Research On Path Planning Strategy Of Mobile Robot Based On Improved Ant Colony Algorithm

With the development of robotics,the autonomous navigation capability of mobile robots has become the key to their strong competitiveness.Path planning technology is an important branch in the research field of autonomous navigation of mobile robots.It enables the robot to find a relatively optimal path safely and quickly in a multi-obstacle environment.Nowadays,the working environment of robots is complex and changeable,and prior information of the environment cannot be obtained even before the planning task starts.However,after the mission begins,it has to face not only static obstacles,but also some unknown dynamic obstacles.The traditional path planning algorithm has been difficult to meet the needs of the actual environment due to its limitations.Scholars at home and abroad have done a lot of improvement research on its basis,but there are still problems such as long planning time and long planning path.This paper focuses on the research of improved ant colony algorithm for mobile robot path planning problem under different environments.The main research contents are as follows:(1)The ant colony algorithm has the disadvantages of blind search,easy to fall into local optimum and slow convergence speed in global path planning.Aiming at the above problems,an improved ant colony algorithm is proposed.First,the initial pheromone is allocated unequally by constructing the initial path by being added to the Goal RRT-Connect algorithm.The problem of falling into a local optimum due to blind search during early planning in the trap map is solved.Secondly,the ant colony relay search method is added,and the self-deadlock problem of the ant taboo table is solved.Finally,the optimal path selection mechanism is optimized by the slice optimization method to obtain the global optimal path.The simulation results show that the optimal path length of the improved algorithm is shortened by 2.38% and the convergence speed is accelerated by 38.05%.The effectiveness and superiority of the improved ant colony algorithm are verified.(2)In an unknown environment,the path planning of mobile robot is prone to fall into local optima,poor real-time stability of velocity,easy to return to explore,and unable to better deal with obstacles with uniformly variable velocity.In response to these problems,an improved dynamic window approach is proposed.Firstly,The Goal RRT-Connect Ant Colony Optimization global path planning algorithm is added.In real-time path planning,the real-time alternate path planning strategy is used to overcome the local optimal solution problem.And the speed of the mobile robot is stably adjusted by adding multiple evaluation functions of the Dynamic Windows Approach algorithm.Secondly,the robot is prone to return to explore in narrow passages in real-time environment.Over-exploration methods are proposed to save walking time.Finally,two evaluation functions are added to select the optimal driving area to improve the dynamic obstacle avoidance efficiency in unknown environment.The simulation and experimental results show that the improved dynamic window approach shortens the planning path length by 10.15% and accelerates the convergence velocity by 37.07%.The effectiveness and superiority of the improved dynamic window approach in an unknown environment are verified.