Adaptive Self-Calibration for Minimalistic Collective Perception by Imperfect Robot Swarms

TL;DR

This paper introduces an adaptive self-calibration method for robot swarms to improve collective perception accuracy despite sensor degradation, outperforming previous fixed-assumption approaches.

Contribution

It develops an adaptive sensor calibration technique that eliminates the need for prior sensor accuracy knowledge in swarm perception algorithms.

Findings

ASDF reduces performance decline due to sensor damage

Adaptive calibration achieves performance comparable to known accuracy scenarios

Swarm perception robustness significantly improves with the proposed method

Abstract

Collective perception is a fundamental problem in swarm robotics, often cast as best-of-$n$ decision-making. Past studies involve robots with perfect sensing or with small numbers of faulty robots. We previously addressed these limitations by proposing an algorithm, here referred to as Minimalistic Collective Perception (MCP) [arxiv:2209.12858], to reach correct decisions despite the entire swarm having severely damaged sensors. However, this algorithm assumes that sensor accuracy is known, which may be infeasible in reality. In this paper, we eliminate this assumption to (i) investigate the decline of estimation performance and (ii) introduce an Adaptive Sensor Degradation Filter (ASDF) to mitigate the decline. We combine the MCP algorithm and a hypothesis test to enable adaptive self-calibration of robots' assumed sensor accuracy. We validate our approach across several parameters of interest. Our findings show that estimation performance by a swarm with correctly known accuracy is superior to that by a swarm unaware of its accuracy. However, the ASDF drastically mitigates the damage, even reaching the performance levels of robots aware a priori of their correct accuracy.