Biological heuristic multi-vehicle cooperative obstacle avoidance control method based on safety area division and related equipment

By constructing a spatiotemporal cost map and a shared pheromone matrix, and combining a consistency function and bimodal conflict handling, the problems of untimely risk avoidance and unsmooth control in multi-vehicle cooperative obstacle avoidance are solved, and safe and efficient transportation in complex environments is achieved.

CN122450129APending Publication Date: 2026-07-24WUHAN UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
WUHAN UNIV OF TECH
Filing Date
2026-05-19
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

Existing multi-vehicle cooperative obstacle avoidance methods suffer from problems such as untimely risk avoidance, global motion direction deviation, and unsmooth control in complex dynamic environments, which affect safety and stability.

Method used

By constructing a spatiotemporal cost map and a shared pheromone matrix, and combining a consistency function and a comprehensive heuristic evaluation function, a set of safe directions is selected. Then, by utilizing a bimodal conflict handling mechanism and a pheromone volatilization and reward mechanism, dynamic obstacle avoidance judgment and continuous control are achieved.

Benefits of technology

It improves the dynamic obstacle avoidance judgment capability, reduces the probability of deviation in the global resultant motion direction, and improves the trajectory smoothness and operational reliability in complex environments.

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Abstract

The application discloses a bio-inspired multi-vehicle cooperative obstacle avoidance control method based on safety area division and related equipment, which can be applied to the technical field of vehicle control. The application calculates the dynamic collision risk in each discrete candidate direction by constructing a space-time cost map and predicting a short-time future trajectory, then constructs a shared pheromone matrix based on the historical candidate directions of each vehicle, and constructs a consistency function and a comprehensive heuristic evaluation function, and then performs coupling calculation on the decision weight in each discrete candidate direction, and selects a safe direction set based on an adaptive safety threshold, then processes the safe direction set based on a bimodal conflict processing mechanism to obtain a target safety area, and combines the decision weight to obtain a local decision direction vector, and generates a target motion direction according to the local decision direction vector of all vehicles, so as to control the multi-vehicle operation process through the target motion direction, thereby realizing the obstacle avoidance capability in a complex environment and improving the stability of continuous control.
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