Mobile device path planning method and electronic equipment

By constructing a fitness function and a particle swarm optimization algorithm with dimension-wise optimization, the problems of local optima and high computational complexity in path planning of the PSO algorithm are solved, and efficient and real-time path planning is achieved.

CN122149498AActive Publication Date: 2026-06-05DALIAN UNIV OF TECH
6 Cites 0 Cited by

Patent Information

Application Number
CN202610628577.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-05-09
Publication Date
2026-06-05
Estimated Expiration
2046-05-09

AI Technical Summary

Technical Problem

Existing particle swarm optimization (PSO) algorithms are prone to getting trapped in local optima in path planning, failing to effectively utilize the feature information of local path points, and high-dimensional path planning has high computational complexity, making it difficult to meet real-time requirements.

Method used

A fitness function is constructed, and particle velocity and position are updated through particle swarm optimization algorithm. After each update, dimension-wise optimization is performed. A coordinate descent strategy is introduced to extract high-fitness path points dimension by dimension and update the global optimal position.

Benefits of technology

Without increasing computational costs, it improves the accuracy and real-time performance of path planning, effectively utilizes the local feature information of low-fitness particles, and avoids redundant calculations in full path evaluation.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention discloses a mobile device path planning method and electronic equipment. The mobile device path planning method comprises the following steps: constructing a fitness function of mobile device path planning; initializing a particle swarm population, wherein particle positions are used for encoding a candidate path composed of a plurality of intermediate path point coordinates; performing multi-time iterative optimization, in each iteration, after the speed and the position of each time are updated, performing dimension-by-dimension optimization operation on the whole population, searching path point coordinates of candidate paths of position codes of all particles in the population dimension by dimension through the dimension-by-dimension optimization operation, and optimizing corresponding path point coordinates in a global optimal position; and after iteration is finished, determining an optimal path based on the path of global optimal position coding. According to the method, the defect that effective local features are lost due to the fact that low-fitness particles are simply eliminated in an existing PSO algorithm is overcome; and meanwhile, repeated calculation of full-path evaluation is avoided, and the real-time performance and high efficiency of algorithm operation are ensured while the optimizing precision is effectively improved.
Need to check novelty before this filing date? Find Prior Art