不整地屋外環境における深層強化学習を用いた信頼性の高い経路計画
TERP: Reliable Planning in Uneven Outdoor Environments using Deep Reinforcement Learning
地形の高低差マップとロボット姿勢から注意マスクを生成し、安定性の低い領域を避けるコストマップと動的に実行可能な軌道を算出するナビゲーション手法を提案した。実機実験で成功率35%向上、標高勾配26%低減を達成した。
著者: Kasun Weerakoon, Adarsh Jagan Sathyamoorthy, Utsav Patel, Dinesh Manocha
分類: cs.RO
原文アブストラクト
We present a novel method for reliable robot navigation in uneven outdoor terrains. Our approach employs a novel fully-trained Deep Reinforcement Learning (DRL) network that uses elevation maps of the environment, robot pose, and goal as inputs to compute an attention mask of the environment. The attention mask is used to identify reduced stability regions in the elevation map and is computed using channel and spatial attention modules and a novel reward function. We continuously compute and update a navigation cost-map that encodes the elevation information or the level-of-flatness of the terrain using the attention mask. We then generate locally least-cost waypoints on the cost-map and compute the final dynamically feasible trajectory using another DRL-based method. Our approach guarantees safe, locally least-cost paths and dynamically feasible robot velocities in uneven terrains. We observe an increase of 35.18% in terms of success rate and, a decrease of 26.14% in the cumulative elevation gradient of the robot's trajectory compared to prior navigation methods in high-elevation regions. We evaluate our method on a Husky robot in real-world uneven terrains (~ 4m of elevation gain) and demonstrate its benefits.
