Control System of Wheel Leg Star Catalog Detection Robot Based on Hierarchical Path Planning Algorithm

Due to the combination of wheeled structure and leg capability, the wheeled configuration of legged robots is variable and the movement speed is fast, resulting in high work efficiency. Therefore, they are increasingly being used in star catalog detection. However, the path planning and motion control of wheeled legged robots are relatively complex. Given this, this study proposes a robot control system based on a hierarchical path planning algorithm. This algorithm utilizes Theta $<^>{\ast }$ algorithm, TEB algorithm, and traversal algorithm to gradually obtain the planned path of the body and wheels in layers, and inputs it into the system to achieve motion control of the robot. The experimental results indicate that, the average probability of finding available paths in incomplete obstacle maps using hierarchical path planning algorithm is greater than 90%, and its overall adaptability to the environment exceeds 80%. Compared with other algorithms, it reaches stable iteration times of about 12 times. The average length of the planned path is reduced by 35.7% and the average time is reduced by 81.9% compared to a single algorithm. In different testing environments, the proposed method has 23 path nodes, significant obstacle avoidance effect, shorter path planning length compared to other comparative methods, and a maximum body attitude angle error of about 0.015 rad, indicating good stability. In view of this, the proposed algorithm can effectively reduce the complexity of path planning for wheeled legged robots and has certain potential applications in the field of robot path planning.