This work introduces the BoundMPC strategy, an innovative online model-predictive path-following approach for robot manipulators. This joint-space trajectory planner allows the following of Cartesian reference paths in the end-effector's position and orientation, including via-points, within the desired asymmetric bounds of the orthogonal path error. These bounds encode the obstacle-free space and additional task-specific constraints in Cartesian space. Contrary to traditional path-following concepts, BoundMPC purposefully deviates from the Cartesian reference path in position and orientation to account for the robot's kinematics, leading to more successful task executions for Cartesian reference paths. Furthermore the simple reference path formulation is computationally efficient and allows for replanning during the robot's motion. This feature makes it possible to use this planner for dynamically changing environments and varying goals. The flexibility and performance of BoundMPC are experimentally demonstrated by five scenarios on a 7-DoF Kuka LBR iiwa 14 R820 robot. The first scenario shows the transfer of a larger object from a start to a goal pose through a confined space where the object must be tilted. The second scenario deals with grasping an object from a table where the grasping point changes during the robot's motion, and collisions with other obstacles in the scene must be avoided. The adaptability of BoundMPC is showcased in scenarios such as the opening of a drawer, the transfer of an open container, and the wiping of a table, where it effectively handles task-specific constraints. The last scenario highlights the possibility of accounting for collisions with the entire robot's kinematic chain. The code is readily available at https://github.com/thieso/boundmpc, inspiring you to explore its potential and adapt it to your specific robotic tasks.

翻译：本文提出BoundMPC策略，一种创新的机器人操作臂在线模型预测路径跟踪方法。该关节空间轨迹规划器能够在末端执行器位置和姿态上跟踪包含路径点的笛卡尔参考路径，同时将正交路径误差控制在期望的非对称边界内。这些边界编码了笛卡尔空间中的无障碍区域及特定任务的附加约束。与传统路径跟踪方法不同，BoundMPC策略性地在位置和姿态上偏离笛卡尔参考路径以适应机器人运动学特性，从而显著提升笛卡尔参考路径的任务执行成功率。此外，简洁的参考路径表述具有计算高效性，支持机器人在运动过程中进行重规划，使其能够适用于动态变化的环境与目标。通过在7自由度Kuka LBR iiwa 14 R820机器人上进行的五种场景实验，验证了BoundMPC的灵活性与性能。第一场景展示了大型物体在受限空间内从起始位姿到目标位姿的转移过程，期间需倾斜物体；第二场景涉及从桌面抓取物体，要求在机器人运动过程中动态调整抓取点并规避场景中的其他障碍物。BoundMPC的适应性在抽屉开启、开口容器转移及桌面擦拭等场景中得到充分展现，能有效处理各类任务特定约束。最后场景突显了该方法规避机器人完整运动链碰撞的能力。相关代码已发布于https://github.com/thieso/boundmpc，以促进研究者探索其潜力并适配特定机器人任务。