遠隔操作移動ロボットナビゲーションにおける操作者意図認識のための教師あり機械学習アプローチ
A Supervised Machine Learning Approach to Operator Intent Recognition for Teleoperated Mobile Robot Navigation
ロボットのセンサデータから操作者のナビゲーション意図をオンラインで推定する教師あり機械学習手法を提案し、模擬環境での実験で有効性を示した。
著者: Evangelos Tsagkournis, Dimitris Panagopoulos, Giannis Petousakis, Grigoris Nikolaou, Rustam Stolkin, Manolis Chiou
分類: cs.RO
原文アブストラクト
In applications that involve human-robot interaction (HRI), human-robot teaming (HRT), and cooperative human-machine systems, the inference of the human partner's intent is of critical importance. This paper presents a method for the inference of the human operator's navigational intent, in the context of mobile robots that provide full or partial (e.g., shared control) teleoperation. We propose the Machine Learning Operator Intent Inference (MLOII) method, which a) processes spatial data collected by the robot's sensors; b) utilizes a supervised machine learning algorithm to estimate the operator's most probable navigational goal online. The proposed method's ability to reliably and efficiently infer the intent of the human operator is experimentally evaluated in realistically simulated exploration and remote inspection scenarios. The results in terms of accuracy and uncertainty indicate that the proposed method is comparable to another state-of-the-art method found in the literature.
