In this paper, we investigate the problem of maximizing the manipulability index while solving a general Inverse Kinematics (IK) problem of a redundant industrial manipulator. Manipulability index has been extensively studied in the robotics literature and several formulae have been developed, nevertheless, they mainly only exploit the robot redundancy. The general IK is formulated as a Quadratic Programming (QP) that can seamlessly incorporate inequality constraints, such as collision avoidance, and we propose two new formulae to integrate the manipulability index maximization into the QP-based IK solver. We then thoroughly analyze the performance of the proposed formulae in simulation and validate them on a real Baxter research robot. The experimental results revealed the outperformance of the proposed formulae in comparison with the classical formula in the literature. Hence, providing a way to improve the manipulability index of a recorded trajectory, e.g. by learning by demonstration, or an offline generated one by a motion planning algorithm.

在本文中，我们研究了在解决冗余工业机械手的一般逆运动学（IK）问题的同时最大化可操作性指标的问题。可操纵性指标已在机器人技术文献中得到了广泛研究，并且已经开发了一些公式，但是它们主要仅利用了机器人冗余。通用IK公式化为二次规划（QP），可以无缝合并不等式约束，例如避免碰撞，我们提出了两个新公式，将可操作性指标最大化集成到基于QP的IK求解器中。然后，我们在仿真中彻底分析所提出的公式的性能，并在真正的Baxter研究机器人上对其进行验证。实验结果表明，与文献中的经典公式相比，该公式的性能优于其他公式。因此，提供了一种改进记录轨迹的可操作性指标的方法，例如通过演示学习或通过运动计划算法离线生成的轨迹。