Research On Mobile Robot Path Planning Based On Improved Genetic Algorithm

The mobile robot is the integration of research achievements such as sensor,machinery,electron,computer and so on,and its development is an important manifestation of the national high level of technology and the degree of industrial automation.In the study of the related technology of mobile robot,the path planning technology of mobile robot is an important part in the field of robotics research.Moreover,it is the necessary foundation and fundamental guarantee for robot to accomplish the given task,and the task of the robot is to move autonomously from a given starting point to a target point according to the surrounding environment information based on the corresponding evaluation criteria(moving time,path length,energy consumption,etc.)in a bounded workspace with obstacles.Meanwhile,the collisions between the robot and obstacles should not occur,and so as the robots.So far,most studies have focused on the single robot path planning in static environment,and the multi-robot system can effectively deal with the complicated,dynamic and parallel tasks.Compared with single robot,it has incomparable advantages,therefore the study on the path planning problem of multi-robot and dynamic environment has important significance.At present,the existing optimization algorithms have their own flaws in solving the problem of path planning.Therefore,it is a research focus to find a better algorithm in the field.As genetic algorithm(GA)has strong robustness,parallelism,and strong global search ability,in this paper,an improved genetic algorithm(IGA)is designed and applied to deal with the single robot and multi-robot systems path planning.The main researches of this paper are as follows:1.Mobile robot path planning under the global environment problem is studied.In order to deal with the problem such as slow convergence speed,local optima problem etc.of the basic genetic algorithm in solving the problem of robot path planning,the genetic algorithm is improved.An artificial potential field method is introduced to initialize population,an adaptive selection method based on the degree of population diversity is proposed,and an adaptive crossover probability and mutation probability is designed in order to improve the quality of solution of our algorithm.The simulation experiments are carried out under the grid environment,and the simulation results prove the feasibility and effectiveness of the proposed improved genetic algorithm.2.In order to solve the problem of single-mobile robot path planning in dynamic environment,the global path planning and local path planning are combined in the planning process in this paper,and according to different types of robots colliding with the dynamic obstacles,the corresponding collision avoiding strategies are proposed.Simulation results show that the proposed algorithm can effectively guide the robot in dynamic environment to realize the obstacle avoidance and get no touching optimal or suboptimal path.3.The multi-robot path planning problem under the dynamic environment is studied.The effective strategy on path coordination is proposed in order to solve the path conflict problem.The experimental result shows that the multi-robot path planning problem can be well realized by using the proposed strategy.