複数ロボットによる空間変動場のホットスポット効率的同定
Efficiently Identifying Hotspots in a Spatially Varying Field with Multiple Robots
移動センサを用いて環境中のホットスポットを特定するため、単一ロボットと分散協調型マルチロボットの2手法を提案し、ガウス過程のハイパーパラメータに依存しない適応的アルゴリズムとボロノイ分割・モンテカルロ木探索を組み合わせて評価した。
著者: Varun Suryan, Pratap Tokekar
分類: cs.RO
原文アブストラクト
In this paper, we present algorithms to identify environmental hotspots using mobile sensors. We examine two approaches: one involving a single robot and another using multiple robots coordinated through a decentralized robot system. We introduce an adaptive algorithm that does not require precise knowledge of Gaussian Processes (GPs) hyperparameters, making the modeling process more flexible. The robots operate for a pre-defined time in the environment. The multi-robot system uses Voronoi partitioning to divide tasks and a Monte Carlo Tree Search for optimal path planning. Our tests on synthetic and a real-world dataset of Chlorophyll density from a Pacific Ocean sub-region suggest that accurate estimation of GP hyperparameters may not be essential for hotspot detection, potentially simplifying environmental monitoring tasks.
