Research On Path Planning Method Based On Improved Ant Colony Algorithm

In recent years,with the continuous development and progress of robot technology,mobile robots have been more and more applied to various fields,especially applying it to home indoor services,not only to assist people or to complete some tasks independently and more convenient for people's family life.Path planning technology is a key technology in the field of mobile robots,but there are still problems such as blind path selection and slow calculation speed.Therefore,a reasonable,effective and accurate path planning algorithm is one of the important contents in the field of mobile robot research.Based on the introduction of the biological population foraging principle of the traditional ant colony algorithm,aiming at the obvious shortcomings in the ant colony algorithm search,such as the problem that the blind search of the ant from the path planning start point to the target point leads to the slow convergence speed and the phenomenon of easy to fall into local optimal solution in the early stage of ant search.The idea of adding a two-way search direction mechanism and a heuristic function with a proportional coefficient guide factor is proposed to avoid the phenomenon that the algorithm chooses to walk the loop or walk in a region that is opposite to the end direction during the search process.Secondly,according to the different selection times of different road sections,setting different pheromone weights strengthens the importance of different road sections and accelerates the algorithm convergence speed.Finally,a grid map is used to build a robot experimental environment simulation model on the matlab software platform,and algorithm simulation is performed.The simulation results verify the effectiveness of the method.The hardware platform used in this paper is the Respberry 3b master controller,the domestic Lidar Rplidar A1,the GY-85 nine-axis attitude sensor,and the Arduino teensy 3.1 motor driver board is selected,and then the improved algorithm is designed for the software part.Many physical test experiments were carried out in an indoor environment with a small area change and the improved ant colony algorithm was applied in experimental scenario 1.The time consumption was reduced by 14.38%,and the path planning length was reduced by 5.82%.In the second experimental scenario,the improved ant colony algorithm is applied,the time consumption is reduced by 8.41%,and the path planning length is reduced by 3.21%.The test results show that the robot can avoid the known obstacles to reach the target point,indicating the feasibility of the proposed improved method and achieving the expected results.