DTG：拡散モデルによる地図なし屋外ナビゲーションのための軌道生成
DTG : Diffusion-based Trajectory Generation for Mapless Global Navigation
拡散モデルと条件付きRNNを用いて、地図のない屋外環境で障害物や未舗装路を避けつつ目的地までの走行距離を抑えた軌道を生成する手法を提案し、実機実験で走行距離と走破性の改善を確認した。
著者: Jing Liang, Amirreza Payandeh, Daeun Song, Xuesu Xiao, Dinesh Manocha
分類: cs.RO
原文アブストラクト
We present a novel end-to-end diffusion-based trajectory generation method, DTG, for mapless global navigation in challenging outdoor scenarios with occlusions and unstructured off-road features like grass, buildings, bushes, etc. Given a distant goal, our approach computes a trajectory that satisfies the following goals: (1) minimize the travel distance to the goal; (2) maximize the traversability by choosing paths that do not lie in undesirable areas. Specifically, we present a novel Conditional RNN(CRNN) for diffusion models to efficiently generate trajectories. Furthermore, we propose an adaptive training method that ensures that the diffusion model generates more traversable trajectories. We evaluate our methods in various outdoor scenes and compare the performance with other global navigation algorithms on a Husky robot. In practice, we observe at least a 15% improvement in traveling distance and around a 7% improvement in traversability.
