AI-Guided Solar Panel Installation Under Glare and Leveling Constraints
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Solution Overview
Problem
The installation of solar panels on rotatable structures poses challenges due to the fragile and large nature of the panels, making it difficult to ensure all panels are coplanar and leveled, and conventional computer vision techniques are affected by environmental inconsistencies like glare and illumination issues, increasing installation costs.
Innovation Solution
A solar panel handling system that combines tooling for handling panels with machine learning techniques to overcome environmental inconsistencies, using an end-of-arm assembly tool with suction cups and a linear guide assembly, and a neural network for autonomous installation, which learns from examples and generalizes to new data to improve installation efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional computer vision techniques are used for solar panel installation, then object detection can be performed, but detection accuracy deteriorates due to glare and illumination issues
Solution Approach 1:
The patent introduces an intermediary calibration target with known geometric features between the camera and the solar panels. This calibration target serves as a mediator that provides reference points for detecting camera pose and orientation, allowing the system to compensate for glare and illumination effects by relying on the stable, known geometry of the calibration target rather than directly detecting the panels under adverse lighting conditions
Solution Approach 2:
The system changes the parameters of the detection process by using a calibration target with specific geometric features and known dimensions. By transforming the detection problem from directly detecting panels under variable lighting to detecting the calibration target's stable geometric parameters, the system can then use these calibrated parameters to accurately locate panels despite illumination variations
2Adaptability or versatility
If solar panels are installed on rotatable structures, then the array can track the sun, but it becomes difficult to ensure all panels are coplanar and leveled
Solution Approach 1:
The patent applies preliminary action by using the calibration target to establish reference frames and detect camera pose before the actual panel installation and positioning occurs. The calibration process pre-establishes the geometric relationships and coordinate transformations needed to ensure accurate panel placement, coplanarity, and leveling even on rotatable structures, preventing positioning errors before they occur
Solution Approach 2:
The system implements feedback by continuously using the calibration target to monitor and detect camera pose and orientation during the installation process. This real-time feedback on the positioning system's state allows for adjustments to be made to ensure panels are installed with correct coplanarity and leveling, accommodating the rotatable structure's movement while maintaining precision
3Ease of manufacture
If manual installation methods are used, then flexibility is maintained, but installation costs increase significantly
Solution Approach 1:
The patent applies self-service by implementing an autonomous robotic system that performs installation tasks independently using computer vision and calibration-based positioning. The system serves itself by automatically detecting panel positions, calculating required movements, and executing installation actions without human intervention, thereby maintaining flexibility while dramatically reducing labor costs and improving productivity
Solution Approach 2:
The system replaces manual mechanical installation operations with an automated robotic system guided by computer vision and calibration data. The robotic arm substitutes human operators, performing picking, positioning, and placement actions automatically based on visual feedback and calibrated coordinate transformations, thereby reducing labor costs while maintaining installation flexibility
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system enhances the efficiency and reliability of solar panel installation by accurately positioning and leveling panels, even in challenging environmental conditions, reducing installation costs and improving the overall installation process.
Implementation Method 1
an end of arm assembly tool with suction cups
Implementation Method 2
a force torque transducer configured to move the clamping tool along the installation structure
Implementation Method 3
machine learning techniques to overcome environmental inconsistencies. The system can learn from examples with glare and illumination issues, and can generalize to new data during inference
Data Source
AI summary
A system and method for installing solar panels are provided. The method includes obtaining images of solar panels an installation structure during installation. The method also includes pre-processing the images by compensating for camera intrinsics or distortions, rectifying the images, and/or determining depth information. The method also includes detecting the solar panels by inputting the images into neural networks. The method also includes a first post-processing to compute a first panel pose based on an output of the neural networks. The method also includes generating control signals, based on the first panel pose, for operating a robotic controller for installing the solar panels. In some embodiments, the method also includes homography transforms to obtain a second panel pose, based on the first panel pose and visual patterns or fiducials on a solar panel, and generating the control signals further based on the second panel pose.