Robot path planning method based on self-adaptive sparrow search algorithm

A search algorithm and path planning technology, applied in instruments, navigation calculation tools, two-dimensional position/channel control, etc., can solve local optimal problems, achieve high convergence speed, improve search performance and development performance, and increase population diversity sexual effect

Active Publication Date: 2021-03-09
GUANGZHOU UNIVERSITY
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AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is to overcome the deficiencies in the prior art above, and to provide a robot path based on the adaptive sparrow search algorithm that can solve the problem of easily falling into a local optimum in the later stage of the search, thereby improving search performance and development performance planning method

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  • Robot path planning method based on self-adaptive sparrow search algorithm
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  • Robot path planning method based on self-adaptive sparrow search algorithm

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Embodiment Construction

[0047] The present invention will be further described below in conjunction with drawings and embodiments.

[0048] Step 1: The adaptive strategy improves the mathematical model of the sparrow algorithm.

[0049] The standard SSA algorithm is to find the solution of the optimization problem by simulating the foraging process of sparrows. The principle of the algorithm is: Divide the sparrow population with an early warning mechanism into two types: discoverers and joiners. The discoverers themselves have a high degree of fitness and have a wider search range, and are responsible for guiding the population to search for food. When the sparrow detects danger, the discoverer will also lead the joiner to move to a safe area. The joiner follows the finder for better fitness. At the same time, in order to increase their own predation rate, some joiners will compete for food or forage around the finder. In particular: the identities of discoverers and joiners in the population cha...

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Abstract

The invention discloses a robot path planning method based on an adaptive sparrow search algorithm. The method comprises the following steps: S1, introducing an adaptive weight and a differential variation strategy to propose the adaptive sparrow search algorithm; and S2, planning the path of the robot by adopting an adaptive sparrow algorithm. According to the method, the capacity of the standardSSA algorithm for large-scale optimization and local precise optimization in the early stage is improved through the self-adaptive strategy, the population diversity of the SSA algorithm is improvedthrough the differential mutation strategy, the problem that the SSA algorithm is prone to falling into local optimum in the later stage of search is solved, and therefore the search performance and development performance of the algorithm are improved; and meanwhile, the algorithm has relatively high convergence rate and relatively strong optimization capability.

Description

technical field [0001] The invention relates to the technical field of robot path planning methods, in particular to a robot path planning method based on an adaptive sparrow search algorithm. Background technique [0002] The path planning problem of mobile robot is a hot field in the research direction of robot at present, and it is also a basic problem. In the path planning problem, the robot is required to find a collision-free optimal or close path from the starting point to the target point in a given environment based on certain criteria (such as the shortest time, the lowest power consumption, and the shortest distance). optimal path. [0003] In recent years, various intelligent optimization algorithms have been continuously proposed, which have attracted extensive attention from academia and industry. Among them, particle swarm optimization (PSO), genetic algorithm, firefly algorithm, beetle whisker algorithm, bat algorithm, cuckoo search algorithm, ant colony al...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/20G05D1/02
CPCG01C21/20G05D1/0212
Inventor 刘贵云舒聪彭智敏李君强
Owner GUANGZHOU UNIVERSITY
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