Research On Motion Planning Of Mobile Robot Based On Improved Sparrow Search Algorithm

With the rise of communication technologies such as 5G and the popularity of intelligent equipment,the application field of mobile robots is expanding,and the tasks to be performed are becoming more and more complex.In order to complete more challenging work,mobile robot motion planning is an indispensable step.Therefore,how to design an efficient and practical motion planning algorithm has become one of the urgent problems to be solved in the development of mobile robot systems.At present,most of the research still stays in the path planning stage with distance as the evaluation index,and cannot construct a complete motion planning process.The generated path cannot be directly applied to the dynamic environment or subsequent control tasks,and many cannot meet the actual environmental scene needs;at the same time,the performance of existing planning algorithms still needs to be improved.Therefore,this paper mainly focuses on the following contents:(1)A two-dimensional grid model with elevation information is established.On this basis,the mathematical model of path planning problem and sparrow search algorithm is constructed,which creates a theoretical basis for solving the subsequent path planning problem.(2)An improved sparrow search algorithm(HSSA)is proposed to solve the challenges of large computation and slow convergence speed in the global static path planning problem affected by multiple factors.Firstly,the Halton sequence is used to initialize the population to improve the quality of the initial population and ensure higher diversity.Secondly,the sparrow position update formula is simplified,and the dynamic factor is used to adjust the global and local search ability.Finally,the Lévy flight strategy is introduced to get rid of the local optimal problem in the search process.Compared with other common algorithms and advanced algorithms in 12 sets of test functions,the ability of HSSA to solve optimization problems is improved by 45.94 % compared with SSA,which verifies the effectiveness of HSSA.At the same time,a multi-factor path cost model is designed in a grid map with elevation information to verify its performance in complex and multivariate global path planning problems.The experimental results show that the optimal fitness value of the path obtained by HSSA in the regular obstacle environment and the scattered obstacle environment is reduced by 3.72 % and 4.61 %respectively,and the time spent is shortened by 43.37 % and 52.22 % respectively.It can be concluded that HSSA can find a better path in a shorter time.(3)The HSSA is combined with the improved dynamic window method to achieve trajectory optimization in a dynamic environment with unknown local information,and ensure that the generated trajectory can meet the needs of the robot control system.Aiming at the problem of insufficient forward-looking and easy to fall into local optimum in the traditional dynamic window method,this paper obtains the virtual target point through the global optimal path searched by HSSA through the path optimization strategy,and guides DWA to carry out local search to improve forward-looking.The number of iterations and the distance between obstacles are used to dynamically adjust the weight coefficient to prevent the algorithm from falling into local optimum.Adjust the initial heading angle selection method,adjust the direction at the initial stage of the algorithm,and improve the efficiency of the algorithm.The improved fusion trajectory planning algorithm can find safe trajectories in C-shaped environment,unknown obstacle environment and dynamic and unknown obstacle mixed environment.In the latter two environments,compared with DWA,the average trajectory length planned by the fusion algorithm is shortened by 2.41 % and 2.27 %,the optimization time is reduced by 17.65 %and 19.72 %,and the line speed during operation is increased by 21.59 % and 19.54 %.The three groups of indicators are better than the comparison algorithm,and the generated trajectory meets the robot kinematics requirements and can be used as a reference input for the subsequent control system.