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
@article{arxiv.2309.08214,
title = {MTG: Mapless Trajectory Generator with Traversability Coverage for Outdoor Navigation},
author = {Jing Liang and Peng Gao and Xuesu Xiao and Adarsh Jagan Sathyamoorthy and Mohamed Elnoor and Ming C. Lin and Dinesh Manocha},
journal= {arXiv preprint arXiv:2309.08214},
year = {2024}
}