School of Automation and Electrical Engineering, Chengdu Technological University, Chengdu 611730, China.
School of Automation, Chengdu University of Information Technology, Chengdu 610225, China.
Sensors (Basel). 2023 Jul 1;23(13):6082. doi: 10.3390/s23136082.
Path planning is an important part of the navigation control system of mobile robots since it plays a decisive role in whether mobile robots can realize autonomy and intelligence. The particle swarm algorithm can effectively solve the path-planning problem of a mobile robot, but the traditional particle swarm algorithm has the problems of a too-long path, poor global search ability, and local development ability. Moreover, the existence of obstacles makes the actual environment more complex, thus putting forward more stringent requirements on the environmental adaptation ability, path-planning accuracy, and path-planning efficiency of mobile robots. In this study, an artificial potential field-based particle swarm algorithm (apfrPSO) was proposed. First, the method generates robot planning paths by adjusting the inertia weight parameter and ranking the position vector of particles (rPSO), and second, the artificial potential field method is introduced. Through comparative numerical experiments with other state-of-the-art algorithms, the results show that the algorithm proposed was very competitive.
路径规划是移动机器人导航控制系统的重要组成部分，因为它对移动机器人是否能够实现自主性和智能起着决定性的作用。粒子群算法可以有效地解决移动机器人的路径规划问题，但传统的粒子群算法存在路径过长、全局搜索能力差、局部开发能力差等问题。此外，障碍物的存在使实际环境更加复杂，从而对移动机器人的环境适应能力、路径规划精度和路径规划效率提出了更高的要求。在本研究中，提出了一种基于人工势场的粒子群算法（apfrPSO）。首先，该方法通过调整惯性权重参数和对粒子的位置向量进行排序（rPSO）来生成机器人规划路径，其次，引入了人工势场方法。通过与其他最先进算法的比较数值实验，结果表明所提出的算法具有很强的竞争力。
