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：我们提出了一种基于学习的户外机器人导航轨迹生成算法。我们的目标是计算既能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中利用有限车载感知能力的全局规划而设计，同时确保对所有可通行方向的全面覆盖。我们的框架采用结合可通行约束的条件变分自编码器生成模型，并利用优化公式实现覆盖性能。我们突出了该方法相较于最先进轨迹生成技术的优势，并在包含建筑环绕、十字路口穿越、小径行进及越野地形等挑战性大型户外场景中，使用Clearpath Husky和Boston Dynamics Spot机器人验证了其性能。实际应用中，我们的方法使可通行区域覆盖率提升6%，且非可通行区域内的轨迹段占比减少89%。演示视频链接：https://youtu.be/OT0q4ccGHts