Batch robot path planning and real-time obstacle avoidance method and system in unknown environment based on self-adaption and independent decision

Through prior potential path planning and real-time obstacle avoidance optimization, combined with D*LITE and APPA algorithm, the efficiency and safety problems of robot path planning in unknown environments are solved, and batch robots are efficient obstacle avoidance in dynamic environments.

CN120540289APending Publication Date: 2025-08-26HUBEI UNIV OF SCI & TECH
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Patent Information

Application Number
CN202510454340.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-11
Publication Date
2025-08-26

AI Technical Summary

Technical Problem

Efficient and secure path planning and real-time obstacle avoidance in unknown environments are still a challenge, especially in the presence of dynamic obstacles, and prior art is difficult to achieve independent real-time obstacle avoidance and path search for batch robots.

Method used

The concepts of prior potential path planning and real-time obstacle avoidance path optimization are adopted, combined with D* LITE algorithm, adaptive path planning algorithm (APPA) and unified collaborative boundary grid (UCBG) strategies, to realize independent decision-making and real-time obstacle avoidance of robots in unknown environments.

Benefits of technology

It improves the real-time and success rate of robot path planning in unknown environments, enhances adaptability to dynamic environments, and ensures that batch robots can avoid obstacles safely and efficiently.

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Abstract

The invention discloses a batch robot path planning and real-time obstacle avoidance method and system in an unknown environment based on self-adaption and independent decision, and belongs to the technical field of image processing, and the method comprises the steps: S1, carrying out the grid map modeling based on real world map data, and carrying out the potential path planning of each robot through a D * LITE algorithm; s2, on the basis of a potential path planning and self-adaptive path planning algorithm framework, each robot independently performs real-time obstacle avoidance and path finding; s3, continuously calculating and optimizing the real-time obstacle avoidance and path finding by adopting a unified collaborative boundary grid strategy until respective limiting conditions of each robot are met; and S4, completing real-time obstacle avoidance according to the corresponding path planning when respective limiting conditions of each robot are met. According to the method, independent real-time obstacle avoidance and path finding of the batch robots in the unknown environment are realized, the adaptability of the robots to various unknown dynamic environments is enhanced, and the real-time performance and the success rate of the dynamic path planning method are greatly improved.
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