縦に険しい地形での効率的な6自由度運動力学モデリングのための地形注意学習
Terrain-Attentive Learning for Efficient 6-DoF Kinodynamic Modeling on Vertically Challenging Terrain
車輪ロボットが縦に険しい地形を走行する際、現在の車両と地形の相互作用に重要な地形のみに注意を向ける6自由度運動力学学習手法を提案し、予測誤差を平均51.1%削減した。
著者: Aniket Datar, Chenhui Pan, Mohammad Nazeri, Anuj Pokhrel, Xuesu Xiao
分類: cs.RO
原文アブストラクト
Wheeled robots have recently demonstrated superior mechanical capability to traverse vertically challenging terrain (e.g., extremely rugged boulders comparable in size to the vehicles themselves). Negotiating such terrain introduces significant variations of vehicle pose in all six Degrees-of-Freedom (DoFs), leading to imbalanced contact forces, varying momentum, and chassis deformation due to non-rigid tires and suspensions. To autonomously navigate on vertically challenging terrain, all these factors need to be efficiently reasoned within limited onboard computation and strict real-time constraints. In this paper, we propose a 6-DoF kinodynamics learning approach that is attentive only to the specific underlying terrain critical to the current vehicle-terrain interaction, so that it can be efficiently queried in real-time motion planners onboard small robots. Physical experiment results show our Terrain-Attentive Learning demonstrates on average 51.1% reduction in model prediction error among all 6 DoFs compared to a state-of-the-art model for vertically challenging terrain.
