Mobile robot path planning method for improving probabilistic roadmap method

Abstract

The invention relates to a mobile robot path planning method for improving a probabilistic roadmap method, and relates to the field of robot navigation. On the basis of the probabilistic roadmap method (PRM), an actual path planning function of a mobile robot is achieved. According to the specific technical scheme, an actual environment map is converted into a binary map, self-adaptive sampling and Gaussian sampling are combined in a sampling stage, the number of sampling points in difficult areas (such as narrow channels) is increased, so that the binary map has stronger adaptability to complex maps; in a query phase, by changing the distance connecting the sampling points, unreasonable paths are reduced, thereby effectively increasing the planning speed. The mobile robot path planning method has a simple procedure and a high calculation speed, effectively improves the shortcomings of an original algorithm, and greatly improves the work efficiency of self-mobile robot path planning.

Description

Technical field [0001] The invention belongs to the technical field of robots, and relates to a path planning method for a mobile robot, in particular to a path planning method for a mobile robot that improves a random path diagram method. Background technique [0002] The appearance of robots has greatly liberated people and freed them from boring, tiring and dangerous work. In 1959, the first industrial robot in human history was manufactured by Engelberg, the father of robots, which gave people the first experience of the role of robots. At present, various countries and scientific research institutions around the world attach great importance to robotics, because robots are an important indicator of a country's development and technological level. Since the 20th century, autonomous mobile robots have slowly entered our field of vision. Because of their high flexibility, autonomous mobile robots have begun to be applied in more and more fields. As the core part of mobile rob...

AI Technical Summary

Problems solved by technology

In the sampling stage, due to the probabilistic nature of the sampling strategy of the PRM algorithm, the sampling probability of each area in the map is the same. Therefore, when the number of sampling points is constant, the larger the free area, the more sampling points, but it is difficult in the narrow channel area of ​​the map. Ensure that there are enough multi-sampling points, so that the planned path fails or the planned path is not optimal

In the query phase, because the optimal path needs to be queried in the path network graph, the number of paths in the path network graph determines the query time. A reasonable path greatly reduces the query efficiency, and the planned path is close to the obstacle at the turn, so the safety is not high

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