IKSel: Selecting Good Seed Joint Values for Fast Numerical Inverse Kinematics Iterations

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 method discretizes the robot configuration space via Centroidal Voronoi Tessellation (CVT) indexed in a KDTree, ranks candidate joint configurations by minimal joint-space adjustment, and iteratively re-attempt with the next-nearest seeds in pose space according to CVT Voronoi distance. 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.