Navigation obstacle avoidance method and device, autonomous mobile equipment and storage medium

By generating high-density point clouds and combining cost maps with rapid replanning (RRT), the problem of navigation methods in dynamic environments struggling to balance high-precision mapping and rapid obstacle avoidance is solved, enabling safe and efficient navigation for autonomous mobile devices.

CN122085995APending Publication Date: 2026-05-26SHENZHEN QIYANG SPECIAL EQUIP TECH ENG CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHENZHEN QIYANG SPECIAL EQUIP TECH ENG CO LTD
Filing Date
2026-01-21
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing navigation methods struggle to simultaneously achieve high-precision mapping, real-time pose estimation, and rapid obstacle avoidance in dynamic environments, failing to meet the demands for safe and efficient autonomous mobility.

Method used

By constructing and enhancing an initial 3D point cloud, a high-density point cloud is generated. Obstacle instance segmentation, semantic classification, and motion trajectory tracking are then performed. Combined with a cost map and global path planning, obstacle avoidance is achieved using Rapid Replanning (RRT).

Benefits of technology

It achieves high-precision obstacle perception and rapid obstacle avoidance in dynamic environments, ensuring safe and efficient navigation for autonomous mobile devices.

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Abstract

The invention discloses a navigation obstacle avoidance method and device, autonomous mobile equipment and a storage medium, and the method is applied to the autonomous mobile equipment, and comprises the steps: carrying out the enhancement of an initial three-dimensional point cloud through a preset deep learning model, and generating a high-density point cloud; obstacle instance segmentation, semantic classification and obstacle movement trajectory tracking are carried out based on the high-density point cloud, geometric features, category information and predicted positions of all obstacles are obtained, and a cost map is constructed according to the geometric features, the category information and the predicted positions; generating a global path by adopting a global path planning algorithm and a local planner based on the cost map; and moving according to the global path, quickly generating an obstacle avoidance track in the neighborhood of the current position of the autonomous mobile device based on quick replanning of RRT when detecting that a preset emergency condition is met, and switching to move according to the obstacle avoidance track. Therefore, accurate obstacle perception is realized through high-density point cloud, and global path generation and local obstacle avoidance are cooperatively completed in combination with a cost map and RRT emergency re-planning.
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