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/OT0q4ccGHts

翻译：我们提出了一种新颖的基于学习的轨迹生成算法，用于户外机器人导航。其目标是计算既满足环境特定可通行约束的无碰撞路径。该方法专为无地图环境下、利用有限机载机器人感知能力进行全局规划而设计，同时确保对所有可通行方向的全面覆盖。我们的公式采用条件变分自编码器（CVAE）生成模型，该模型通过可通行约束以及用于覆盖优化的公式进行增强。我们突出了该方法相较于最先进的轨迹生成方法的优势，并展示了其在具有挑战性的大型户外环境中的性能，包括建筑物周围、交叉路口、小径以及越野地形，实验使用了Clearpath Husky和Boston Dynamics Spot机器人。实际应用中，该方法使可通行区域的覆盖率提升了6%，且位于不可通行区域的轨迹段减少了89%。视频链接：https://youtu.be/OT0q4ccGHts