不整地走行における一貫したナビゲーション行動のためのロボット適応
Robot Adaptation for Generating Consistent Navigational Behaviors over Unstructured Off-Road Terrain
車輪滑りや空気圧低下などの外乱を明示的にモデル化せず、実際の行動と期待行動のずれを補正するオフセット行動を学習し、不整地でも一貫した走行を実現する手法を提案した。
著者: Sriram Siva, Maggie Wigness, John G. Rogers, Hao Zhang
分類: cs.RO
原文アブストラクト
Terrain adaptation is an essential capability for a ground robot to effectively traverse unstructured off-road terrain in real-world field environments such as forests. However, the expected robot behaviors generated by terrain adaptation methods cannot always be executed accurately due to setbacks such as wheel slip and reduced tire pressure. To address this problem, we propose a novel approach for consistent behavior generation that enables the ground robot's actual behaviors to more accurately match expected behaviors while adapting to a variety of unstructured off-road terrain. Our approach learns offset behaviors that are used to compensate for the inconsistency between the actual and expected behaviors without requiring the explicit modeling of various setbacks. Our approach is also able to estimate the importance of the multi-modal features to improve terrain representations for better adaptation. In addition, we develop an algorithmic solver for our formulated regularized optimization problem, which is guaranteed to converge to the global optimal solution. To evaluate the method, we perform extensive experiments using various unstructured off-road terrain in real-world field environments. Experimental results have validated that our approach enables robots to traverse complex unstructured off-road terrain with more navigational behavior consistency, and it outperforms previous methods, particularly so on challenging terrain.
