Improved Artificial Potential Field Method Applied for AUV Path Planning

Abstract

With the topics related to the intelligent AUV, control and navigation have become one of the key researching fields. This paper presents a concise and reliable path planning method for AUV based on the improved APF method. AUV can make the decision on obstacle avoidance in terms of the state of itself and the motion of obstacles. The artificial potential field (APF) method has been widely applied in static real-time path planning. In this study, we present the improved APF method to solve some inherent shortcomings, such as the local minima and the inaccessibility of the target. A distance correction factor is added to the repulsive potential field function to solve the GNRON problem. The regular hexagon-guided method is proposed to improve the local minima problem. Meanwhile, the relative velocity method about the moving objects detection and avoidance is proposed for the dynamic environment. This method considers not only the spatial location but also the magnitude and direction of the velocity of the moving objects, which can avoid dynamic obstacles in time. So the proposed path planning method is suitable for both static and dynamic environments. The virtual environment has been built, and the emulation has been in progress in MATLAB. Simulation results show that the proposed method has promising feasibility and efficiency in the AUV real-time path planning. We demonstrate the performance of the proposed method in the real environment. Experimental results show that the proposed method is capable of avoiding the obstacles efficiently and finding an optimized path.

Research Progress of Path Planning Methods for Autonomous Underwater Vehicle

“…e first is to add a distance correction factor into the repulsive potential field function to solve the GNRON problem; the second is to propose the regular hexagonguided method to solve the local minima problem; the third is to introduce the relative velocity method to avoid the dynamic obstacles in time. e improved APF can find a collision-free optimal path in both static and dynamic environments [20].…”

Intelligent Optimization Algorithm‐Based Path Planning for a Mobile Robot

“…For classical intelligent optimization algorithms, Fan et al [ 8 ] proposed the improved APF applied for autonomous underwater vehicle, which solved the problem of path target unreachable. Chang et al [ 9 ] proposed an improved DWA, which improved the success rate of paths in unknown environment by optimizing and adding evaluation functions.…”

End-to-End AUV Motion Planning Method Based on Soft Actor-Critic

“…(3) Artificial potential field method [5]: this method requires less environmental information and is convenient to operate; however, it easily falls into the local optimum problem. (4) Curve interpolation method (Bezier curve, polynomial curve, B-spline curve, Dubins curve, etc.)…”

“…The disadvantage is that the calculation is large, the real-time performance is not good, and it is difficult to find the best evaluation function. (5) Method based on AI: this mainly includes ant colony algorithm, genetic algorithm, and reinforcement learning methods (RL) [8][9][10]. The main advantages of these algorithms are that we do not need to build a complicated environmental obstacle model.…”

“…(2) Sampling-based method (rapidly exploring random tree) [ 4 ]: the method can search high-dimensional space quickly and effectively, but it also has disadvantages, such as the planning result not being optimal and the planned path not being smooth enough. (3) Artificial potential field method [ 5 ]: this method requires less environmental information and is convenient to operate; however, it easily falls into the local optimum problem. (4) Curve interpolation method (Bezier curve, polynomial curve, B-spline curve, Dubins curve, etc.)…”