Autonomous Work Machine Route Control for Sun Backlight Avoidance
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Solution Overview
Problem
Autonomous work machines face challenges in recognizing the external world due to complex image processing requirements and are unable to effectively avoid backlighting from the sun, which affects their ability to operate efficiently.
Innovation Solution
The system includes a camera-equipped autonomous work machine that uses wireless communication with a server to obtain and share sun information, allowing it to generate and implement routes that avoid backlighting by associating azimuth information with imaging date and time information and position data, thereby controlling its path to prevent backlighting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If image processing is applied to recognize external world information, then the autonomous work machine can operate autonomously, but the image processing operation becomes more complicated under various capturing conditions
Solution Approach 1:
The system performs preliminary action by obtaining sun position information (azimuth and altitude) in advance based on date and time, and pre-determining backlighting risk for each track segment before the autonomous work machine actually travels. This allows the complex backlighting avoidance calculations to be done beforehand, simplifying real-time decision-making.
Solution Approach 2:
The patent introduces an intermediary information layer (sun position data and pre-calculated backlighting risk flags) between the raw camera images and the image processing algorithm. Instead of directly processing complex images under varying lighting conditions, the system uses this intermediary data to predict and avoid backlighting scenarios, reducing the complexity of real-time image processing.
2Productivity
If the autonomous work machine travels through various tracks to work in the work area, then work coverage is improved, but backlighting from the sun may occur affecting image recognition
Solution Approach 1:
The system divides the work area into multiple track segments and pre-calculates the backlighting risk for each segment based on the sun's position at different times and locations. This preliminary assessment allows the machine to select optimal tracks that avoid backlighting conditions while still achieving comprehensive work coverage.
Solution Approach 2:
The patent implements dynamic track selection by continuously updating the backlighting risk assessment as the autonomous work machine moves through the work area and as the sun's position changes throughout the day. The system dynamically adjusts the travel plan to avoid tracks with high backlighting risk, maintaining optimal image recognition conditions while preserving work coverage.
3Productivity
If the autonomous work machine operates during sunny periods for efficiency, then work speed is improved, but backlighting conditions deteriorate image quality
Solution Approach 1:
The system obtains sun position information (azimuth and altitude angles) in advance based on the date and time of operation. By pre-calculating the backlighting risk for each track segment using this sun position data, the system can identify and select tracks that maintain good image quality even during sunny periods, thus preserving both work speed and recognition accuracy.
Data Source
Figure 1A~1B
Figure 2
Figure 3
AI summary
An autonomous work machine that includes a camera configured to capture an image of an external world, comprises an obtainment unit configured to obtain, based on date and time information, azimuth information of the sun, and imaging date and time information of the autonomous work machine, sun information that indicates one of a position and an azimuth of the sun in which backlighting can occur while the autonomous work machine is traveling in a work area, and a control unit configured to set, based on the sun information obtained by the obtainment unit, tracks by which the autonomous work machine will avoid backlighting.