Research on adaptive motion planning of complex terrain for space hexapod motion robot based on reinforcement learning

Hexapod robot has wide application prospect in complex environment because of its high stability and flexibility. However, it is often difficult for traditional steering gear control methods to maintain accurate and stable motion in dynamic or irregular space environments. In this paper, a motion control method of hexapod robot based on negative feedback control of steering gear is proposed. By introducing an external sensor Angle sensor, the actual Angle of steering gear is obtained in real time, and the PWM control signal is dynamically adjusted according to the Angle deviation. This negative feedback mechanism can effectively eliminate the error accumulation of the steering gear and achieve higher precision control, so as to ensure the smooth operation of the robot in the complex space environment. The experimental results show that the proposed control method significantly improves the stability and motion accuracy of the hexapod robot in varied terrain, which lays a foundation for the performance of the hexapod robot in practical applications in the future.