Research On Robot Path Planning Strategy Based On Intelligent Optimal Algorithms

With the development of science and technology,robots have gradually penetrated into many fields of human life.In the development of robotics,the research of path planning technology is a very important and crucial aspect.The development speed of robot technology depends on the depth of research on path planning technology,which involves not only mobile robots in two-dimensional plane,but also industrial robots in three-dimensional space.Firstly,the kinematics basis of mobile robots and industrial robots is elaborated in detail,and the robots used in the last two chapters are modeled.At the same time,the structure and parameters of the robot are briefly described.Subsequently,aiming at the motion of wheeled mobile robots in environments where information is completely unknown,a multi-behavior-based local path planning algorithm for robots is proposed in this paper.The method ensures that the robot avoids static and dynamic obstacles safely and promptly by timely and reasonably switching various behaviors,and uses the improved artificial potential field method to track the target of the shift.To deal with the common obstacle avoidance U-trap problem,a trap escaping strategy along the wall was proposed.Inaddition,a speed fuzzy controller is designed to realize the robot's intelligent driving.Finally,the simulation results based on the MATLAB platform verify the effectiveness of the proposed algorithm.The comparison with other algorithms also proves the feasibility.A multi-pass sequential local search based on improved ant colony algorithm to solve the problem of path planning of manipulator for minimum consumption in the presence of obstacles is proposed.The problem is approached from a control perspective as a shortest path optimal control problem,where the configuration space is searched for path points that optimize a cost function.This method discretizes the joint space of the manipulator into spatial nodes including the initial state and the target state,and via an improved ant colony algorithm to solve the problem of combined optimization.In order to save computing time,path planning is performed using collision check while searching.Thealgorithm is implemented on a 4-DOF Dobot and a 6-DOF manipulator arm and simulation results are analyzed for cost of execution.