Research On Path Planning Of Six Degrees Of Freedom Manipulator Based On Improved RRT~* Algorithm

On the one hand,with the proposal of "Made in China 2025" plan,in order to achieve the goal of manufacturing power,on the other hand,due to the continuous improvement of medical technology and the gradual improvement of people's quality of life,a large-scale group of elderly people appeared in society,so people put forward higher expectations for industrial robots,medical robots and life service robots,and further breakthroughs are urgently needed in the related technologies and knowledge of robotics.The research object of this paper is the robot arm control algorithm in industrial robots,and its research focus is the obstacle avoidance path planning scheme of the robot arm.An improved fast search random tree optimization algorithm(RRT*)based on the obstacle avoidance path planning of the robot arm is proposed.Firstly,in order to improve the efficiency of the inverse kinematics algorithm of manipulator,an improved quantum behavior particle swarm optimization(QPSO)is proposed.Social learning mechanism and levy flight mechanism are introduced into particle updating in the algorithm to improve the updating strategy of potential well center,so as to ensure the diversity of population and improve the searching ability.Evolutionary factors and aggregation factors are used to dynamically adjust the length of potential well to ensure that the searching range of particles is in a reasonable range and improve the convergence of the algorithm.The advantages of improved quantum behavioral particle swarm optimization(QPSO)are verified by simulation experiments and data,which lays a foundation for obstacle avoidance path planning of manipulator.Secondly,aiming at the problem of long path planning time of RRT*algorithm,some improvement measures such as adding heuristic cost function,gravitational potential function and dynamic step length are adopted,and envelope collision detection and uneven quadratic B-spline curve smoothing are used to optimize the path,so as to improve the optimization quality of the path.Finally,through the simulation experiment of environmental complexity adaptability and the simulation comparison experiment with RRT* algorithm and GB-RRT*(Goal Bias RRT*)algorithm,it is proved that the algorithm has strong environmental adaptability,high search efficiency and advantages in steps,time and path length.The algorithm also provides a reference for path planning and optimization in other fields.