Online trajectory planning enables robot manipulators to react quickly to changing environments or tasks. Many robot trajectory planners exist for known environments but are often too slow for online computations. Current methods in online trajectory planning do not find suitable trajectories in challenging scenarios that respect the limits of the robot and account for collisions. This work proposes a trajectory planning framework consisting of the novel Cartesian path planner based on convex sets, called BoundPlanner, and the online trajectory planner BoundMPC. BoundPlanner explores and maps the collision-free space using convex sets to compute a reference path with bounds. BoundMPC is extended in this work to handle convex sets for path deviations, which allows the robot to optimally follow the path within the bounds while accounting for the robot's kinematics. Collisions of the robot's kinematic chain are considered by a novel convex-set-based collision avoidance formulation independent on the number of obstacles. Simulations and experiments with a 7-DoF manipulator show the performance of the proposed planner compared to state-of-the-art methods. The source code is available at github.com/TU-Wien-ACIN-CDS/BoundPlanner and videos of the experiments can be found at www.acin.tuwien.ac.at/42d4.

翻译：在线轨迹规划使机器人机械臂能够快速响应环境或任务的变化。现有许多针对已知环境的机器人轨迹规划方法，但通常计算速度过慢，难以满足在线计算需求。当前在线轨迹规划方法在复杂场景中难以同时满足机器人运动约束与避障要求。本研究提出一种轨迹规划框架，包含基于凸集的新型笛卡尔路径规划器BoundPlanner与在线轨迹规划器BoundMPC。BoundPlanner通过凸集探索并映射无碰撞空间，计算带边界约束的参考路径。本研究扩展了BoundMPC以处理路径偏离的凸集表示，使机器人能够在边界内最优跟踪路径，同时考虑运动学约束。通过新型基于凸集的避障公式考虑机器人运动链的碰撞检测，其复杂度与障碍物数量无关。基于7自由度机械臂的仿真与实验表明，所提规划器性能优于现有先进方法。源代码发布于github.com/TU-Wien-ACIN-CDS/BoundPlanner，实验视频可访问www.acin.tuwien.ac.at/42d4获取。