Research On Robot Path Planning Based On Improved Biogeography Optimization Algorithm

The rapid development of robot technology has prompted robots to gradually enter people's lives.Human life has made a qualitative leap.People's lives are from the most primitive manualization to mechanization,to the current intelligence,mobile robots are It has received extensive attention.Positioning,navigation and path planning are also one of the keys to solving problems for mobile robots.So far,many intelligent algorithms have been used to solve the problem of mobile robot path planning.However,when the traditional algorithm performs path planning in the grid environment,there are problems such as long search path and slow search speed,and it is difficult to avoid obstacles in real time under dynamic environment.Therefore,in response to these problems,this paper has done a mobile robot path planning method that improves the Biogeography Based Optimization(BBO)algorithm.This paper first verifies the effectiveness of the BBO algorithm in solving the robot path planning problem,and carries out experimental simulation in MATLAB.Compared with the genetic,particle swarm and simulated annealing algorithms,the experimental results are compared with the TSP problem to verify the robustness and effectiveness of the BBO algorithm.Secondly,in this paper,the simplified factor is introduced in the BBO algorithm for the known static obstacle environment,which can reduce the number of transitions in the redundant nodes and paths in the robot path planning,and effectively shorten the path length.The introduction of the midpoint smoothing mechanism can make the planned robot path smoother,reduce the unnecessary energy loss of the robot during the movement,and shorten the path length.The introduction of a multi-step search mechanism can extend the search range of the robot and improve search efficiency.The improved BBO algorithm is applied to robot path planning.The simulation results show that the improved algorithm can effectively reduce the planning path length,improve the search efficiency,reduce the turning angle,and improve the path planning performance of mobile robots.Finally,for the robot path planning problem in dynamic environment,an improved BBO optimization algorithm with divided regions and fixed time steps isdesigned.The dynamic environment is divided into multiple sub-regions,and the moving step of the fixed robot is used to carry out dynamic environment changes.Solve robot path planning in a dynamic environment.In the comprehensive consideration of multiple indicators such as timeliness,path distance and planning time of mobile robots in unknown dynamic path planning,an adaptive variable step size mobile robot path planning algorithm is proposed.The robot selects a variable step size local path according to the surrounding environment.The results of planning and simulation experiments show that in the unknown environmental path planning,the improved algorithm has further optimization in search distance,search time,and dynamic obstacle avoidance,and can effectively complete path planning.