Abstract
This article considers a robot path planning problem originated from a robot factory inspection scenario. In the problem, the robot is in a dynamic uncertain environment, that is, a moving target object and several static and dynamic obstacles. An inertial positioning strategy is proposed to enable the robot to predict the position of the target in advance. From this predicted position, the robot path is generated by cubic spline interpolation, and then an improved particle swarm optimization algorithm with a random positive feedback factor in velocity updating optimizes the path. The experimental results show that the proposed method can successfully avoid the obstacles and reach the target object. In addition, the inertial positioning strategy and the improvement of particle swarm optimization can effectively shorten the path of the robot.
Highlights
In recent years, with the rapid development of artificial intelligence (AI) technology, intelligent mobile robots have been widely used in many industrial fields, such as intelligent production, factory inspection, cargo handling, abnormal environment detection, underwater operations, and so on.[1,2,3] In the research of intelligent mobile robots, robot path planning, a key technology, has been a classical problem that has attracted the attention of many researchers.Generally, robot path planning problems can be classified in terms of two different evaluation metrics.[4]
We mainly focus on the difference between paths with and without introducing the inertial positioning
The improved particle swarm optimization (IPSO)-Sp, particle swarm optimization (PSO)-Sp, genetic algorithm (GA)-Sp, differential evolution (DE)-Sp, ant colony optimization (ACO), fuzzy logic, artificial potential field (APF), and A* algorithms in this article are implemented on the same software and hardware platform, the programming environment is MATLAB R2018a
Summary
Robot path planning problems can be classified in terms of two different evaluation metrics.[4] One is based on environmental information, which separates the problems into static or dynamic path planning. Environmental information and the position of the target do not change over time. The position of the target or obstacle can change in dynamic path planning. The other one is based on the robot’s perception of the environmental information, which divides the problem into global and local path planning. Global path planning means that the robot can obtain global information, including information about all the obstacles, Laboratory of Intelligent Computing & Information Processing of Ministry of Education, Xiangtan University, Xiangtan, China
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