Robotic Lawn Mower Abnormal Area Marking for Predicament Avoidance
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Solution Overview
Problem
Robotic lawn mowers face challenges in accurately determining and marking abnormal areas during operation, which can lead to stranding or overturning, affecting normal operation and efficiency.
Innovation Solution
An abnormal area marking method and apparatus for robotic lawn mowers that detect predicaments, determine abnormal areas based on sensing data, classify the type of abnormality (e.g., road surface depression or bulge), and adjust operation routes accordingly to avoid risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the robotic lawn mower operates without abnormal area marking, then the operation process is simple, but the risk of stranding or overturning increases
Solution Approach 1:
The system performs preliminary detection and marking of abnormal areas before the robotic lawn mower encounters them during operation. Sensors detect road surface abnormalities in advance, the system marks these areas on a map, and plans alternative routes beforehand, preventing stranding or overturning before they occur.
Solution Approach 2:
The system continuously monitors the operation environment using sensors, compares detected road surface conditions with normal parameters, and dynamically adjusts the operation route based on the marked abnormal areas. This closed-loop feedback mechanism ensures safe operation while adapting to changing conditions.
2Productivity
If the robotic lawn mower uses basic sensing without classification, then the detection process is simple, but the mowing efficiency decreases due to unnecessary route adjustments
Solution Approach 1:
The system applies different processing and response strategies to different types of abnormal areas based on their classification. Road surface depressions, bulges, and other abnormalities are identified and marked with specific characteristics, allowing the robotic lawn mower to adjust routes selectively rather than avoiding all marked areas uniformly.
Solution Approach 2:
The system changes the parameters of road surface detection by analyzing multiple sensor data dimensions (height, slope, curvature) to classify abnormal areas. This multi-parameter analysis enables precise identification of different abnormal area types, improving both detection accuracy and route planning efficiency.
3Loss of information
If the robotic lawn mower collects sensing data for the entire operation period, then the data completeness is high, but the data processing time and energy consumption increase
Solution Approach 1:
The system extracts only the critical sensing data segments that contain abnormal area information from the entire operation period. Instead of processing all collected data, it identifies and extracts relevant portions where road surface abnormalities occur, significantly reducing processing time while maintaining data completeness for safety-critical decisions.
Solution Approach 2:
The system performs preliminary filtering and pre-processing of sensing data during collection, organizing data by time segments and spatial locations. This preliminary action prepares the data structure in advance, enabling faster processing and analysis when abnormal areas need to be identified and marked.
Data Source
AI summary
Embodiments of the present disclosure disclose an abnormal area marking method and a related apparatus. The method includes: when a predicament is detected during an operation process, determining an area of a current position as an abnormal area; obtaining a sensing data set within a specified time period during the operation process, where the sensing data set is a set of data acquired by a sensor, and the specified time period represents a safe traveling time period before the robotic lawn mower gets into the predicament; determining abnormality description information of the abnormal area based on the sensing data set; and determining a target position corresponding to the abnormal area on an operation map, and establishing an association relationship between the target position and the abnormality description information.