Research On Complete Coverage Path Planning In Unknown Environments

With the combination of intelligent technology and social production and life, global manufacturing is to develop in the direction of digitization, intelligence, greening and automation. As the mainstay of intelligent manufacturing, since the robot have high intelligence, high efficiency and accuracy, and can replace human in repetitive or dangerous tasks, etc., it is popular in terms of production and life. Flexibility and intelligence of robot are also put forward higher requirements, wherein the path planning problem is an important research topic, while complete coverage path planning is one of the more special and has extensive application prospects in form. On the detailed analysis of biologically inspired neural network algorithm, the existing weakness is to be improved, and fuzzy logic algorithms for obstacle border exploration is proposed to update environment map in real time, at last the feasibility of the algorithm is verified through simulation experiments. This paper mainly completes the following content:1. Firstly study, analyze and compare commonly used several means of environment modeling methods, walking styles, algorithms and their respective advantages and disadvantages, on this basis, the algorithm of this paper is determined. According to the characteristics of the selected algorithm, the matching grid environment modeling method and reciprocating way of walking are determined.2. In the known working environment, this paper fulfills complete coverage path planning by means of the biological inspired network algorithm, and the process is expounded in detail. Analyze the deficiencies of uncertainty over the direction at an inflection point, put forward the improved method, and at last demonstrate the effectiveness of the improved measures through simulation experiment.3. In consideration of the lack of a full range of sensory information in unknown environments, this paper introduces the fuzzy logic algorithms to detect obstacle boundary, and elaborates on the process. And it integrates global planning based on biologically inspired neural networks and local planning based on fuzzy logic to achieve complete coverage in an unknown environment, and proves the feasibility of this idea through simulation experiment.