Optimized inverse kinematics solutions for a 6-DOF robot

Inverse kinematics (IK) for surgical robotic manipulators requires a balance between computational speed, pose accuracy, and robustness to poor initialization and near-singular configurations. This paper presents a controlled comparative evaluation of optimization-based IK solvers for a 6-DOF industrial robot (Viper 650s) under a unified constrained optimization framework with identical robot modelling assumptions, constraints, trajectories, and evaluation metrics. Six methods are investigated, spanning standalone and hybrid techniques: Sequential Quadratic Programming (SQP), Branch and Bound (B&B), Ant Colony Optimization (ACO), and three hybrid configurations (ACO-SQP, B&B-SQP, and a three-stage ACO-B&B-SQP pipeline). All methods are assessed using the same evaluation framework to examine the practical effect of combining global exploration, bounded search-space reduction, and local refinement on path-following accuracy. Two representative trajectories (a planar triangle and a spatial helix) are executed with repeated trials, and performance is evaluated in terms of trajectory execution time and geometric path-tracking error, supplemented by repeated-run statistical summaries. The optimization methods are implemented in MATLAB and tested within the Automation Control Environment (ACE). The results show that the ACO-B&B-SQP pipeline achieved the lowest mean path error on both trajectories under the present setup, whereas SQP remained the fastest. The study is positioned as a unified comparative evaluation framework for optimization-based IK on the Viper 650s robot, incorporating the implementation and evaluation of a three-stage ACO-B&B-SQP hybrid pipeline within this common framework.