Path Planning Algorithm And Verification Platform Implementation Of Mobile Robot In Indoor Environment

With the development of technology,mobile robots have been integrated into our daily lives.The path planning problem has attracted many people’s attention,and the requirements for path planning algorithms are constantly improving.Ant colony optimization is one of the intelligent algorithms,which has the advantages of strong robustness and easy integration with other algorithms,and is effective in solving path planning related problems.However,it also has drawbacks,such as easy generation of local optimal solutions,slow convergence,and low efficiency.In response to this issue,this article takes the Quanser QBot2 e mobile platform as the research object,focusing on the trajectory planning problem of mobile robots in static environments and environments containing both dynamic and static obstacles.Firstly,in a static obstacle environment,this article presents a robot path planning technique that optimizes the complex ant colony algorithm.According to the size of the map and the complexity of obstacles,the ant colony is divided into an induction layer ant colony and an optimization layer ant colony;In order to improve the convergence speed of the algorithm and find more accurate solutions,the induction layer ant colony searches for a suboptimal path through an improved greedy algorithm,and then expands the distance of each grid that the suboptimal path passes through to construct a small space.The design optimization layer ant colony uses a variable step size optimization strategy to search for the path again;After each iteration,only the pheromone concentration on the top ranked path is updated.If the ants in an iteration find a higher path quality in the path optimization,the reward pheromone strategy is used to update the path to prevent the algorithm from falling into local optimization.Simulation experiments have shown that compared with other algorithms,the optimized compound ant colony algorithm has a faster convergence speed when the path is the same,and can obtain the optimal path in environments with different complexity,verifying its effectiveness and reliability.Secondly,in a dynamic obstacle environment,this paper proposes a robot path planning technique that integrates A* ant colony and dynamic window method.For the traditional ant colony algorithm,the improved A* algorithm is used to distribute the initial pheromone unevenly to solve the problem of aimless search at the initial stage of the algorithm;Provide algorithm customized moving step size and search method to improve path optimization efficiency;Modify the heuristic function value in the transition probability function and increase the obstacle influence factor to avoid deadlock while accelerating convergence speed;Adopting a quadratic path optimization strategy to make the path shorter and smoother;Introducing a dynamic obstacle avoidance evaluation subfunction into the evaluation function of the dynamic window method to improve the safety of the path.The simulation experimental results show that the improved A* ant colony algorithm can reduce the path length by 8.75% and the number of turning points by 59% compared to traditional ant colony algorithms.After integrating the optimized dynamic window method,the mobile robot can not only ensure the global optimal path planning in a static environment,but also effectively avoid dynamic obstacles in the environment.Finally,this article establishes a path planning model for the Quanser QBot 2e mobile platform.By collecting environmental information through mobile robots,establishing an environmental model and converting it into an occupied grid graph,setting the starting point of the mobile robot,and using the improved A* ant colony algorithm to plan the route,the mobile robot can move according to the planned path.The results verify that the improved ant colony algorithm is feasible in practical environments.