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

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。目标是计算满足环境特定可穿越性约束的无碰撞路径。该方法专为无地图环境中的全局规划而设计，利用机器人有限的车载感知能力，同时确保对所有可穿越方向的全面覆盖。本方案采用条件变分自编码器生成模型，该模型增强了可穿越性约束及用于覆盖优化的优化公式。我们重点展示了该方法相较于最先进轨迹生成方法的优势，并在包括建筑周边、交叉路口、小径及越野地形等具有挑战性的大型户外环境中，使用Clearpath Husky与Boston Dynamics Spot机器人验证了其性能。实际应用中，该方法使可穿越区域覆盖率提升6%，并减少了89%位于不可穿越区域的轨迹片段。视频链接：https://youtu.be/3eJ2soAzXnU