Path planning method and device based on swarm cooperative optimization algorithm, equipment and medium

By using a swarm collaborative optimization algorithm, a solution space for robot obstacle avoidance paths is constructed. A population is generated using chaotic mapping, and the number of path points is optimized by combining environmental factors. This solves the problem of imbalance between global search and local development in robot path planning and improves the efficiency of path planning in dynamic environments.

CN122130094APending Publication Date: 2026-06-02JILIN JIANZHU UNIVERSITY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
JILIN JIANZHU UNIVERSITY
Filing Date
2026-05-06
Publication Date
2026-06-02

AI Technical Summary

Technical Problem

In existing technologies, robot path planning algorithms suffer from an imbalance between global search and local development in dynamic and complex environments, resulting in slow convergence speed and premature convergence, making it difficult to generate high-quality feasible paths.

Method used

A swarm collaborative optimization algorithm is adopted. By constructing the solution space of the robot obstacle avoidance path, a population is generated based on a preset chaotic mapping. The environmental complexity is determined by combining the obstacle density factor, the straight path blocking factor and the obstacle distribution factor. Global search and local development are carried out, the number of path points of each individual is updated, and the optimal path is iteratively optimized.

Benefits of technology

It enhances the robot's global exploration and local development capabilities in dynamic environments, improves the adaptability and search efficiency of path planning, and generates high-quality obstacle avoidance paths.

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Abstract

This application discloses a path planning method, apparatus, device, and medium based on a swarm collaborative optimization algorithm, comprising: generating a population; determining environmental complexity based on obstacle density factor, straight path blocking factor, and obstacle distribution factor; determining the current theoretical path points based on environmental complexity; and determining the actual path points based on the current theoretical path points and the previous generation's optimal path points; in each iteration loop, performing a global search on each individual based on a first random number and swarm behavior parameters; performing local development on each individual based on a second random number, swarm behavior parameters, and the global search result; updating the individual's historical optimal solution and the current global optimal solution based on the local development results; updating the optimal path points based on the basic path points and the actual path points; and outputting the global optimal solution when the number of iterations is a preset number of rounds, thereby enhancing the global exploration and local development capabilities and improving adaptability and search efficiency in dynamic environments.
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