Research on obstacle avoidance path planning based on improved artificial potential field method
The traditional artificial potential field(APF)method has been widely adopted in the field of path planning be-cause of its simplicity and efficiency.However,the application of this method often generates local minima.In addition,this method has limited adaptability in dynamic environments.Therefore,an improved APF method based on the simulated annealing(SA)algorithm is proposed to eliminate the above problems.The improved method combines the real-time obstacle avoidance ca-pability of the APF method and the global optimization property of the SA method.The improved method is optimized with the SA algorithm by adding a random object point near the local minima,so as to help the improved method jump out of the local minima and gradually approach the global optimum or near-optimum solution.A series of simulation experiments show that the improved method based on SA significantly reduces the cases of falling into local minima and exhibits stronger robustness and better path planning results in a variety of dynamic scenarios in comparison with the traditional APF method.In addition,the pro-posed method demonstrates good real-time performance and adaptability,so it can meet the needs of vehicles for obstacle avoidance and path planning in complex dynamic environments.
vehicle path planningAPF methodSA algorithmdynamic obstacle avoidancelocal minimarandom object point
万方数据
维普
