動的環境における適応的集合知覚のための軽量変化検出
Lightweight Change Detection for Adaptive Collective Perception in Dynamic Environments
リソース制約のあるロボット群が動的に変化する環境で多数決型の集合知覚を行うため、定メモリ・定計算量の軽量変化検出をベイズ意思決定に統合し、空間相関に応じてサンプリング間隔を適応させる手法を提案した。
著者: Thiemen Siemensma, Tiago Travassos, Jiacheng Zhang, Bahar Haghighat
分類: cs.RO
原文アブストラクト
Swarm robotics enables scalable inspection through collective perception, where robots infer environmental properties from distributed observations. A common benchmark considers robots exploring black-white tiled environments and determining the majority tile type. While most work studies static environments, dynamic environments remain underexplored. We study collective perception in dynamic environments where the tile proportion and the spatial correlation of surface patterns may change over time. To enable implementation on resource-constrained robots, we integrate a lightweight change detection mechanism into a Bayesian collective decision-making framework that resets robot beliefs when distribution shifts are detected. The proposed detector requires constant memory and constant per-sample computation, unlike two recent competitive approaches that store the full sample stream, making it suitable for memory-constrained robots. We further introduce a method that detects spatially correlated sampling and adapts sampling intervals online to mitigate correlation effects. Our simulation results in Webots show competitive change detection delays, more consistent precision across communication settings, and improved decision accuracy under changing spatial correlations.