Autonomous ground vehicle systems have found extensive potential and practical applications in the modern world. The development of an autonomous ground vehicle poses a significant challenge, particularly in identifying the best path plan, based on defined performance metrics such as safety margin, shortest time, and energy consumption. Various techniques for motion planning have been proposed by researchers, one of which is the use of artificial potential fields. Several authors in the past two decades have proposed various modified versions of the artificial potential field algorithms. The variations of the traditional APF approach have given an answer to prior shortcomings. This gives potential rise to a strategic survey on the improved versions of this algorithm. This study presents a review of motion planning for autonomous ground vehicles using artificial potential fields. Each article is evaluated based on criteria that involve the environment type, which may be either static or dynamic, the evaluation scenario, which may be real-time or simulated, and the method used for improving the search performance of the algorithm. All the customized designs of planning models are analyzed and evaluated. At the end, the results of the review are discussed, and future works are proposed.

翻译：自主地面车辆系统在现代世界中展现出广泛的应用潜力与实践价值。开发这类车辆面临重大挑战，尤其是在根据安全裕度、最短时间与能耗等定义性能指标确定最优路径规划方面。研究者提出了多种运动规划技术，其中人工势场法是一种重要方法。过去二十年中，多位学者提出了各种改进型人工势场算法。传统人工势场法的变体解决了此前存在的不足，这为系统梳理该算法的改进版本提供了研究契机。本文综述了基于人工势场的自主地面车辆运动规划研究，每篇文献依据以下标准进行评估：环境类型（静态或动态）、评估场景（实时或仿真）以及改善算法搜索性能的方法。所有自定义的规划模型均经过分析与评估。最后，综述结果得到讨论，并提出了未来研究方向。