Path planning method and cleaning robot
By combining obstacle maps and terrain elevation maps to construct a total cost map, the problem of inaccurate path planning for cleaning robots is solved, achieving more reliable and accurate path planning.
CN122450118APending Publication Date: 2026-07-24ANKER INNOVATIONS TECH CO LTD
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Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ANKER INNOVATIONS TECH CO LTD
- Filing Date
- 2025-01-23
- Publication Date
- 2026-07-24
AI Technical Summary
Technical Problem
Existing cleaning robots use planar LiDAR to build obstacle maps, which may miss low obstacles, leading to inaccurate path planning.
Method used
The system uses a first sensor to acquire an obstacle map and a second sensor to acquire a terrain elevation map. By combining the two, a total cost map is constructed. The system then integrates obstacle status and terrain elevation information to perform path planning.
Benefits of technology
It improves the reliability and accuracy of route planning and reduces driving risks.
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Smart Images
Figure CN122450118A_ABST
Abstract
The application relates to a path planning method and a cleaning robot. The method comprises the following steps: acquiring an obstacle map determined according to current frame sensing data collected by a first sensor, acquiring a terrain height map determined according to current frame sensing data collected by a second sensor, determining a total cost map according to the obstacle map and the terrain height map, and determining a planning path with a minimum total cost from a planning starting point to a planning ending point of the cleaning robot according to the total cost map; a scanning direction of the first sensor is parallel to the ground; a scanning direction of the second sensor intersects the ground; in the method, an obstacle state brought by the obstacle map and a terrain height brought by the terrain height map are fused in the obtained total cost map, multi-dimensional environmental information is fused, scene restoration of the total cost map is improved, reliability and accuracy of path planning based on the total cost map are correspondingly improved, and driving risk is reduced.
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