An approach to solving the inverse kinematics problem of virtual human's lower limbs kinematic chain

摘要

The lower limbs kinematic chain (LLKC) is the important part of human body. It contains two joints with four degrees of freedom (DOF). As it is difficult to compute LLKC's inverse kinematic solution quickly and accurately by using neural network or genetic algorithm (GA) due to its high degree of freedom, we proposed a BP-GA approach by combining BP neural network and GA to resolve it. Firstly, the mathematical model of LLKC was built based on D-H method. Then BP neural network output a local optimal solution, which could be served as an individual of GA initial population. And the searching domain of GA could be determined by local optimal solution. Finally, the high-accuracy solution was searched by using the adaptive GA. The actions of walking, squatting and seated position were simulated and results showed that the precise solution could be calculated efficiently by using the proposed approach with the BP neural network high-speed and GA high-accuracy.