Path planning algorithm based on Improved Artificial Potential Field method

Abstract

The domain of research and development concerning mobile robot obstacle avoidance continues to remain an active area of interest. Artificial potential fields (APF) are a common and effective method for obstacle avoidance path planning, where the robot is guided to the target location by a simulated environmental potential field. Traditional artificial potential field methods tend to trap robots in local minima, impeding their ability to reach the goal. This research endeavours to introduce a new approach, the Improved Artificial Potential Field (IAPF) algorithm, which incorporates the A-star method in constructing the artificial potential field. This technique more effectively addresses the issue of path planning for mobile robots, thereby avoiding local minimum solutions. Through simulation experiments in different scenarios, the feasibility of the IAPF algorithm of this paper is verified. The results show that, compared with the traditional APF method, the IAPF algorithm can solve problem of local minimum and plan a sensible path.

Improved path planning method for unmanned aerial vehicles based on artificial potential field

“…Finally, the particle model agent is used to simulate and verify the above improved algorithms to achieve multiple aircraft formation and obstacle avoidance and collision avoidance issues. Zhang proposes a strategy of dynamically changing the step size of UAVs, dynamically adjusting the step size of movement based on the relative change value between directional angles, effectively avoiding the problems existing in the APF during the route planning process [8]. At the same time, for optimizing the function parameters in the APF, a genetic algorithm is added to the optimal parameters in setting the potential field function parameters [9].…”