Research on Joint Path Optimization Based on Inverse Kinematics Model and Genetic Algorithm

This paper presents a comprehensive study on the optimization of joint trajectories based on an inverse kinematics model integrated with a genetic algorithm (GA). Initially, a mathematical model is established with precise parametric definitions to delineate the relative positions and orientations of the mechanical components. The inverse kinematics approach is then employed to finely tune each joint angle, aiming to minimize the end-effector error and enhance the joint path optimization. Furthermore, energy consumption calculations are incorporated, considering dynamic characteristics such as rotational inertia and angular velocity of the joints. An energy consumption model for each joint is developed, and a comprehensive objective function is formulated to combine both error and energy consumption for optimization. Ultimately, the genetic algorithm is leveraged for joint path planning and optimization, demonstrating its effectiveness in achieving superior joint trajectories.