Abstract: The artificial potential field method is commonly used in small-scale path planning tasks， but it faces issues such as low path safety and poor path quality when dealing with complex obstacles. To address these problems， an adaptive artificial potential field method is proposed. Firstly， the repulsive potential field function is improved by setting a dynamic gain coefficient to ensure target accessibility in complex environments. Secondly， virtual goal points are set adaptively according to the distribution of obstacles， globally guiding the unmanned vehicle out of local minima traps and avoiding undesirable motion states such as jitter or hesitation. Lastly， the step length is adjusted adaptively based on the safe distance between the unmanned vehicle and obstacles， reducing unnecessary evasive actions by the vehicle， thus improving path quality and ensuring safety during navigation. Experimental results show that compared with other methods， the adaptive artificial potential field method reduces the average number of planning cycles by 12.82%， the average turning angle by 58.36%， and the average path length by 7.11%. These results demonstrate that the adaptive artificial potential field method has higher path safety and better path quality than other improved methods.

Key words: path planning, artificial potential field method, dynamic gain, potential field function, adaptive