Rapidlyexploring Random Trees Based on Cylindrical Sampling Space In this paper, the defect of traditional fastexpanding random tree (RRT) is optimized for the path planning in threedimensional environment. Due to the randomness generated by its own nodes, the traditional RRT algorithm has the defects of the planned path twist and easy to cross the target point, and greatly differs from the ideal path, so that the time required for the robot to implement path tracking is greatly increased. In this paper, an RRT algorithm based on cylindrical sampling space is proposed. The algorithm uses the line connecting the starting point and the ending point of the path as the central axis. The maximum distance from all nodes on the path to the cylinder axis is taken as the radius value of the first cylinder. The cylinder space is used as the new node sampling space in the next path planning. Then, a new cylinder is generated by reducing the radius of the previous cylinder as the current sample sampling space. Repeat the above steps until find the last optimized path. The path generated by the traditional RRT algorithm and the path generated by the RRT optimization algorithm based on cylindrical sampling space are compared and analyzed under the given obstacle and obstaclefree environment. The simulation results show that the path generated by the RRT optimization algorithm based on the cylindrical sampling space is smoother and shorter, almost no redundant path. The path meets the actual motion requirements of the robot.

Path Planning of Redundant Series Manipulators Based on Improved RRT Algorithms Aiming at the path planning problem of the redundant manipulator with seven degrees of freedom, this paper presents a new algorithm based on improved RRT (Rapidlyexploring Random Tree algorithm), GmRRT algorithm. The new algorithm performs path planning in the joint space of the robot to improve the efficiency of the planning. The new algorithm combines the target probability offset to improve the orientation of the growth of extended random trees; uses the method of generating random points instead of single random points to improve the computational efficiency of path planning; and introduces the method of adaptive virtual gravity of targets to ensure the balance of the new algorithm in target orientation and exploration to the surrounding space. Lastly, a simulation experiment is carried out on the MATLAB platform. The results show that the improved algorithm has advantages in time and keeps the algorithm concise.