MTG: 走行可能領域を網羅する地図なし屋外ナビゲーションの軌道生成
MTG: Mapless Trajectory Generator with Traversability Coverage for Outdoor Navigation
地図のない屋外環境で、限られた機載センサのみを用いて走行可能な方向を網羅しつつ衝突のない経路を生成するCVAEベースの学習手法を提案し、HuskyとSpotで検証した。
著者: Jing Liang, Peng Gao, Xuesu Xiao, Adarsh Jagan Sathyamoorthy, Mohamed Elnoor, Ming C. Lin, Dinesh Manocha
分類: cs.RO
原文アブストラクト
We present a novel learning-based trajectory generation algorithm for outdoor robot navigation. Our goal is to compute collision-free paths that also satisfy the environment-specific traversability constraints. Our approach is designed for global planning using limited onboard robot perception in mapless environments while ensuring comprehensive coverage of all traversable directions. Our formulation uses a Conditional Variational Autoencoder (CVAE) generative model that is enhanced with traversability constraints and an optimization formulation used for the coverage. We highlight the benefits of our approach over state-of-the-art trajectory generation approaches and demonstrate its performance in challenging and large outdoor environments, including around buildings, across intersections, along trails, and off-road terrain, using a Clearpath Husky and a Boston Dynamics Spot robot. In practice, our approach results in a 6% improvement in coverage of traversable areas and an 89% reduction in trajectory portions residing in non-traversable regions. Our video is here: https://youtu.be/3eJ2soAzXnU
