煙による時変センサ可視性制約下でのマルチエージェントエルゴディック探索
Multi-Agent Ergodic Exploration under Smoke-Based, Time-Varying Sensor Visibility Constraints
煙の拡散でセンサ視界が変化する山火事探索において、エルゴディック軌道最適化と可視性モデルを組み合わせ、複数ドローンが効率的に情報収集する手法を提案した。
著者: Elena Wittemyer, Ananya Rao, Ian Abraham, Howie Choset
分類: cs.RO
原文アブストラクト
In this work, we consider the problem of multi-agent informative path planning (IPP) for robots whose sensor visibility continuously changes as a consequence of a time-varying natural phenomenon. We leverage ergodic trajectory optimization (ETO), which generates paths such that the amount of time an agent spends in an area is proportional to the expected information in that area. We focus specifically on the problem of multi-agent drone search of a wildfire, where we use the time-varying environmental process of smoke diffusion to construct a sensor visibility model. This sensor visibility model is used to repeatedly calculate an expected information distribution (EID) to be used in the ETO algorithm. Our experiments show that our exploration method achieves improved information gathering over both baseline search methods and naive ergodic search formulations.
