Research On Task Assignment And Path Planning Of Logistics Robot Based On Improved Harris Hawks Optimization

With the continuous progress of science and technology and the development of e-commerce industry,the logistics business volume and end distribution cost are also increasing.In order to improve the efficiency and reduce the cost of end distribution,robot distribution gradually replaces human distribution.In order to improve the efficiency and reduce the cost of distribution,robot distribution is gradually replacing human distribution.The main research problems are robot task assignment and path planning,and how to reduce the cost of robot task assignment and improve the path planning optimization and obstacle avoidance ability are the problems that need to be solved in the logistics industry.In this paper,the Harris Hawk algorithm is used as a method to solve the robot task assignment and path planning problem.To verify the performance of the Harris Hawk algorithm for solving the problem,a good point set strategy,a nonlinear escape factor and a Logistic-Cubic cascaded chaotic perturbation are incorporated into the algorithm for solving 23 different dimensional functions and 2 engineering problems with different constraints.The results show that the improved Harris Hawk algorithm has better performance in solving function problems and engineering problems.The testing of the performance of the Harris Hawk algorithm for solving the problems lays the foundation for solving the robot task assignment and path planning.To optimize the robot task assignment problem,an improved harris hawk algorithm is designed to solve it.Firstly,a mathematical model with fixed cost,distribution and transportation cost and power consumption cost considering the load as the optimal objective is established.Secondly,the initial solution of the algorithm is encoded so that the continuous harris hawk algorithm can solve the discrete task assignment problem.Finally,variation,insertion,evolutionary reversal and simulated annealing algorithm strategies are incorporated into the Harris Hawk Optimization to expand the search range and prevent the algorithm from falling into local optimum in order to find a better set of task assignments and reduce the distribution cost.Randomly generated data sets of 7 different task sets are used for simulation experiments,and the results show that the robot task assignment cost problem is better optimized.In order to improve the obstacle avoidance and optimization seeking capability of robot path planning,a harris hawk algorithm incorporating cubic B spline curves is designed to find a more optimized smooth route without collision.Firstly,a static environment map model is established using the raster method.Secondly,an adaptation function is constructed with the objectives of path shortest and smoothness optimal.Finally,the search method of harris hawk algorithm is improved to expand the search range of the algorithm.Meanwhile,the three-time B-sample curve smoothing strategy is added to smooth the robot path inflection points and shorten the robot driving distance.In the experimental stage,two kinds of obstacle environment models,flat type and concave type,are established using the raster method,and simulation experiments are conducted for seven groups of models.The results show that the robot’s obstacle avoidance ability and the optimization seeking ability are improved in the path planning problem.The research in this paper broadens the basic theory of robot task assignment and path planning,enriches the algorithm research of robot problems,and has certain theoretical value and practical significance for the future development of distribution robots.