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

The rapid development of automatic control technology has promoted the application of mobile robots in many fields.The precise control of mobile robots cannot be achieved without the support of path planning technology.ROS mobile robot is selected as the research object to study its path planning in static and dynamic environments.In a static environment,a multi-step ant colony algorithm is proposed for the shortcomings of the ant colony algorithm when planning a path.In a dynamic environment,an improved potential field ant colony algorithm is proposed in view of the insufficient ability of ant colony algorithm to avoid dynamic obstacles.The main research contents of this thesis are as follows:(1)In a static environment,in views of the shortcomings of traditional ant colony algorithm such as long convergence time and non-optimal path searched,the improvement is performed in three aspects: setting the initial pheromone distribution optimization strategy,introducing the fallback strategy and setting the multi-step movement strategy.In addition,the cubic spline interpolation method is used to smooth the shortest path.Finally,two static operating environments with different levels of complexity are designed for simulation and comparison.The results show that the shortest path planned by the multi-step ant colony algorithm is shorter and takes less time than the traditional ant colony algorithm.The smoothed path is more conducive to the safe travel of the robot.(2)In a dynamic environment,an improved potential field ant colony algorithm is proposed and combined with the dynamic window approach.First of all,in view of the shortcomings of the traditional artificial potential field method,such as the local minimums and the target unreachable,the improvement is performed in three aspects:introducing escape force,supplementing the energy of the target point,and adding the relative speed factor between the robot and the dynamic obstacle.Then,combining the advantages of multi-step ant colony algorithm and improved artificial potential field method,an improved potential field ant colony algorithm is proposed.Finally,a collision avoidance strategy based on the dynamic window approach is designed.The robot predicts whether it will collide with dynamic obstacles and the type of collision when moves,and calls the corresponding strategy for collision avoidance.The simulation results show that the mobile robot can avoid dynamic obstacles in time and plan an optimized path.(3)A mobile robot platform is built to verify the algorithm.Experiments are made in static and dynamic environments.The results show that the improved ant colony algorithm is more effective than ant colony algorithm in solving path planning problems.