让传感器损坏的机器人集群自校准，提升群体决策准确率。
Adaptive Self-Calibration for Minimalistic Collective Perception by Imperfect Robot Swarms
- 用假设检验实现传感器精度的动态自校准。
- 自校准后性能接近已知精度时的水平。
- 适合传感器不稳定的集群机器人系统。
群体感知是群机器人中的基础问题，常被建模为最佳选择决策（best-of-$n$）。以往研究多假设机器人具备完美传感或仅有少量故障机器人。我们此前提出极简群体感知（MCP）算法（arxiv:2209.12858），可在整个集群传感器严重损坏的情况下仍做出正确决策。但该算法依赖已知的传感器精度，这在现实中难以实现。本文消除此假设，(i) 研究了估计性能的下降，(ii) 提出自适应传感器退化滤波器（ASDF）以缓解下降。将MCP与假设检验结合，实现机器人对自身传感器精度的自适应自校准。我们在多个参数下验证该方法。结果表明，已知精度的集群性能优于未知精度的集群；然而，使用ASDF后性能显著改善，甚至达到预先知晓正确精度时的水平。
原文摘要 · Abstract (English)
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.
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