Nowadays, robots are applied in dynamic environments. For a robust operation, the motion planning module must consider other tasks besides reaching a specified pose: (self) collision avoidance, joint limit avoidance, keeping an advantageous configuration, etc. Each task demands different joint control commands, which may counteract each other. We present a hierarchical control that, depending on the robot and environment state, determines online a suitable priority among those tasks. Thereby, the control command of a lower-prioritized task never hinders the control command of a higher-prioritized task. We ensure smooth control signals also during priority rearrangement. Our hierarchical control computes reference joint velocities. However, the underlying concepts of hierarchical control differ when using joint accelerations or joint torques as control signals instead. So, as a further contribution, we provide a comprehensive discussion on how joint velocity control, joint acceleration control, and joint torque control differ in hierarchical task control. We validate our formulation in an experiment on hardware.

翻译：如今，机器人被应用于动态环境中。为实现稳健运行，运动规划模块除达到指定位姿外，还需考虑其他任务：如（自身）碰撞规避、关节限位规避、保持有利构型等。每项任务要求不同的关节控制指令，这些指令可能相互矛盾。我们提出一种分层控制方法，该方法根据机器人与环境状态在线确定任务间的合适优先级。由此，低优先级任务的控制指令永不干扰高优先级任务的控制指令。我们确保在优先级动态调整过程中控制信号保持平滑。该分层控制方法计算参考关节速度。然而，当采用关节加速度或关节力矩作为控制信号时，分层控制的底层逻辑存在差异。因此，作为另一贡献，我们全面讨论了关节速度控制、关节加速度控制与关节力矩控制在分层任务控制中的本质区别。最终，我们通过硬件实验验证了所提方法的有效性。