Autonomous high-speed navigation through large, complex environments requires real-time generation of agile trajectories that are dynamically feasible, collision-free, and satisfy constraints. Most modern trajectory planning techniques rely on numerical optimization because high-quality, expressive trajectories that satisfy constraints can be systematically computed. However, strict requirements on computation time and the risk of numerical instability can limit the use of optimization-based planners in safety-critical situations. This work presents an optimization-free planning framework called STITCHER that leverages graph search to generate long-range trajectories by stitching short trajectory segments together in real time. STITCHER is shown to outperform modern optimization-based planners through its innovative planning architecture and several algorithmic developments that make real-time planning possible. Simulation results show safe trajectories through complex environments can be generated in milliseconds that cover tens of meters.

翻译：在大型复杂环境中进行高速自主导航，需要实时生成动态可行、无碰撞且满足约束条件的敏捷轨迹。大多数现代轨迹规划技术依赖于数值优化，因为可以通过系统计算得到满足约束条件的高质量、高表现力轨迹。然而，对计算时间的严格要求以及数值不稳定的风险，可能限制基于优化的规划器在安全关键场景中的应用。本文提出了一种名为STITCHER的无优化规划框架，该框架利用图搜索通过实时拼接短轨迹段来生成长距离轨迹。STITCHER凭借其创新的规划架构及多项实现实时规划的算法改进，被证明优于现代基于优化的规划器。仿真结果表明，该系统能在毫秒级时间内生成覆盖数十米复杂环境的安全轨迹。