This paper revisits the numerical inverse kinematics (IK) problem, leveraging modern computational resources and refining the seed selection process to develop a solver that is competitive with analytical-based methods. The proposed seed selection strategy consists of three key stages: (1) utilizing a K-Dimensional Tree (KDTree) to identify seed candidates based on workspace proximity, (2) sorting candidates by joint space adjustment and attempting numerical iterations with the one requiring minimal adjustment, and (3) re-selecting the most distant joint configurations for new attempts in case of failures. The joint space adjustment-based seed selection increases the likelihood of rapid convergence, while the re-attempt strategy effectively helps circumvent local minima and joint limit constraints. Comparison results with both traditional numerical solvers and learning-based methods demonstrate the strengths of the proposed approach in terms of success rate, time efficiency, and accuracy. Additionally, we conduct detailed ablation studies to analyze the effects of various parameters and solver settings, providing practical insights for customization and optimization. The proposed method consistently exhibits high success rates and computational efficiency. It is suitable for time-sensitive applications.

翻译：本文重新审视数值逆运动学问题，利用现代计算资源并优化种子选择过程，开发出一种可与基于解析法相竞争的求解器。所提出的种子选择策略包含三个关键阶段：(1) 利用K维树根据工作空间邻近性识别种子候选值；(2) 按关节空间调整量对候选值排序，并优先尝试调整量最小的候选值进行数值迭代；(3) 在迭代失败时，重新选择距离最远的关节构型进行新尝试。基于关节空间调整量的种子选择提高了快速收敛的可能性，而重试策略则有效帮助规避局部极小值和关节限位约束。与传统数值求解器及基于学习的方法的对比结果表明，所提方法在成功率、时间效率和精度方面具有优势。此外，我们进行了详细的消融研究，以分析不同参数和求解器设置的影响，为定制化和优化提供了实用见解。该方法始终表现出高成功率和计算效率，适用于对时间敏感的应用场景。