Shadow Region Mapping for Accurate Robotic Lawn Navigation
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Solution Overview
Problem
Existing automatic lawn mowers experience low positioning accuracy due to shadow regions with weak navigation signals caused by trees, buildings, and obstacles, which affect navigation and working efficiency.
Innovation Solution
A map establishing method that generates a working region map and identifies initial shadow sections, explores the shadow region to collect positioning signal quality data, and corrects the shadow region boundaries to improve navigation accuracy by determining entering and exiting directions for efficient processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the self-moving device operates in shadow regions formed by trees, buildings, and obstacles, then the device can work in complex environments, but the positioning accuracy deteriorates due to weak navigation signals
Solution Approach 1:
The shadow region is segmented into an initial shadow section (boundary portion with poor signals) and an initial shadow region (enlarged area extending into the working region). This segmentation allows the system to identify and handle different parts of the shadow region with appropriate strategies, exploring only the necessary enlarged portion rather than the entire shadow area, thus maintaining positioning accuracy while adapting to complex environments
Solution Approach 2:
The system performs preliminary exploration of the initial shadow region before actual working operations. During this exploration phase, the self-moving device collects positioning signal quality data and positioning coordinates in advance, generating a corrected shadow region map. This preliminary action ensures that when the device later needs to work in or near shadow regions, it has already gathered sufficient navigation data to maintain positioning accuracy
2Measurement precision
If the system explores and corrects shadow regions to improve positioning accuracy, then the positioning accuracy improves, but the time consumption and working efficiency deteriorate
Solution Approach 1:
The exploration and correction process is applied locally only to shadow regions rather than the entire working region. The system identifies specific shadow sections on the boundary and enlarges only those areas into initial shadow regions for exploration. This localized approach ensures positioning accuracy is improved where needed (in shadow regions) while minimizing the time loss associated with exploration, as the majority of the working region requires no such processing
Solution Approach 2:
The system performs partial exploration by enlarging the shadow section by a predetermined distance to create the initial shadow region, which is then explored. This partial action (exploring only the enlarged portion rather than the entire shadow region) balances the need for sufficient positioning data with the constraint of time consumption, avoiding excessive exploration while still gathering adequate navigation information
Data Source
AI summary
A map establishing method includes: generating a working region map and an initial shadow section, the working region map being a map of a boundary, and the initial shadow section being a part of the boundary on which a positioning signal does not meet a quality requirement; generating an initial shadow region according to the initial shadow section; exploring the initial shadow region, and collecting positioning signal quality data and positioning coordinates during exploration; and generating a corrected shadow region according to the positioning signal quality data and the positioning coordinates. A self-moving device includes: a controller; a map generator; and an exploration assembly and a shadow region corrector. An automatic working system is provided with the self-moving device. An actual range of a shadow region is first explored, and then a corrected shadow region is obtained, so that a working region map can be updated.


