让传感器逐渐失效的机器人集群仍能准确感知环境状态
BayesCPF: Enabling Collective Perception in Robot Swarms with Degrading Sensors
- 用扩展卡尔曼滤波动态校准随时间退化的传感器
- 在不同退化模型下，估计精度接近已知真实精度时的表现
- 适合研究分布式感知与容错机器人集群的学者
集体感知问题——一群机器人共同感知周围环境并就环境状态达成共识——是群体机器人领域的基础问题。以往研究要么假设整个集群传感器完好，要么仅考虑少数故障成员。另一项研究虽考虑了全集群不可靠传感器，但假设故障模式已知且恒定。为此，本文提出贝叶斯集体感知滤波器（BayesCPF），使传感器持续退化的机器人能够准确估计环境特征的填充率（fill ratio）。核心贡献在于在BayesCPF中引入扩展卡尔曼滤波，实现对随时间变化的传感器退化进行在线校准。我们在模拟和物理实验中验证了该方法在不同退化模型、初始条件和环境下的有效性。结果表明，无论退化模型假设如何，只要保持模型和初始精度假设一致，其填充率估计性能均接近已知真实传感器精度时的表现。
原文摘要 · Abstract (English)
The collective perception problem -- where a group of robots perceives its surroundings and comes to a consensus on an environmental state -- is a fundamental problem in swarm robotics. Past works studying collective perception use either an entire robot swarm with perfect sensing or a swarm with only a handful of malfunctioning members. A related study proposed an algorithm that does account for an entire swarm of unreliable robots but assumes that the sensor faults are known and remain constant over time. To that end, we build on that study by proposing the Bayes Collective Perception Filter (BayesCPF) that enables robots with continuously degrading sensors to accurately estimate the fill ratio -- the rate at which an environmental feature occurs. Our main contribution is the Extended Kalman Filter within the BayesCPF, which helps swarm robots calibrate for their time-varying sensor degradation. We validate our method across different degradation models, initial conditions, and environments in simulated and physical experiments. Our findings show that, regardless of degradation model assumptions, fill ratio estimation using the BayesCPF is competitive to the case if the true sensor accuracy is known, especially when assumptions regarding the model and initial sensor accuracy levels are preserved.
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