ABSTRACT
Abstract
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.
Description
RELATED APPLICATION DATA
This application is based on and claims priority under 35 U.S.C. § 119 to U.S. Provisional Application No. 63/397,125, filed Aug. 11, 2022, the entire contents of which is incorporated herein by reference.
BACKGROUND
Field of the Invention
The present disclosure generally relates to a solar panel handling system, and more particularly, to a system and method for installation of solar panels on installation structures.
Discussion of the Related Art
In the discussion that follows, reference is made to certain structures and/or methods. However, the following references should not be construed as an admission that these structures and/or methods constitute prior art. Applicant expressly reserves the right to demonstrate that such structures and/or methods do not qualify as prior art against the present invention.
Installation of a photovoltaic array typically involves affixing solar panels to an installation structure. This underlying support provides attachment points for the individual solar panels, as well as assists with routing of electrical systems and, when applicable, any mechanical components. Because of the fragile nature and large dimensions of solar panels the process of affixing solar panels to an installation structure poses unique challenges. For example, in many instances the solar panels of a photovoltaic array are installed on a rotatable structure which can rotate the solar panels about an axis to enable the array to track the sun. In such instances, it is difficult to ensure that all of the solar panels in an array are coplanar and leveled relative to the axis of the rotatable structure. Additionally, the installation costs for photovoltaic array can be a considerable portion of the total build cost for the photovoltaic array. Thus, there is a need for a more efficient and reliable solar panel handling system for installing solar panels in photovoltaic array. Conventional computer vision techniques may be used when the environment is ideal. However, glare, over- or under-exposure can negatively affect object detection algorithms.
SUMMARY
Accordingly, the present invention is directed to a solar panel handling system that substantially obviates one or more of the problems due to limitations and disadvantages of the related art.
The solar panel handling system disclosed herein facilitates the installation of solar panels of a photovoltaic array on a pre-existing installation structure such as, for example, a torque tube. Installing solar panels can be made more efficient and reliable by combining tooling for handling the solar panel with components that enable mating of the solar panel to the solar panel support structure. Some embodiments use 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.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
To achieve these and other advantages and in accordance with the purpose of the present invention, as embodied and broadly described, a system for installing a solar panel may comprise an end of arm assembly tool comprising a frame and suction cups coupled to the frame, and a linear guide assembly coupled to the end of arm assembly tool, wherein the linear guide assembly includes: a linearly moveable clamping tool including an engagement member configured to engage a clamp assembly slidably coupled to an installation structure, a force torque transducer configured to move the clamping tool along the installation structure, and a junction box coupled to the frame and including a controller configured to control the force torque transducer and the suction cups, and a power supply.
In another aspect, a method of installing a solar panel may comprise engaging an end of arm assembly tool with a solar panel, the end of arm assembly tool comprising a frame and suction cups coupled to the frame, positioning the solar panel relative to an installation structure having a clamp assembly slidably coupled thereto, engaging a linear guide assembly coupled to the end of arm assembly tool with the clamp assembly, the linear guide assembly comprising a linearly moveable clamping tool including an engagement member configured to engage the clamp assembly and a force torque transducer configured to move the clamping tool along the installation structure, and actuating the force torque transducer to move the clamp assembly along the installation structure so as to engage with a side of the solar panel, thereby fixing the solar panel relative to the installation structure.
In another aspect, a method of training a neural network for autonomous solar installation, according to some embodiments. The method includes obtaining one or more images during installation. The one or more images includes an image of one or more solar panels and an installation structure. The method also includes pre-processing the one or more images including one or more of compensating for camera intrinsics or distortions, rectifying the images, and determining depth information. The method also includes detecting the one or more solar panels by inputting the one or more images into one or more neural networks that are trained to detect solar panels. The method also includes a first post-processing to compute a first panel pose based on an output of the one or more neural networks. The method also includes generating control signals, based on the first panel pose, for operating a robotic controller for installing the one or more solar panels. In some embodiments, the method also includes a second post-processing including one or more homography transforms to obtain a second panel pose for the one or more solar panels, based on the first panel pose. The second post-processing compensates or corrects for inaccuracies in the first panel pose based on visual patterns or fiducials on a solar panel. The control signals for operating the robotic controller for installing the one or more solar panels is further based on the second panel pose.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are intended to provide further explanation of the invention as claimed.
BRIEF DESCRIPTION OF THE DRAWINGS
The accompanying drawings, which are incorporated herein and form part of the specification, illustrate the present invention and, together with the description, further serve to explain principles of the invention and to enable a person skilled in the relevant arts to make and use the invention. The exemplary embodiments are best understood from the following detailed description when read in conjunction with the accompanying drawings. It is emphasized that, according to common practice, the various features of the drawings are not to scale. On the contrary, the dimensions of the various features are arbitrarily expanded or reduced for clarity. Included in the drawings are the following figures:
FIG. 1 shows a perspective view of a solar panel handling system along with a container of solar panels, in accordance with an embodiment of the present disclosure.
FIG. 2 A- 2 C show a top, front, and side view, respectively, of the solar panel handling system and container of solar panels of FIG. 1 .
FIGS. 3 A- 3 C shows a top ( FIG. 3 A ), front ( FIG. 3 C ), and side view ( FIG. 3 B ) of the solar panel handling system coupled to a single solar panel, in accordance with an embodiment of the present disclosure.
FIGS. 4 A and 4 B show perspective views of the solar panel handling system, in accordance with an embodiment of the present disclosure.
FIGS. 5 A and 5 B show a top view and a front view, respectively, of a solar panel handling system, in accordance with an embodiment of the present disclosure.
FIG. 5 C shows a side view with a clamping tool of the solar panel handling system in a retracted position, in accordance with an embodiment of the present disclosure.
FIG. 5 D shows a side view with a clamping tool in an extended or advanced position, in accordance with an embodiment of the present disclosure.
FIGS. 6 A and 6 B show perspective views of the clamping tool of a solar panel handling system in engagement with a clamp assembly coupled to an installation structure, in accordance with an embodiment of the present disclosure.
FIG. 7 A shows a top view of the clamping tool of the solar panel handling system in engagement with a clamp assembly coupled to an installation structure, in accordance with an embodiment of the present disclosure.
FIG. 7 B shows a front view of the clamping tool of the solar panel handling system in engagement with a clamp assembly coupled to an installation structure, in accordance with an embodiment of the present disclosure.
FIG. 7 C shows a side view of the clamping tool of the solar panel handling system in engagement with a clamp assembly coupled to an installation structure, in accordance with an embodiment of the present disclosure.
FIG. 7 D shows a back view of the clamping tool of the solar panel handling system in engagement with a clamp assembly coupled to an installation structure, in accordance with an embodiment of the present disclosure.
FIG. 8 schematically illustrates, in an overhead view, the solar panel handling system during the process of installing a solar panel, in accordance with an embodiment of the present disclosure.
FIG. 9 illustrates the solar panel handling system including the assembly tool coupled with an assembly moving robot using a robotic arm.
FIG. 10 illustrates the solar panel handling system having two robotic arms in which two assembly tools are coupled with an assembly moving robot using respective robotic arms.
FIGS. 11 A to 11 C illustrate a process for installing the solar panels.
FIGS. 12 A and 12 B illustrate an arrangement for a moving robot system including two module vehicles and a ground vehicle having two robotic arms.
FIG. 13 schematically illustrates installation achieved using computer vision registration.
FIG. 14 schematically illustrates an arrangement wherein the module vehicles are exchanged with new module vehicles having additional solar panels for replenishment.
FIGS. 15 - 34 provide detailed illustrations of an example configuration for a system for installing solar panels according to an embodiment of the present disclosure.
FIG. 35 A shows a block diagram of an example image processing pipeline, according to some embodiments.
FIG. 35 B shows an example rectified acquired image, according to some embodiments.
FIG. 35 C shows an example output for neural network image segmentation for the acquired rectified image shown in FIG. 35 B , according to some embodiments.
FIG. 35 D shows an example panel corner detection, according to some embodiments.
FIG. 36 shows examples for images of a road, under different lighting conditions, and segmentation masks for the images, according to some embodiments.
FIG. 37 A shows an example of a captured image that includes a solar panel and a torque tube, according to some embodiments.
FIG. 37 B shows an example of an annotated image for the captured image shown in FIG. 37 A , according to some embodiments.
FIG. 37 C shows an example prediction by the trained model, according to some embodiments.
FIG. 38 A shows an example of image classification.
FIG. 38 B shows an example of object localization for the image shown in FIG. 38 A .
FIG. 38 C shows an example of semantic segme
RELATED APPLICATION DATA
This application is based on and claims priority under 35 U.S.C. § 119 to U.S. Provisional Application No. 63/397,125, filed Aug. 11, 2022, the entire contents of which is incorporated herein by reference.
BACKGROUND
Field of the Invention
The present disclosure generally relates to a solar panel handling system, and more particularly, to a system and method for installation of solar panels on installation structures.
Discussion of the Related Art
In the discussion that follows, reference is made to certain structures and/or methods. However, the following references should not be construed as an admission that these structures and/or methods constitute prior art. Applicant expressly reserves the right to demonstrate that such structures and/or methods do not qualify as prior art against the present invention.
Installation of a photovoltaic array typically involves affixing solar panels to an installation structure. This underlying support provides attachment points for the individual solar panels, as well as assists with routing of electrical systems and, when applicable, any mechanical components. Because of the fragile nature and large dimensions of solar panels the process of affixing solar panels to an installation structure poses unique challenges. For example, in many instances the solar panels of a photovoltaic array are installed on a rotatable structure which can rotate the solar panels about an axis to enable the array to track the sun. In such instances, it is difficult to ensure that all of the solar panels in an array are coplanar and leveled relative to the axis of the rotatable structure. Additionally, the installation costs for photovoltaic array can be a considerable portion of the total build cost for the photovoltaic array. Thus, there is a need for a more efficient and reliable solar panel handling system for installing solar panels in photovoltaic array. Conventional computer vision techniques may be used when the environment is ideal. However, glare, over- or under-exposure can negatively affect object detection algorithms.
SUMMARY
Accordingly, the present invention is directed to a solar panel handling system that substantially obviates one or more of the problems due to limitations and disadvantages of the related art.
The solar panel handling system disclosed herein facilitates the installation of solar panels of a photovoltaic array on a pre-existing installation structure such as, for example, a torque tube. Installing solar panels can be made more efficient and reliable by combining tooling for handling the solar panel with components that enable mating of the solar panel to the solar panel support structure. Some embodiments use 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.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
To achieve these and other advantages and in accordance with the purpose of the present invention, as embodied and broadly described, a system for installing a solar panel may comprise an end of arm assembly tool comprising a frame and suction cups coupled to the frame, and a linear guide assembly coupled to the end of arm assembly tool, wherein the linear guide assembly includes: a linearly moveable clamping tool including an engagement member configured to engage a clamp assembly slidably coupled to an installation structure, a force torque transducer configured to move the clamping tool along the installation structure, and a junction box coupled to the frame and including a controller configured to control the force torque transducer and the suction cups, and a power supply.
In another aspect, a method of installing a solar panel may comprise engaging an end of arm assembly tool with a solar panel, the end of arm assembly tool comprising a frame and suction cups coupled to the frame, positioning the solar panel relative to an installation structure having a clamp assembly slidably coupled thereto, engaging a linear guide assembly coupled to the end of arm assembly tool with the clamp assembly, the linear guide assembly comprising a linearly moveable clamping tool including an engagement member configured to engage the clamp assembly and a force torque transducer configured to move the clamping tool along the installation structure, and actuating the force torque transducer to move the clamp assembly along the installation structure so as to engage with a side of the solar panel, thereby fixing the solar panel relative to the installation structure.
In another aspect, a method of training a neural network for autonomous solar installation, according to some embodiments. The method includes obtaining one or more images during installation. The one or more images includes an image of one or more solar panels and an installation structure. The method also includes pre-processing the one or more images including one or more of compensating for camera intrinsics or distortions, rectifying the images, and determining depth information. The method also includes detecting the one or more solar panels by inputting the one or more images into one or more neural networks that are trained to detect solar panels. The method also includes a first post-processing to compute a first panel pose based on an output of the one or more neural networks. The method also includes generating control signals, based on the first panel pose, for operating a robotic controller for installing the one or more solar panels. In some embodiments, the method also includes a second post-processing including one or more homography transforms to obtain a second panel pose for the one or more solar panels, based on the first panel pose. The second post-processing compensates or corrects for inaccuracies in the first panel pose based on visual patterns or fiducials on a solar panel. The control signals for operating the robotic controller for installing the one or more solar panels is further based on the second panel pose.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are intended to provide further explanation of the invention as claimed.
BRIEF DESCRIPTION OF THE DRAWINGS
The accompanying drawings, which are incorporated herein and form part of the specification, illustrate the present invention and, together with the description, further serve to explain principles of the invention and to enable a person skilled in the relevant arts to make and use the invention. The exemplary embodiments are best understood from the following detailed description when read in conjunction with the accompanying drawings. It is emphasized that, according to common practice, the various features of the drawings are not to scale. On the contrary, the dimensions of the various features are arbitrarily expanded or reduced for clarity. Included in the drawings are the following figures:
FIG. 1 shows a perspective view of a solar panel handling system along with a container of solar panels, in accordance with an embodiment of the present disclosure.
FIG. 2 A- 2 C show a top, front, and side view, respectively, of the solar panel handling system and container of solar panels of FIG. 1 .
FIGS. 3 A- 3 C shows a top ( FIG. 3 A ), front ( FIG. 3 C ), and side view ( FIG. 3 B ) of the solar panel handling system coupled to a single solar panel, in accordance with an embodiment of the present disclosure.
FIGS. 4 A and 4 B show perspective views of the solar panel handling system, in accordance with an embodiment of the present disclosure.
FIGS. 5 A and 5 B show a top view and a front view, respectively, of a solar panel handling system, in accordance with an embodiment of the present disclosure.
FIG. 5 C shows a side view with a clamping tool of the solar panel handling system in a retracted position, in accordance with an embodiment of the present disclosure.
FIG. 5 D shows a side view with a clamping tool in an extended or advanced position, in accordance with an embodiment of the present disclosure.
FIGS. 6 A and 6 B show perspective views of the clamping tool of a solar panel handling system in engagement with a clamp assembly coupled to an installation structure, in accordance with an embodiment of the present disclosure.
FIG. 7 A shows a top view of the clamping tool of the solar panel handling system in engagement with a clamp assembly coupled to an installation structure, in accordance with an embodiment of the present disclosure.
FIG. 7 B shows a front view of the clamping tool of the solar panel handling system in engagement with a clamp assembly coupled to an installation structure, in accordance with an embodiment of the present disclosure.
FIG. 7 C shows a side view of the clamping tool of the solar panel handling system in engagement with a clamp assembly coupled to an installation structure, in accordance with an embodiment of the present disclosure.
FIG. 7 D shows a back view of the clamping tool of the solar panel handling system in engagement with a clamp assembly coupled to an installation structure, in accordance with an embodiment of the present disclosure.
FIG. 8 schematically illustrates, in an overhead view, the solar panel handling system during the process of installing a solar panel, in accordance with an embodiment of the present disclosure.
FIG. 9 illustrates the solar panel handling system including the assembly tool coupled with an assembly moving robot using a robotic arm.
FIG. 10 illustrates the solar panel handling system having two robotic arms in which two assembly tools are coupled with an assembly moving robot using respective robotic arms.
FIGS. 11 A to 11 C illustrate a process for installing the solar panels.
FIGS. 12 A and 12 B illustrate an arrangement for a moving robot system including two module vehicles and a ground vehicle having two robotic arms.
FIG. 13 schematically illustrates installation achieved using computer vision registration.
FIG. 14 schematically illustrates an arrangement wherein the module vehicles are exchanged with new module vehicles having additional solar panels for replenishment.
FIGS. 15 - 34 provide detailed illustrations of an example configuration for a system for installing solar panels according to an embodiment of the present disclosure.
FIG. 35 A shows a block diagram of an example image processing pipeline, according to some embodiments.
FIG. 35 B shows an example rectified acquired image, according to some embodiments.
FIG. 35 C shows an example output for neural network image segmentation for the acquired rectified image shown in FIG. 35 B , according to some embodiments.
FIG. 35 D shows an example panel corner detection, according to some embodiments.
FIG. 36 shows examples for images of a road, under different lighting conditions, and segmentation masks for the images, according to some embodiments.
FIG. 37 A shows an example of a captured image that includes a solar panel and a torque tube, according to some embodiments.
FIG. 37 B shows an example of an annotated image for the captured image shown in FIG. 37 A , according to some embodiments.
FIG. 37 C shows an example prediction by the trained model, according to some embodiments.
FIG. 38 A shows an example of image classification.
FIG. 38 B shows an example of object localization for the image shown in FIG. 38 A .
FIG. 38 C shows an example of semantic segmentation, according to some embodiments.
FIG. 38 D shows an example of instance segmentation, according to some embodiments.
FIG. 39 shows an example of instance segmentation for solar panels, according to some embodiments.
FIG. 40 shows an example image processing system, according to some embodiments.
FIGS. 41 A and 41 B shows a trailer system with a coarse camera, according to some embodiments.
FIGS. 42 A and 42 B show histograms of pose error norms for the neural network with and without coarse position, according to some implementations.
FIGS. 43 A and 43 B show examples for coarse positions of solar panels using a B Mask R-CNN model, according to some embodiments.
FIG. 44 shows a system 4400 for solar panel installation, according to some embodiments.
FIG. 45 A shows a vision system for tracking trailer position, and FIG. 45 B shows an enlarged view of the vision system, according to some embodiments.
FIG. 46 A shows a vision system for module pick, and FIG. 46 B shows an enlarged view of the vision system, according to some embodiments.
FIGS. 47 A, 47 B, and 47 C show a system 4700 for distance measurement at module angle, according to some embodiments.
FIG. 48 A shows a system for laser line generation for detecting tube and clamp position, according to some embodiments.
FIG. 48 B shows an enlarged view of the laser line generation system shown in FIG. 48 A , and FIG. 48 C shows a view of laser line generation (horizontal line detects a clamp, and a vertical line detects a tube), according to some embodiments.
FIG. 49 A shows a vision system 4900 for estimating tube and clamp position, FIG. 49 B shows an enlarged view of the vision system, and FIG. 49 C shows the nut on the clamp that, when tightened, compresses the clamp to keep the panels in place, according to some embodiments.
FIG. 50 A shows a flowchart of a method for autonomous solar installation, according to some embodiments.
FIG. 50 B shows a flowchart of a method of training a neural network for autonomous solar installation, according to some embodiments.
FIG. 51 shows an example application of Hough transforms for identifying corners of a solar panel, according to some embodiments.
FIG. 52 shows an example application of segmentation, according to some embodiments.
FIG. 53 shows an example application of corner detection algorithm, according to some embodiments.
FIG. 54 shows grid intersections that can be detected using a two-pass homography algorithm, according to some embodiments.
FIG. 55 shows a schematic diagram of region-of-interest segmentation, according to some embodiments.
FIG. 56 shows multiple-data-channel capabilities of LiDAR used by some embodiments.
FIG. 57 is a schematic diagram of an example computer vision/artificial intelligence (AI) architecture for estimating six degrees-of-freedom (6DoF) pose of a solar panel, according to some embodiments.
FIG. 58 is a schematic diagram of another example computer vision/AI pipeline for estimating the 6DoF pose of a solar panel, according to some embodiments.
FIG. 59 is a schematic diagram of sensor fusion architecture for determining the 6DoF pose of a solar panel, according to some embodiments.
FIG. 60 is a schematic diagram of another sensor fusion architecture for determining the 6DoF pose of a solar panel, according to some embodiments.
FIG. 61 is a schematic diagram of a sensor fusion architecture, according to some embodiments.
FIG. 62 is a schematic diagram of a solar panel installation, according to some embodiments.
FIG. 63 shows a flowchart of a method for autonomous solar installation, according to some embodiments.
The features and advantages of the present invention will become more apparent from the detailed description set forth below when taken in conjunction with the drawings, in which like reference characters identify corresponding elements throughout. In the drawings, like reference numbers generally indicate identical, functionally similar, and/or structurally similar elements.
DETAILED DESCRIPTION
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings.
FIG. 1 shows a perspective view of a solar panel handling system along with a box of solar panels, in accordance with an embodiment of the present disclosure. The solar panel handling system may include an end of arm assembly tool 100 which can couple to individual solar panels 120 from a box of solar panels and move them to a position relative to an installation structure for installation.
The end of arm assembly tool 100 may include a frame 102 and one or more attachment devices 104 coupled to the frame 102 . Example attachment devices 104 include suction cups or other structures that can be releasably attached to the surface of the solar panel 120 and, at least in the aggregate, maintain attachment during manipulation of the solar panel 120 by the end of arm assembly tool 100 . The frame 102 may consist of several trusses 102 -A for providing structural strength and stability to the frame 102 . The frame 102 also functions as a base for the end of arm assembly tool 100 and other related components of the solar panel handling system disclosed herein.
Other related components of the solar panel handling system disclosed herein may be coupled to the frame 102 so as to fix a relative position of the components on the end of arm assembly tool 100 . One or more of the various components of the solar panel handling system may be coupled to one or more of the trusses 102 -A so as to fix a relative position of the components on the end of arm assembly tool 100 .
The attachment devices 104 are configured to reliably attach to a planar surface such, as for example, a surface of a solar panel, such as by using vacuum. In a suction cup embodiment, the suction cups can be actuated by pushing the cup against the planar surface, thereby pushing out the air from the cup and creating a vacuum seal with the planar surface. As a consequence, the planar surface adheres to the suction cup with an adhesion strength that is dependent on the size of the suction cup and the integrity of the seal with the planar surface. In some embodiments, the suction cups engage with the solar panel to create an air-tight seal, and then a vacuum pump sucks the air out of the suction cups, generating the vacuum required for the proper adhesion to the solar panel. In some embodiments, an air inlet (not shown) provides air onto the planar surface when the planar surface is sealed to the suction cup so as to deactivate the vacuum and release the planar surface from the suction cup.
The system may further include a linear guide assembly 106 coupled to the end of arm assembly tool 100 . The linear guide assembly 106 includes a linearly movable clamping tool 108 with an engagement member 108 -A configured to engage a clamp assembly coupled to an installation structure. The linear guide assembly 106 can be actuated to move the clamping tool 108 along an axis between, for example, an extended position and a retracted position. The axis of movement of the clamping tool 108 may be parallel to an axis of the installation structure. Thus, the linear guide assembly 106 can move the clamping tool 108 and the engagement member 108 -A along the installation structure.
In some embodiments, the engagement member 108 -A may include electromagnets which may be actuated to grasp a clamp assembly 602 (see FIG. 6 A, 6 B ). Alternatively, or additionally, the engagement member 108 -A may include a gripper to prevent disengagement between the clamp assembly 602 and the engagement member 108 -A when the linear guide assembly 106 is actuated to move the clamping tool relative to the installation structure as described in more detail elsewhere herein.
The linear guide assembly 106 is actuated using a force torque transducer 110 . In some embodiments, the linear guide assembly 106 and the force torque transducer 110 may form a rack and pinion structure such that the rotation of the force torque transducer 110 results in advancement or retraction of the clamping tool 108 . In some embodiments, the linear guide assembly 106 may be a hydraulic assembly including a telescoping shaft coupled to the clamping tool 108 . In such embodiments, the force torque transducer 110 may be configured in the form of a pump for pumping a hydraulic fluid. In other embodiments, the force torque transducer 110 may be configured in the form of or coupled to a liner drive motor that engages a surface of the telescoping shaft coupled to the clamping tool 108 .
In some embodiments, the linear guide assembly 106 may include an electric rod actuator to move the clamping tool 108 parallel to an axis of the installation structure.
In some embodiments, the guide assembly 106 may include a roller 606 to facilitate the movement of the clamping tool 108 along the installation structure 604 . The roller may, for example, include a bearing or other components designed for reducing friction while the clamping tool 108 moves relative to the installation structure. The roller may be coupled with a sensor, such as by a force sensor or rotation sensor, to provide feedback to a controller.
In some embodiments, the guide assemble may include a spring mechanism 608 that enables small amounts of tilting (up to 15 degrees of tilt) of the clamping tool 108 relative to the installation structure 604 . Such tilting may occur when the orientation assembly 804 tilts the end of arm assembly tool 100 relative to the installation structure 604 in order to appropriately level the solar panel.
The system may further include a junction box 112 coupled to the frame 102 . The junction box 112 may include a controller configured to control the force torque transducer 110 and the attachment devices 104 . In some embodiments, the junction box 112 may also include a power supply or a power controller for controlling the power supply to various components.
In some embodiments, the controller 112 may include a processor operationally coupled to a memory. The controller 112 may receive inputs from sensors associated with the solar panel handling system (e.g., an optical sensor or a proximity sensor 108 -B described elsewhere herein). The controller 112 may then process the received signals and output a control command for controlling one or more components (e.g., the linear guide assembly 106 , the clamping tool 108 , or the attachment devices 104 ). For example, in some embodiments, the controller 112 may receive a signal from a proximity sensor determining that the clamp assembly is approaching a trailing edge of a solar panel being installed and accordingly reduce the speed of the linear guide assembly 106 to reduce excessive forces and impacts on the solar panel.
Referring to FIG. 8 , in some embodiments, the solar panel handling system may further include an optical sensor 802 such as, for example, a camera, a photodetector, or any other optical imaging or light sensing device. The optical sensor is suitably located on the frame 102 , for example, at an outer or lower surface of an edge member indicated by position 802 -A in FIG. 8 , or at an interior location of the frame 102 that has a field of view that includes the leading edge of the solar panel, such as indicated by position 802 -B in FIG. 8 . The optical sensor may be configured to sense an orientation of the solar panel relative to the installation structure during the operation of the end of arm assembly tool. In some embodiments, the optical sensor may be configured in the form of one or more light guided levels (not shown). In such embodiments, one or more light beams (e.g., laser beams) may be projected along or parallel to the axis of the installation structure 604 from one end of the end of arm assembly tool 100 , such as first locations on the frame 102 . One or more photodetectors may be positioned at another end of the end of arm assembly tool 100 , such as second locations on the frame 102 , so as to detect the one or more laser beams. Thus, if the solar panel 120 being installed is not appropriately oriented or properly level relative to the installation structure 604 , the solar panel 102 may obstruct the some or all of one or more laser beams resulting in varying signals from the one or more photodetectors, indicating that the solar panel 120 is not appropriately oriented or properly level relative to the installation structure 604 .
In some embodiments, one or more sensors, such as optical sensors 802 , may be used to detect and recognize objects to position and control the installation with improved accuracy. The sensor(s) may be implemented together with a neural network of, for example, an artificial intelligence (AI) system. For example, a neural network can include acquiring and correcting images related to the solar panel handling system, the solar panels (both installed and to be installed), and the installation environment (both natural environment, such as topography, and installed equipment, such as structures related to the solar panel array). Also, for example, a neural network can include acquiring and correcting positional or proximity information. The corrected images and/or the corrected positional or proximity information are input into the neural network and processed to estimate movement and positioning of equipment of the solar panel handling system, such as that related to autonomous vehicles, storage vehicles, robotic equipment, and installation equipment. The estimated movement and positioning are published to a control system associated with the individual equipment of the solar panel handling system or to a master controller for the solar panel handling system as a whole.
In some embodiments, the signal from the optical sensor may be input to the controller. In some embodiments, the solar panel handling system may further include an orientation assembly 804 (see FIG. 8 ) configured to tilt the end of arm assembly tool 100 relative to the installation structure 604 . In such embodiments, the controller 112 may control the orientation in response to an input from the optical signal indicating that the solar panel being installed is not appropriately oriented or properly level relative to the installation structure, such as a torque tube 604 . It will be appreciated that while the orientation assembly 804 is shown as being coupled to the force torque transducer 110 , those of ordinary skill in the art will readily recognize other means of implementing the orientation assembly 804 .
In some embodiments, the controller 112 may also be configured to control the attachment devices 104 so as to activate or deactivate the attachment/detachment thereof. For embodiments in which the attachment devices 104 are suction cups, a vacuum can enable coupling or release of the solar panels 120 with the end of arm assembly tool 100 .
In some embodiments, the installation structure 604 may have an octagonal cross-section, as shown, e.g., in FIGS. 6 A, 6 B, and 7 A- 7 D , to form a torque tube preventing inadvertent slipping of the clamp assembly 602 . However, other cross-sectional shapes may be used, such as squared, oval, or other shape. Further, the installation structure 604 may use a circular cross-sectional shapes.
In some embodiments, the assembly tool 100 may be configured to couple with an assembly moving robot 903 (an example of which is shown in FIGS. 9 and 10 ). The assembly moving robot 903 may be configured to position the end of the arm assembly tool 100 relative to a stack or storage container 905 of solar panels, move a selected solar panel and position the selected solar panel relative to the installation structure 604 . In some embodiments, the assembly moving robot 903 may be operationally coupled with the end of arm assembly tool 100 via the force transducer 110 (or where applicable, the orientation assembly 804 ). In s
CLAIMS
Claims ( 20 )
What is claimed is:
1 . A method for autonomous solar panel installation, the method comprising:
obtaining one or more images during installation, wherein the one or more images comprises an image of one or more solar panels and an installation structure; pre-processing the one or more images including one or more of compensating for camera intrinsics or distortions, rectifying the images, and determining depth information; detecting the one or more solar panels by inputting the one or more images into one or more neural networks that are trained to detect solar panels; a first post-processing to compute a first panel pose based on an output of the one or more neural networks; and generating control signals, based on the first panel pose, for operating a robotic controller for installing the one or more solar panels.
2 . The method of claim 1 , further comprising:
a second post-processing comprising one or more homography transforms to obtain a second panel pose for the one or more solar panels, based on the first panel pose, wherein the second post-processing compensates or corrects for inaccuracies in the first panel pose based on visual patterns or fiducials on a solar panel, and wherein the control signals for operating the robotic controller for installing the one or more solar panels is further based on the second panel pose.
3 . The method of claim 2 , wherein the visual patterns comprise a grid pattern on the solar panel.
4 . The method of claim 2 , wherein the output of the one or more neural networks comprises panel segmentation, and
wherein the second post-processing comprises:
filtering out background in an image using the panel segmentation as a mask, to obtain a masked panel;
identifying grid intersections in the masked panel as corners using a corner finding algorithm;
computing a homography matrix H 1 using the corners;
determining locations of grid intersections in millimeter-space, based on H 1 ;
computing a homography matrix H 2 to transform the grid intersections in image-space to their locations in the millimeter space; and
back-projecting corners in the millimeter space to image-space, using inverse of H 2 .
5 . The method of claim 4 , wherein determining locations of the grid intersections comprises an association in H 1 -space based on Euclidean distance.
6 . The method of claim 2 , wherein the output of the one or more neural networks comprises panel segmentation, and
wherein the second post-processing comprises:
filtering out background in an image using the panel segmentation as a mask, to obtain a masked panel;
identifying grid intersections in the masked panel in millimeter space using one or more artificial intelligence techniques;
computing a homography matrix H 1 based on the grid intersections; and
back-projecting corners in the millimeter space to image-space, using inverse of H 1 .
7 . The method of claim 2 , wherein the second post-processing comprises:
estimating four corners of a solar panel in an image-space k i ; computing a homography matrix H 1 that maps k i to a millimeter-space; identifying (i) pixel locations p i of panel features in image-space, and (ii) corresponding locations q i for the pixel locations p i in millimeter-space, based on H 1 ; computing a homography matrix H 2 that maps p i to q i ; and back-projecting corners (0, 0), (H, 0), (H, W), (0, W) from millimeter-space to image-space based on H 2 â1 .
8 . The method of claim 1 , wherein the one or more neural networks is trained to output bounding boxes, segmentation, keypoints, depth and/or a 6DoF pose.
9 . The method of claim 1 , wherein the pre-processing comprises compensating for a camera distortion, rectifying the image, and/or determining depth information based on a single-baseline stereo camera, a multi-baseline stereo camera, a time-of-flight sensor, or a LiDAR sensor.
10 . The method of claim 1 , wherein the first post-processing comprises one or more computer vision algorithms for processing the output of the one or more neural networks based on invariant structures in the images to determine locations of panel keypoints.
11 . The method of claim 10 , wherein the first post-processing further comprises solving for Perspective-n-Point based on panel dimensions and panel keypoints.
12 . The method of claim 11 , wherein the panel keypoints are four corners of the panel frame.
13 . The method of claim 1 , wherein the installation structure includes a torque tube and a clamp, and
wherein the method further comprises a third post-processing comprising processing one or more images of the torque tube and/or clamp.
14 . The method of claim 13 , wherein the one or more images of the torque tube and/or clamp is obtained with a high-resolution camera and structured lighting.
15 . The method of claim 14 , wherein the structured lighting is a laser line that is approximately orthogonal or parallel with respect to the torque tube.
16 . The method of claim 13 , wherein the processing of the one or more images of the torque tube and/or clamp is performed by one or more neural networks and/or a computer vision pipeline.
17 . The method of claim 13 , further comprising locating a nut associated with the clamp by using high-intensity illumination and computer vision algorithms.
18 . The method of claim 17 , wherein high-intensity illumination is a ring light.
19 . A system for installing solar panels, the system comprising:
a camera system for obtaining one or more images during installation, wherein the one or more images comprises an image of one or more solar panels and an installation structure; one or more devices for (i) pre-processing the one or more images including one or more of compensating for camera intrinsics or distortions, rectifying the images, and determining depth information, estimating panel poses for the one or more solar panels, based on the solar panel segments; (ii) detecting the one or more solar panels based on the one or more images; and (iii) a first post-processing to compute a first panel pose based on an output of the one or more neural networks; and a controller for generating control signals, based on the first panel pose, for operating a robotic controller for installing the one or more solar panels.
20 . The system of claim 19 , further comprising:
the one or more devices for a second post-processing comprising one or more homography transforms to obtain a second panel pose for the one or more solar panels, based on the first panel pose, wherein the second post-processing compensates or corrects for inaccuracies in the first panel pose based on visual patterns or fiducials on a solar panel, and wherein the control signals for operating the robotic controller for installing the one or more solar panels is further based on the second panel pose.
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