Rapidly adapting robot swarms with Swarm Map-based Bayesian Optimisation

TL;DR

This paper introduces two algorithms, SMBO and SMBO-Dec, for rapid behaviour adaptation in swarm robotics, enabling quick recovery from environmental disturbances through Bayesian optimisation techniques.

Contribution

The paper presents novel centralized and decentralized Bayesian optimisation algorithms for swarm behaviour adaptation, improving recovery speed and effectiveness after perturbations.

Findings

SMBO and SMBO-Dec outperform random search and gradient descent.

Achieve up to 80% performance recovery within 30 evaluations.

Effective in diverse disturbance scenarios including sensor faults and environmental changes.

Abstract

Rapid performance recovery from unforeseen environmental perturbations remains a grand challenge in swarm robotics. To solve this challenge, we investigate a behaviour adaptation approach, where one searches an archive of controllers for potential recovery solutions. To apply behaviour adaptation in swarm robotic systems, we propose two algorithms: (i) Swarm Map-based Optimisation (SMBO), which selects and evaluates one controller at a time, for a homogeneous swarm, in a centralised fashion; and (ii) Swarm Map-based Optimisation Decentralised (SMBO-Dec), which performs an asynchronous batch-based Bayesian optimisation to simultaneously explore different controllers for groups of robots in the swarm. We set up foraging experiments with a variety of disturbances: injected faults to proximity sensors, ground sensors, and the actuators of individual robots, with 100 unique combinations for each type. We also investigate disturbances in the operating environment of the swarm, where the swarm has to adapt to drastic changes in the number of resources available in the environment, and to one of the robots behaving disruptively towards the rest of the swarm, with 30 unique conditions for each such perturbation. The viability of SMBO and SMBO-Dec is demonstrated, comparing favourably to variants of random search and gradient descent, and various ablations, and improving performance up to 80% compared to the performance at the time of fault injection within at most 30 evaluations.