptation, explicit task allocation, and stigmergy in swarm robotics. Summary: Remaining challenges for resilient robot teams are integrating change-detection and trial-and-error learning methods, obtaining reliable performance evaluations under constrained evaluation time, improving the safety of resilient robot teams, theoretical results demonstrating rapid adaptation to given environmental perturbations, and designing realistic and compelling case studies.