In this paper, we present an online method for converting an arbitrary geometric path represented by a sequence of states, generated by any planner (e.g., sampling-based planners like RRT or PRM, search-based planners like ARA*, etc.), into a corresponding kinematically feasible, jerk-limited trajectory. The method generates a sequence of quintic/quartic splines that can be discretized at a user-specified control rate, and then streamed to a low-level robot controller. Our approach enables real-time adaptation to newly captured changes in the environment. It can also be re-invoked at any time instance to generate a new trajectory from the robot's current to a desired target state or sequence of states. We can guarantee that the trajectory will remain collision-free for a certain amount of time in dynamic environments, while allowing bounded geometric deviation from the original path. The kinematic constraints are taken into account, including limited jerk. We validate the approach in a comparative simulation study against the competing method, demonstrating favorable behavior w.r.t. smoothness, computational time, and real-time performance, particularly in scenarios with frequent changes of target states (up to 1 [kHz]). Experiments on a real robot demonstrate that the proposed approach can be used in real-world scenarios including human presence.

翻译：本文提出一种在线方法，可将任意规划器（例如基于采样的规划器如RRT或PRM、基于搜索的规划器如ARA*等）生成的由状态序列表示的几何路径，转换为对应的运动学可行且加加速度受限的轨迹。该方法生成一系列五次/四次样条曲线，这些曲线可按用户指定的控制频率进行离散化，随后传输至底层机器人控制器。我们的方法能够实时适应环境中新捕获的变化，并可在任意时刻重新调用，以从机器人当前状态到期望目标状态（或状态序列）生成新轨迹。我们能够保证在动态环境中，轨迹在一定时间内保持无碰撞状态，同时允许相对于原始路径的有界几何偏差。方法考虑了包括加加速度限制在内的运动学约束。我们通过对比仿真实验验证了该方法相对于竞争方案的优势，在平滑性、计算时间和实时性能方面（特别是在目标状态频繁变化的场景中，频率高达1[kHz]）均表现出更优特性。真实机器人实验表明，所提方法可在包含人员存在的实际场景中有效应用。