人間と自律ロボットの協調による目標探索のための閉ループベイズ意味データ融合
Closed-loop Bayesian Semantic Data Fusion for Collaborative Human-Autonomy Target Search
人間の曖昧な言語観察とロボットのセンサ検出を統合し、CPOMDP計画で動的目標を効率的に探索・捕捉する協調人間機械センシング手法を実機検証した。
著者: Luke Burks, Ian Loefgren, Luke Barbier, Jeremy Muesing, Jamison McGinley, Sousheel Vunnam, Nisar Ahmed
分類: cs.RO, cs.AI, cs.SY, eess.SY
原文アブストラクト
In search applications, autonomous unmanned vehicles must be able to efficiently reacquire and localize mobile targets that can remain out of view for long periods of time in large spaces. As such, all available information sources must be actively leveraged -- including imprecise but readily available semantic observations provided by humans. To achieve this, this work develops and validates a novel collaborative human-machine sensing solution for dynamic target search. Our approach uses continuous partially observable Markov decision process (CPOMDP) planning to generate vehicle trajectories that optimally exploit imperfect detection data from onboard sensors, as well as semantic natural language observations that can be specifically requested from human sensors. The key innovation is a scalable hierarchical Gaussian mixture model formulation for efficiently solving CPOMDPs with semantic observations in continuous dynamic state spaces. The approach is demonstrated and validated with a real human-robot team engaged in dynamic indoor target search and capture scenarios on a custom testbed.
