Research On Robot Path Planning Based On Improved Particle Swarm Optimization Algorithm Of Neural Network

With the rapid development of modern robot technology and its wide application in many fields,some complicated and dangerous tasks can be replaced by robots.As one of the core contents of robot technology,path planning has important practical value and research significance.Therefore,the design of efficient algorithm to solve the path planning problem plays a key role in improving the robot technology.This paper presents an improved particle swarm optimization algorithm based on neural network and cubic spline curve function.The problem of premature convergence and path oscillation in traditional particle swarm optimization algorithm is improved by adding inertial weight.Collision detection is carried out by neural network algorithm,and static and dynamic obstacles(including rectangle and circle)are detected.The cubic spline curve function is used to reduce the coding dimension of particle swarm optimization(pso).The work of this paper is described as follows:(1)introduce the strategy of linear decreasing inertia weight.At the early stage,a large inertial weight is used to guarantee the global searching ability of particle swarm optimization algorithm,and the particle gradually converges to a certain region in the search space by decreasing the inertial weight.In the later stage,the precise search in this area can obtain the high precision solution.(2)the neural network algorithm is adopted in collision detection to improve the detection efficiency.For the existence of dynamic obstacles in the environment,neural network algorithm is used to dynamically detect the constraint conditions of obstacles changing with time step.(3)using cubic spline curve function to improve the path planning of classic particle swarm optimization(pso),where there are problems of coding dimension height and path unsmoothness.The path points are obtained by dividing the path curves evenly,and thecollision detection is carried out simultaneously with all obstacles to shorten the calculation time.And the fitness value of the collision path was updated.The simulation results show that the improved particle swarm optimization algorithm proposed in this paper has some improvements in convergence,smoothness,collision detection time and coding dimension,and can effectively plan smooth collision-free path quickly in both static and dynamic environments.