Research On Dynamic Obstacle Avoidance Of Mobile Robots Based On Improved Artificial Potential Field Method

Mobile robots are the hotspot of robot research.Path planning is the prerequisite for mobile robots to perform tasks.Obstacle avoidance algorithms are the key to path planning.Many traditional obstacle avoidance algorithms will not achieve ideal results in complex environments.This paper first analyzes the limitations of the traditional artificial potential field method(APF)and proposes corresponding improvement strategy.Then the artificial potential field method is used to improve the safe area of repulsion.Finally,the algorithm is applied to the tracking of moving target points and the rounding up of multiple target points,and the proposed algorithm is simulated and verified.The main content of the paper is as follows:1)For the traditional algorithm of artificial potential field method,there will be situations where the robot cannot reach the target point.According to the reasons of different situations,the potential field algorithm is improved.Firstly,for the situation where the obstacle is close to the target point and the robot cannot reach the target point,we improve it by adding the influence factor of the distance between the target point and the robot in the repulsion field function,and change the force balance at this position to make the robot reach the target point.Secondly,after the robot falls into the local minimum,the auxiliary force and angular offset are applied to make the robot escape from the local minimum.Finally,based on the kinematics model of the robot,a simulation environment is set in MATLAB to verify the improved algorithm.The results show that the improved artificial potential field method can solve the problem of unreachable target points and local minimums in path planning,so that the robot can reach the target point successfully.2)In view of the complex environment with moving target points in the environment,this paper proposes a new improvement strategy for the artificial potential field method.In view of the complex environment with moving target points in the environment,this paper proposes a new improvement strategy for the artificial potential field method.Firstly,improve the robot's repulsive safety zone from a circle to an ellipse to ensure the safety of obstacle avoidance and increase the pass-ability in complex environments;Then,the potential field function in the dynamic environment is further improved,and the velocity and acceleration information of the moving target point is added to the gravitational field function to ensure the tracking ability of the robot to the moving target.In the repulsion field function,the information of velocity and acceleration of moving obstacles is added to provide more repulsion force and steering force for the robot,and the ability of avoiding dynamic obstacles is increased.The improved algorithm is verified by simulation in different environments,and the effectiveness of the improved algorithm is proved by data comparison.3)The improved potential field method is combined with the task allocation strategy,and applied to the capture of moving target points.In the case of seizing a single target point,the virtual target point position is calculated according to the target motion information detected by the sensor,and the robot is assigned to the corresponding virtual target point position according to the principle of the optimal total path before the task of seizing begins.For the multi-target point capture,the robots corresponding to each target point are grouped according to the cost function,and the robots in the group complete the capture process according to the task assignment in the single target capture.The simulation results show that the improved algorithm can complete the scheduled tasks in the complex environment.4)The pioneer robot software platform provided by the laboratory is used to verify the improved algorithm.First draw the environment map on Mapper3,a map creation software dedicated to pioneer robots.Then select loading robot in Mobile Sim software for simulation.The experimental results show that the improved algorithm can make the robot avoid obstacles to complete the task of tracking and seizing,which proves the feasibility and effectiveness of the improved algorithm.