Robot optimal energy efficiency path planning method based on improved A algorithm

Abstract

The invention belongs to the technical field of robot path planning and discloses a robot optimal energy efficiency path planning method based on an improved A algorithm. According to gradient characteristics of a three-dimensional terrain environment and geometrical properties of robot motion, an energy cost consumption model is established, and a starting point and a target point are determined;by combining the basic principle and process description of an A* algorithm, a heuristic evaluation function of the A* algorithm is determined; the neighbor node searching mode of a traditional A* algorithm, namely multi-neighborhood search, is improved, traditional 8-neighborhood extension is changed into 24-neighborhood extension; the improved A* algorithm is executed to output an optimal energy efficiency path. The turning times of paths are decreased by adopting the method, the improved strategy of multi-neighborhood search is proposed, the energy consumption for finding the optimum energy efficiency paths of the improved A* algorithm is lower compared with a traditional A* algorithm, the turning times of paths are also decreased relatively, and the method has effectiveness and reliability.

Description

technical field [0001] The invention belongs to the technical field of robot path planning, and in particular relates to a robot energy efficiency optimal path planning method based on the improved A algorithm. Background technique [0002] At present, the existing technologies commonly used in the industry are as follows: Path planning is an important branch of research fields such as intelligent transportation, communication routing, and decision management. With the advancement of science and technology and the increasing demands of human beings, its application scope is also expanding, and it has gradually become the core technology in certain professional fields. . Robot path planning is an important part of the path planning research field, and it is also a research hotspot in robotics related fields. The environment in which robots work is often complex and uneven, especially when performing certain tasks in the wild. In flat terrain, the shortest path length may b...

AI Technical Summary

Problems solved by technology

When the traditional A* algorithm expands the adjacent nodes during the search process, it only expands one layer outward with the current node as the center, that is, the eight nodes adjacent to the current node. At this time, the angle of the robot's motion direction will be limited to an integer times

The direction of action is limited, so according to the neighbor node expansion method of the traditional A* algorithm, the final path obtained by the robot path planning in the actual environment may not be optimal

[0003] In summary, the problems in the prior art are: When the traditional A* algorithm expands adjacent nodes during the search process, it only expands one layer from the center of the current node, that is, the eight nodes adjacent to the current node. At this time, the angle of the robot's motion direction will be limited to 45 degrees. Integer multiples of , that is to say, the direction of the algorithm is limited during the search process, resulting in some optimal nodes may not be selected

Therefore, the final path obtained by robot path planning in the actual environment may not be optimal, which will cause the robot to consume more energy and affect its persistent work and task completion

[0004] The difficulty and significance of solving the above technical problems: It is necessary to improve the way the traditional A* algorithm expands adjacent nodes. After the improvement, it is divided into two stages. The first stage expands 1-8 adjacent nodes, and the second stage expands 9-24 adjacent nodes. It is necessary to calculate the The heuristic information value of , takes a certain amount of time to calculate

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