A Workspace-Analysis-Based Genetic Algorithm for Solving Inverse Kinematics of a Multi-Fingered Anthropomorphic Hand

Although the solution of inverse kinematics for a serial redundant manipulator has been widely researched, many algorithms still seem limited in dealing with complex geometries, including multi-finger anthropomorphic hands. In this paper, the inverse kinematic problems of multiple fingers are an aggregate problem when the target points of fingers are given. The fingers are concatenated to the same wrist and the objective is to find a solution for the wrist and two fingers simultaneously. To achieve this goal, a modified immigration genetic algorithm based on workspace analysis is developed and validated. To reduce unnecessary computation of the immigration genetic algorithm, which arises from an inappropriate inverse kinematic request, a database of the two fingers’ workspace is generated using the Monte Carlo method to examine the feasibility of inverse kinematic request. Furthermore, the estimation algorithm provides an optimal set of wrist angles for the immigration genetic algorithm to complete the remaining computation. The results reveal that the algorithm can be terminated immediately even when the inverse kinematic request is out of the workspace. In addition, a distribution of population in each generation illustrates that the optimized wrist angles provide a better initial condition, which significantly improves the convergence of the immigration genetic algorithm.