Planning Method And Platform Implementation Of Mobile Robots In Indoor Environment

With the rapid development of science and technology,the simple and fast of mobile robots has been reflected in all aspects,from agriculture,service industry,industry,medicine and other comprehensive penetration of people’s daily life.As one of the important bases of mobile robot motion control,path planning has been paid more and more attention.The robot establishes the environment map by detecting the obstacle information in the environment,and searches for a reasonable path from the starting point to reach the end point without collision,which is path planning.As one of the swarm intelligence algorithms,the traditional ant colony algorithm has better robustness and computational parallelism.It is easy to combine with other algorithms to improve performance,and has strong learning ability and self-organization.It is effective in solving path planning problems.However,the traditional ant colony algorithm has many shortcomings,such as easy to produce local optimal solution,slow convergence,low efficiency,only acting on the static environment.In this thesis,the traditional ant colony algorithm is improved to make up for some shortcomings of its application in path planning problems,such as low search efficiency and prone to local optimization.Then the improved algorithm is applied to the static environment and the environment with dynamic and static obstacles.The algorithm is simulated and verified in MATLAB environment and Quanser QBot 2mobile platform.The results show that the performance of the ant colony algorithm is greatly improved after the improvement of this thesis.The main work is as follows :Aiming at the defects of local optimum and slow convergence of traditional ant colony algorithm,a path planning method of mobile robot based on adaptive obstacle avoidance ant colony algorithm is proposed.The pheromone volatilization factor can be adaptively changed with the number of iterations to improve the convergence speed and path quality of the algorithm and improve the performance of the algorithm.Finally,simulation experiments are carried out to prove the effectiveness of the algorithm.Aiming at the problems of low environmental adaptability,more turning points,larger turning angle,longer path and slow convergence of traditional ant colony algorithm,a path planning method of mobile robot based on multi-strategy ant colony algorithm is proposed.Firstly,the non-uniform pheromone distribution is adopted according to the position of the current grid relative to the starting point,which makes the initial pheromone concentration of the dominant grid higher and avoids blind ant search.Secondly,the directional neighborhood expansion strategy is used to redefine the ant movement rules to further shorten the path and improve the search efficiency.After that,the angle guidance factor is used to increase the guidance of the end point,and the obstacle influence factor is added to avoid the path falling into deadlock and reduce the occurrence rate of tortuous paths.Finally,the double-layer elite ant colony strategy is used to increase the pheromone content of the best path,improve the convergence of the algorithm,and prevent the algorithm from falling into local optimum.Simulation verifies the feasibility and superiority of multi-strategy ant colony algorithm in mobile robot path planning.Aiming at the environment containing both dynamic obstacles and static obstacles,a path planning method of mobile robot based on ant colony-rolling window algorithm in dynamic environment is proposed.Firstly,ignoring the dynamic obstacles in the environment,the multi-strategy ant colony algorithm is used to plan an optimal global path for the static obstacle environment,and the path is optimized to shorten the path length and smooth path by reducing the number of turning points.Then,with the optimized path as the basic path,the robot uses the rolling window algorithm to avoid dynamic obstacles in the process of walking along this path,so as to achieve the purpose of dynamic obstacle avoidance.The experimental results show that the method can ensure that the robot can plan effective routes and avoid obstacles in real time when the environment contains dynamic and static obstacles,and ensure the safety of the robot.The path planning model of mobile robot is built on Quanser QBot 2mobile platform,and the improved ant colony algorithm is used as the planning algorithm.Firstly,the robot is controlled to collect enough environmental information in the environmental space and establish an environmental model.Then,the environmental model is transformed into an occupation grid graph.After setting the starting point,the initial planning route is obtained by using the multi-strategy ant colony algorithm.Then,the path is optimized,and unnecessary turning points in the path nodes are deleted to further smooth the path.Finally,the mobile robot is allowed to move according to the smoothed path.The results verify the feasibility of the improved ant colony algorithm in the actual mobile robot path planning problem.