Obstacle avoidance path scheme of snake robot based on bidirectional fast expanding random tree algorithm

With the continuous innovation and development of related technologies in the field of robotics, various robots have emerged in large numbers, the application scenarios of robots are becoming more and more complex, and the requirements for robot technology are also getting higher and higher. As an indispensable part of robotics research, path planning technology has very important research and application value. As a new movable snake-shaped robot, the snake-shaped robot has always been a research hotspot in the field of snake-shaped robots due to its multi-gait movement ability and strong environmental adaptability. Among the various motion forms of the snake-shaped robot, the winding motion has the highest efficiency, and the snake-shaped robot in this motion form can also assist itself in its movement by contact with obstacles. However, there are few researches on the obstacle-assisted motion of snake-like robots, and the algorithm for solving collision dynamics is relatively simple. Therefore, it is necessary to conduct in-depth research on obstacle-assisted motion snake-like robots. The trajectory planning algorithm of the multi-NAO snake robot is studied. First, it analyzes the basic principles and implementation steps of the RRT algorithm and evaluates its advantages and disadvantages. On this basis, an improved fast-expanding random algorithm is proposed for the shortcomings of the RRT algorithm. Combining the advantages of the global search of the RRT algorithm, it is necessary to introduce an appropriate local search algorithm. Under the premise of ensuring that the path can be generated, a certain algorithm is added to improve the smoothness of the path, in order to reduce the turning time of the snake-like robot when it walks, and improve the search efficiency. Aiming at the randomness of RRT when generating random tree nodes, other algorithms are purposefully introduced to enable it to grow toward the target point. Path planning simulations verify that the improved two-way rapid expansion random tree algorithm has significantly improved search speed and search efficiency compared with Basic-RRT and Bi-RRT algorithms, with shorter average planning time and higher success rate, the generated path is smoother. Improved algorithm can not only avoid static global obstacles in a dynamic environment, but also efficiently avoid sudden dynamic obstacles. The real-time dynamic obstacle avoidance experiments based on the FANUC snake-shaped robot guiding the anthropomorphic obstacles to move and rotate in translation and the snake-shaped robot dynamically avoiding real human arms verify that the proposed algorithm can dynamically avoid regular and irregular moving obstacles online in time. When the number of obstacles is 3, the number of successful plans is 49, and the planning time is 55 ms; when the number of obstacles is 12, the number of successful plans is 40, and the planning time is 88.9 ms. Obviously, the increase in the number of obstacles leads to a decrease in the number of successful planning and an increase in planning time.