This paper proposes actuator-aware inverse kinematics for torque-controlled redundant robots under joint-limit constraints. In the considered architecture, the inverse-kinematic output is not merely a purely kinematic joint-velocity command; it is the required joint velocity supplied to a downstream torque-level controller. Therefore, a small commanded task residual may not necessarily improve realized motion. The proposed method formulates a convex quadratic programming problem whose decision variable is the joint-level required velocity. Control barrier function style bounds impose reference-level joint-limit admissibility, while the task equation is handled through a penalized slack variable. Redundancy is resolved using a controller-compatibility objective that accounts for previous-command consistency and actuator torque-capacity weighting. The method is independent of the particular torque-level controller and can serve as an intermediate IK layer between an endpoint trajectory and a redundant robot controller. Experiments on a virtual-decomposition-controlled seven-degree-of-freedom upper-limb exoskeleton compare the method with standard inverse-kinematic baselines and a constrained task-preserving quadratic programming baseline. The results indicate lower limit-pushing commands, bounded admissible required velocities, and improved realized task behavior in the tested trajectory, without modifying the downstream controller.

翻译：本文针对关节极限约束下的力矩控制冗余机器人，提出了一种作动器感知的逆运动学方法。在所考虑的架构中，逆运动学输出并非纯粹的关节速度指令，而是提供给下游力矩级控制器的所需关节速度。因此，较小的任务残差并不一定能改善实际运动效果。所提方法构建了一个凸二次规划问题，其决策变量为关节级所需速度。通过控制障碍函数形式的约束，施加参考级关节极限可行性，同时利用带惩罚的松弛变量处理任务方程。冗余度通过考虑先前指令一致性与作动器力矩容量加权的控制器相容性目标来求解。该方法独立于特定的力矩级控制器，可作为末端轨迹与冗余机器人控制器之间的中间逆运动学层。在虚拟分解控制的七自由度上肢外骨骼上进行实验，将所提方法与标准逆运动学基线及带约束的任务保持二次规划基线进行了对比。结果表明，该方法在不修改下游控制器的情况下，在测试轨迹上能够产生更少的极限趋近指令、有界的可行所需速度，并改善了实际任务执行行为。