Optimization Design and Method for the Motion Stability of the New Hexapod Robot

This paper proposes an optimization design method for the motion stability of hexapod robots, focusing on controlling the leg joint rotations through a Central Pattern Generator (CPG) model. The optimization model incorporates both probabilistic and interval uncertainties in the CPG model, aiming to enhance the robot's motion stability in dynamic environments. A hybrid probabilistic-interval model is developed, and the optimization problem is transformed using Karush-Kuhn-Tucker (KKT) conditions and maximum entropy principles. The efficiency of the solution process is further improved through a genetic algorithm, addressing high-dimensional, nonlinear optimization challenges. The proposed approach significantly improves the performance of hexapod robots in terms of stability and adaptability, making it a valuable contribution to robotic motion control applications.