Substation inspection and obstacle clearing robot control method and system based on beidou satellite

Abstract

The application relates to the technical field of robot control, and particularly discloses a substation inspection and obstacle removal robot control method and system based on Beidou satellites, which comprises the following steps: determining the target position of a robot according to a sensing signal of a Beidou satellite, and determining whether the robot deviates from a preset inspection path according to the target position; if yes, controlling the robot to make the robot perform inspection according to the preset inspection path; and if no, loading a 3D stereogram of the area where the robot is located from a Beidou cloud 3D vision system according to the target position; detecting whether there is an obstacle through an obstacle sensor and the 3D stereogram, and if yes, controlling a manipulator to remove the obstacle according to sensing data of the obstacle sensor; on the one hand, the robot is positioned through the Beidou satellite, and the positioning accuracy is high; and on the other hand, the obstacle is detected through the obstacle sensor and the 3D stereogram, so that the problem of inaccuracy in detection through the sensor is made up, and the obstacle can be accurately detected and removed.

Description

Technical Field

[0001] This invention belongs to the field of robot control technology, and in particular relates to a control method and system for a substation inspection and obstacle clearing robot based on the Beidou satellite. Background Technology

[0002] To address the challenges of increased personnel and access to harsh environments in power distribution networks due to rising inspection workloads, inspection and obstacle removal robots are widely used in inspection and obstacle removal.

[0003] Currently, inspection and obstacle removal robots mainly rely on lidar installed on the robot for positioning. For example, lidar scans the environment to obtain point clouds, and then matches the scanned point clouds with a pre-set point cloud map for accurate positioning. However, in the field of power distribution network technology, the inspection environment of the robot is not fixed. For example, the trees on the power distribution line change with their growth cycle, causing the scanned point cloud to not match the pre-set point cloud map, making it difficult to achieve accurate positioning. This results in insufficient robustness of lidar positioning, making it difficult to provide accurate positioning for robot inspection and obstacle removal. Summary of the Invention

[0004] The purpose of this invention is to provide a control method and system for a substation inspection and obstacle removal robot based on the BeiDou satellite system, aiming to solve the problems of insufficient robustness and accuracy of inspection and obstacle removal robots relying on lidar positioning.

[0005] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0006] A control method for a substation inspection and obstacle removal robot based on the BeiDou satellite system, wherein the robot is equipped with a BeiDou positioning module, obstacle sensors, and a robotic arm, and communicates with the BeiDou cloud 3D vision system, specifically including the following steps:

[0007] During the robot's inspection and obstacle removal process, the control Beidou positioning module transmits positioning signals to the Beidou satellite and receives the sensing signals returned by the Beidou satellite after responding to the positioning signals.

[0008] The target position of the robot is determined based on the sensing signals;

[0009] Determine whether the robot has deviated from the preset inspection path based on the target location;

[0010] If so, control the robot so that it performs inspections according to the preset inspection path;

[0011] If not, load a 3D stereoscopic image of the area where the robot is located from the Beidou Cloud 3D Vision System according to the target location;

[0012] The obstacle sensor on the robot and the 3D stereo image are used to detect whether there are obstacles in the robot's direction of travel;

[0013] When an obstacle is detected, the robotic arm is controlled to remove the obstacle based on the sensing data from the obstacle sensor.

[0014] As a further limitation of the technical solution of the present invention, during the robot's inspection and obstacle clearing process, the control of the Beidou positioning module to transmit positioning signals to the Beidou satellite and receive the sensing signals returned by the Beidou satellite after responding to the positioning signals specifically includes the following steps:

[0015] During the robot's inspection and obstacle removal process, the control Beidou positioning module transmits positioning signals to at least three Beidou satellites and receives sensing signals returned by at least three Beidou satellites after responding to the positioning signals.

[0016] As a further limitation of the technical solution of the present invention, determining the target position of the robot based on the sensing signal specifically includes the following steps:

[0017] The distance from each Beidou satellite to the robot is calculated based on the sensing signals of each Beidou satellite;

[0018] Three circles are generated with the BeiDou satellite as the center and the distance from at least three BeiDou satellites to the robot as the radius. The intersection of the three circles is taken as the target position of the robot.

[0019] As a further limitation of the technical solution of the present invention, loading a 3D stereoscopic image of the area where the robot is located from the Beidou Cloud 3D vision system according to the target location specifically includes the following steps:

[0020] The target location is sent to the BeiDou satellite system via BeiDou short message. The BeiDou satellite system then sends the target location to the BeiDou cloud 3D vision system. The BeiDou cloud 3D vision system generates a 3D stereoscopic scan image of the area where the robot is located based on the target location and performs visual monitoring based on the 3D stereoscopic scan image.

[0021] The system receives 3D stereoscopic scan images of the area where the robot is located from the Beidou Cloud 3D vision system.

[0022] As a further limitation of the technical solution of the present invention, the Beidou Cloud 3D Vision System includes a Beidou base station subsystem, which is used to provide a communication channel for the area within a preset range where the target location is located.

[0023] As a further limitation of the technical solution of the present invention, after determining the target position of the robot based on the sensing signal, the invention further includes the following steps:

[0024] The target location is sent to the user terminal, which then updates the network map loaded on the user terminal based on the target location.

[0025] As a further limitation of the technical solution of the present invention, the obstacle sensor includes an ultrasonic sensor, a screening camera, and an obstacle capture camera. The robot detects whether there are obstacles in its direction of travel by using the obstacle sensor on the robot and the 3D stereoscopic image. Specifically, this includes the following steps:

[0026] The ultrasonic sensor, the 3D stereoscopic scanning image, and the screening camera are used to determine whether there are obstacles within a preset distance of the robot's travel direction.

[0027] As a further limitation of the technical solution of the present invention, controlling the robotic arm to remove obstacles based on the sensing data of the obstacle sensor specifically includes the following steps:

[0028] The obstacle-capturing camera is controlled to acquire images of obstacles;

[0029] The obstacle features are identified based on the obstacle image, and the robotic arm is controlled to remove the obstacle based on the obstacle features.

[0030] As a further limitation of the technical solution of the present invention, the robot's chassis includes a chassis adjustment device, and the robotic arm is controlled to clear obstacles based on the sensing data of the obstacle sensor. Specifically, the method further includes the following steps:

[0031] The robot uses ultrasonic sensors to detect whether there are obstacles within a preset height range in its direction of travel.

[0032] If so, control the chassis adjustment device to adjust the height of the chassis.

[0033] A control system for a substation inspection and obstacle removal robot based on the BeiDou satellite system. The robot is equipped with a BeiDou positioning module, obstacle sensors, and a robotic arm. The robot communicates with the BeiDou cloud 3D vision system. Specifically, it includes the following modules:

[0034] The Beidou positioning module is used to control the Beidou positioning module to transmit positioning signals to the Beidou satellite and receive the sensing signals returned by the Beidou satellite after responding to the positioning signals during the robot's inspection and obstacle removal process.

[0035] A target location determination module is used to determine the target location of the robot based on the sensing signal;

[0036] The deviation judgment module is used to determine whether the robot has deviated from the preset inspection path based on the target position. If yes, the path control module is executed; if no, the stereo image loading module is executed.

[0037] A path control module is used to control the robot so that the robot performs inspections according to the preset inspection path.

[0038] The stereo image loading module is used to load a 3D stereo image of the area where the robot is located from the Beidou Cloud 3D vision system according to the target location.

[0039] The obstacle detection module uses the obstacle sensor on the robot and the 3D stereo image to detect whether there are obstacles in the robot's travel direction. If so, it executes the obstacle clearing control module.

[0040] The obstacle removal control module is used to control the robotic arm to remove obstacles based on the sensing data from the obstacle sensor when an obstacle is detected.

[0041] Compared with the prior art, the beneficial effects of the present invention are:

[0042] The robot in this embodiment of the invention is equipped with a Beidou positioning module, obstacle sensors, and a robotic arm. The robot communicates with the Beidou Cloud 3D vision system. During the robot's inspection and obstacle clearing process, the Beidou positioning module transmits positioning signals to the Beidou satellite and receives the sensing signals returned by the Beidou satellite after responding to the positioning signals. Based on the sensing signals, the robot's target position is determined. Based on the target position, it is determined whether the robot has deviated from the preset inspection path, and the robot is controlled to inspect along the preset path. If not, a 3D stereoscopic image of the robot's location is loaded from the Beidou Cloud 3D vision system based on the target position. The obstacle sensors on the robot and the 3D stereoscopic image detect whether there are obstacles in the robot's direction of travel. When an obstacle is detected, the robotic arm is controlled to clear the obstacle based on the sensor data. This achieves robot positioning via Beidou satellite, improving robot positioning accuracy. Furthermore, the obstacle detection using the obstacle sensors and the 3D stereoscopic image provided by the Beidou Cloud 3D vision system compensates for the inaccuracy of sensors on the robot, enabling accurate obstacle detection and clearing. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention.

[0044] Figure 1 A flowchart of a control method for a substation inspection and obstacle removal robot based on the BeiDou satellite system, according to an embodiment of the present invention, is shown.

[0045] Figure 2 The diagram illustrates the principle of robot positioning via BeiDou satellite in this embodiment.

[0046] Figure 3 A schematic diagram illustrating the process of locating the robot using the BeiDou satellite in this embodiment is shown.

[0047] Figure 4 This embodiment shows the application architecture diagram of Beidou Cloud 3D Vision Cloud;

[0048] Figure 5 This embodiment shows a schematic diagram of the robot's obstacle detection and clearance process.

[0049] Figure 6 The diagram illustrates the application architecture of a substation inspection and obstacle removal robot control system based on the BeiDou satellite system, according to an embodiment of the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0051] Figure 1 A flowchart of a substation inspection and obstacle removal robot control method based on the BeiDou satellite system provided in this embodiment of the invention is shown. Specifically, the substation inspection and obstacle removal robot control method based on the BeiDou satellite system of this invention includes the following steps:

[0052] Step 101: During the robot's inspection and obstacle clearing process, control the Beidou positioning module to send positioning signals to the Beidou satellite and receive the sensing signals returned by the Beidou satellite after responding to the positioning signals.

[0053] The robot in this embodiment can be an inspection robot used to walk on the ground in a power distribution network. The robot is equipped with a robotic arm for clearing obstacles. It is also equipped with a Beidou positioning module and obstacle sensors. The obstacle sensors can include ultrasonic sensors and cameras, and the number of cameras can be one or more.

[0054] During the robot's inspection and obstacle removal process, the robot's central processing unit can control the BeiDou positioning module to transmit positioning signals to at least three BeiDou satellites and receive the sensing signals returned by at least three BeiDou satellites after responding to the positioning signals. For example, the robot can be equipped with a servo motor, which can be used to sequentially align the antenna of the BeiDou positioning module with the direction of the three BeiDou satellites to transmit positioning signals.

[0055] Step 102: Determine the target position of the robot based on the sensing signals.

[0056] In one embodiment, the distance from each BeiDou satellite to the robot can be calculated based on the sensing signal of each BeiDou satellite. Three circles are generated with the BeiDou satellite as the center and the distances from at least three BeiDou satellites to the robot as the radii. The intersection of the three circles is taken as the target position of the robot. The distance between the robot and the BeiDou satellite can be calculated by the transmission time of the wireless signal between the BeiDou satellite and the robot and the transmission speed of the wireless signal, which will not be described in detail here.

[0057] like Figure 2 As shown, robot A first calculates the distance R1 between itself and the first Beidou satellite using sensing signals, and then calculates the distance R2 between itself and the second Beidou satellite using sensing signals. Then, it generates two circles with the first and second satellites as centers, and distances R1 and R2 as radii respectively, intersecting at points A and B. Point A is located on Earth, and point B is located in space (excluding the satellite). Next, it calculates the distance R3 between itself and the third satellite, and draws a circle with the third satellite as the center and radius R3, intersecting at point A. The latitude and longitude coordinates of point A on Earth are then the target location of robot A.

[0058] Figure 3 The process for a robot to obtain a target location using at least three satellites involves first calculating the first distance between the robot and the first satellite, then calculating the second distance between the robot and the second satellite. The robot's central processing unit initiates fuzzy positioning, determining two intersection points through the positioning module. One intersection point represents the robot's position, while the other is in outer space. The distance between the robot and the third satellite is then calculated. A circle is generated based on this distance, intersecting one of the two previously identified intersection points. This circle represents the coordinates of robot A. Figure 2 In the diagram, circles with radii R1 and R2 intersect at two points, B and A. A circle with radius R3 intersects at point A. The latitude and longitude coordinates of point A on Earth are the target location of robot A. After the target location is confirmed, the latitude and longitude information is sent to the control terminal via the BeiDou short message server. Relevant personnel can obtain the robot's current target location through the control terminal.

[0059] Furthermore, the target location is sent to the user terminal, which updates the network map loaded on the user terminal based on the target location. Specifically, the user terminal can display the network map, which can be a BeiDou application map, and show the target location of the robot in the network map. When the target location changes, the network map can be updated, realizing the display of a real-time network map of the area where the robot is located on the user terminal, and displaying the robot's target location on the network map, thus realizing visual monitoring of the robot's position.

[0060] Step 103: Determine whether the robot has deviated from the preset inspection path based on the target location.

[0061] In one embodiment, an inspection path can be pre-generated based on the starting and ending points of the inspection and obstacle removal and the map. The inspection path includes the latitude and longitude information of multiple points. After obtaining the latitude and longitude information of the robot's current target position, the distance between the current latitude and longitude position and the preset inspection path can be calculated. When the distance is greater than the distance threshold, it is determined that the robot has deviated from the inspection path, and step 104 can be executed. Otherwise, it is determined that the robot has not deviated from the preset inspection path, and the robot can be controlled to continue to travel in the current direction and step 105 can be executed.

[0062] Step 104: Control the robot so that it performs inspections according to the preset inspection path.

[0063] When it is determined that the robot deviates from the inspection path, the robot can be controlled to correct its direction of travel, so that the robot can travel and inspect according to the inspection path. The specific method of correcting the path can refer to the method of robot deviating from the preset path in the prior art. This embodiment does not limit this.

[0064] Step 105: Load the 3D stereo image of the area where the robot is located from the Beidou Cloud 3D Vision System according to the target location.

[0065] In one embodiment, after the target location of the robot is determined, the target location can be sent to the BeiDou satellite system via BeiDou short message. The BeiDou satellite system then sends the target location to the BeiDou Cloud 3D Vision System. The BeiDou Cloud 3D Vision System generates a 3D stereoscopic scan image of the area where the robot is located based on the target location, performs visual monitoring based on the 3D stereoscopic scan image, and receives the 3D stereoscopic scan image of the area where the robot is located from the BeiDou Cloud 3D Vision System.

[0066] like Figure 4The diagram shows the application architecture of the Beidou Cloud 3D Vision System. The Beidou Cloud 3D Vision System includes a power base station subsystem 10, a data sharing subsystem 20, a Beidou base station subsystem 30, and an inspection subsystem 40. The power base station subsystem 10 can be a system within the power system used to receive data from the Beidou satellite system and generate 3D stereoscopic scanning images. The Beidou base station subsystem 30 can be a ground-based communication system corresponding to the Beidou satellite system, and it can be configured to operate in areas with weak conventional communication signals, allowing the robot to communicate via the Beidou communication system in these areas. The data sharing subsystem 20 can perform anomaly detection based on the 3D stereoscopic scanning images generated by the power base station subsystem 10. For example, it can input the 3D stereoscopic scanning images into an obstacle detection model to determine if obstacles exist on the robot's path. The inspection subsystem 40 can be a drone inspection subsystem, a manual inspection subsystem, or a robot inspection subsystem. The drone inspection subsystem and the robot inspection subsystem can upload data, videos, images, etc. collected by various cameras and sensors to the Beidou base station subsystem 30 through a wireless image transmission module, so that they can be sent to the power base station subsystem 10 through the Beidou base station subsystem 30 to solve the problem of data transmission failure in areas with low conventional communication signal strength.

[0067] In the power base station subsystem 10, the short message server can send the received target location to the power base station, which then sends the target location to the BeiDou satellite system. The BeiDou satellite system uses a satellite 3D laser scanning subsystem to perform laser scanning on the area where the robot is located, obtaining a 3D stereoscopic scan image of the area where the robot is located. This 3D stereoscopic scan image can be displayed for visual monitoring. Simultaneously, the 3D stereoscopic scan image is sent to the integrated server, which then sends it to the data analysis subsystem for analysis. The data is shared and the analysis results are stored in the cloud. When the data analysis subsystem detects an anomaly in the area where the robot is located based on the 3D stereoscopic scan image, such as detecting an obstacle on the robot's inspection path, it can send the alarm information back to the integrated server via the feedback subsystem. The power base station subsystem 10 can then send the alarm information to the robot via the BeiDou satellite system or the short message server using the short message service.

[0068] In the Beidou base station subsystem 30, the baseband chip synthesizes and decodes baseband signals, the video chip can usually be deployed in areas with low communication signal strength to provide a communication channel within a preset range, that is, to provide the ability to output baseband signals and positioning data, and the SoC chip can collect various data.

[0069] In this embodiment, the robot can send the target location to the Beidou Cloud 3D Vision System via short message service. The Beidou Cloud 3D Vision System can perform satellite 3D laser scanning on the area within a preset range where the target location is located, obtain a 3D stereoscopic scan image of the area where the target location is located, and send it to the robot.

[0070] Step 106: Detect whether there are obstacles in the robot's direction of travel using obstacle sensors and 3D stereoscopic images on the robot.

[0071] In one embodiment, the obstacle sensor on the robot includes an ultrasonic sensor, a screening camera, and an obstacle capture camera. The ultrasonic sensor, 3D stereoscopic scanning image, and screening camera can determine whether there is an obstacle within a preset distance in the robot's travel direction. If there is, step 107 is executed; otherwise, the robot continues to be controlled to travel along the preset inspection path.

[0072] Step 107: When an obstacle is detected, control the robotic arm to remove the obstacle based on the sensing data from the obstacle sensor.

[0073] In one embodiment, an obstacle-capturing camera can be controlled to capture obstacle images, obstacle features can be identified based on the obstacle images, and a robotic arm can be controlled to remove the obstacle based on the obstacle features.

[0074] Among them, the ultrasonic sensor can be set at the front of the robot to detect whether there are obstacles within a preset distance in front of the robot. The screening camera is used to identify whether there are obstacles through the collected images. The obstacle detection camera is used to identify the obstacles so as to control the robot arm by detecting the obstacles.

[0075] like Figure 5 The diagram shows a flowchart of the robot controlling its robotic arm to clear obstacles after detection. Figure 5 As shown, the CPU (Central Processing Unit) first controls the ultrasonic sensor to emit ultrasonic waves to detect whether there are obstacles in front of the robot. If so, the CPU obtains obstacle information (such as obstacle location, distance, etc.). The CPU controls the screening camera to collect images and loads 3D stereoscopic scan images of the robot's location from the Beidou Cloud 3D vision system. The obstacle information from the ultrasonic sensor, the 3D stereoscopic scan images, and the images collected by the screening camera are fused to further determine whether there are obstacles. By fusing multi-dimensional information to determine whether there are obstacles, the accuracy of obstacle detection is improved. The obstacle capture camera collects images to identify the size and position of obstacles through images, so as to determine the corresponding obstacle removal plan.

[0076] For example, the size of an obstacle is determined by the image captured by the obstacle capture camera, and the opening and closing of the mechanical gripper on the robotic arm is controlled. The positioning module of the mechanical gripper then provides the specific location of the obstacle to the central processing unit, which controls the mechanical gripper to accurately grasp the obstacle.

[0077] The robotic gripper is an important component of the robot. The robot is equipped with two robotic arms and a robotic gripper at the front end to perform obstacle clearing. When clearing obstacles, the ultrasonic sensors on the robot can quickly determine the location of the obstacle in front. The central processing unit sends an obstacle clearing signal to the host, which controls the motor to rotate at high speed to turn the robot and controls the dual-axis motor to rotate the robot laterally. When the robot reaches the obstacle, the obstacle-capturing camera locks the obstacle's specific position relative to the robot. The central processing unit sends a signal to control the relevant motors so that the robotic gripper aligns with the obstacle. The robotic gripper closes and hugs the obstacle, moving the obstacle into the tipping bucket on the robot.

[0078] The robotic arm structure of the robot in this embodiment can be any mechanism capable of grasping and moving obstacles, or it can be a mechanism that switches obstacles using lasers, blades, etc. (such as a mechanism for clearing tree obstacles). This embodiment of the invention does not limit the specific structure of the robotic arm, nor does it limit the way the robot clears obstacles.

[0079] In another embodiment, an ultrasonic sensor can be used to detect whether there are obstacles within a preset height range in the robot's direction of travel. If so, the chassis adjustment device is controlled to adjust the height of the chassis. Specifically, the robot is equipped with a chassis adjustment device, such as a shock-absorbing airbag. The ultrasonic sensor can detect the unevenness of the road surface, and the chassis adjustment device can be used to adjust the distance between the robot's chassis and the ground. For example, the ultrasonic sensor emits sound wave signals according to a preset cycle. When the sound signal is blocked by an object less than 15cm in height within 10cm in front of it, the robot is controlled to stop moving. The chassis adjustment device raises the chassis height to more than 15cm before the robot continues to move forward. When encountering complex inspection environments, the chassis adjustment device can improve the stability of the robot's inspection and enable the robot to operate stably.

[0080] The robot in this embodiment of the invention is equipped with a Beidou positioning module, obstacle sensors, and a robotic arm. The robot communicates with the Beidou Cloud 3D vision system. During the robot's inspection and obstacle clearing process, the Beidou positioning module transmits positioning signals to the Beidou satellite and receives the sensing signals returned by the Beidou satellite after responding to the positioning signals. Based on the sensing signals, the robot's target position is determined. Based on the target position, it is determined whether the robot has deviated from the preset inspection path, and the robot is controlled to inspect along the preset path. If not, a 3D stereoscopic image of the robot's location is loaded from the Beidou Cloud 3D vision system based on the target position. The obstacle sensors on the robot and the 3D stereoscopic image detect whether there are obstacles in the robot's direction of travel. When an obstacle is detected, the robotic arm is controlled to clear the obstacle based on the sensor data. This achieves robot positioning via Beidou satellite, improving robot positioning accuracy. Furthermore, the obstacle detection using the obstacle sensors and the 3D stereoscopic image provided by the Beidou Cloud 3D vision system compensates for the inaccuracy of sensors on the robot, enabling accurate obstacle detection and clearing.

[0081] Figure 6 This paper illustrates the application architecture of a substation inspection and obstacle removal robot control system based on the BeiDou satellite system provided in an embodiment of the present invention. The robot is equipped with a BeiDou positioning module, obstacle sensors, and a robotic arm. The robot communicates with the BeiDou Cloud 3D vision system. The substation inspection and obstacle removal robot control system based on the BeiDou satellite system in this embodiment specifically includes the following modules:

[0082] The Beidou positioning module 601 is used to control the Beidou positioning module to transmit positioning signals to the Beidou satellite and receive the sensing signals returned by the Beidou satellite after responding to the positioning signals during the robot's inspection and obstacle clearing process.

[0083] The target position determination module 602 is used to determine the target position of the robot based on the sensing signal;

[0084] The deviation judgment module 603 is used to determine whether the robot deviates from the preset inspection path based on the target position. If yes, the path control module 604 is executed; if no, the stereo image loading module 605 is executed.

[0085] The path control module 604 is used to control the robot so that the robot performs inspections according to the preset inspection path.

[0086] The stereo image loading module 605 is used to load a 3D stereo image of the area where the robot is located from the Beidou Cloud 3D vision system according to the target location.

[0087] The obstacle detection module 606 detects whether there are obstacles in the robot's driving direction through the obstacle sensor on the robot and the 3D stereo image. If so, it executes the obstacle clearing control module 607.

[0088] The obstacle removal control module 607 is used to control the robotic arm to remove obstacles based on the sensing data of the obstacle sensor when an obstacle is detected.

[0089] As a further limitation of the technical solution of the present invention, the Beidou positioning module 601 is specifically used for:

[0090] During the robot's inspection and obstacle removal process, the control Beidou positioning module transmits positioning signals to at least three Beidou satellites and receives sensing signals returned by at least three Beidou satellites after responding to the positioning signals.

[0091] As a further limitation of the technical solution of the present invention, the target position determination module 602 is specifically used for:

[0092] The distance from each Beidou satellite to the robot is calculated based on the sensing signals of each Beidou satellite;

[0093] Three circles are generated with the BeiDou satellite as the center and the distance from at least three BeiDou satellites to the robot as the radius. The intersection of the three circles is taken as the target position of the robot.

[0094] As a further limitation of the technical solution of the present invention, the stereoscopic image loading module 605 is specifically used for:

[0095] The target location is sent to the BeiDou satellite system via BeiDou short message. The BeiDou satellite system then sends the target location to the BeiDou cloud 3D vision system. The BeiDou cloud 3D vision system generates a 3D stereoscopic scan image of the area where the robot is located based on the target location and performs visual monitoring based on the 3D stereoscopic scan image.

[0096] The system receives 3D stereoscopic scan images of the area where the robot is located from the Beidou Cloud 3D vision system.

[0097] As a further limitation of the technical solution of the present invention, the Beidou Cloud 3D Vision System includes a Beidou base station subsystem, which is used to provide a communication channel for the area within a preset range where the target location is located.

[0098] As a further limitation of the technical solution of the present invention, it also includes the following modules:

[0099] The network map update module is used to send the target location to the user terminal, and the user terminal is used to update the network map loaded on the user terminal according to the target location.

[0100] As a further limitation of the technical solution of the present invention, the obstacle sensor includes an ultrasonic sensor, a screening camera, and an obstacle capture camera, and the obstacle judgment module 606 specifically includes components for:

[0101] The ultrasonic sensor, the 3D stereoscopic scanning image, and the screening camera are used to determine whether there are obstacles within a preset distance of the robot's travel direction.

[0102] As a further limitation of the technical solution of the present invention, the obstacle clearing control module 607 is specifically used for:

[0103] The obstacle-capturing camera is controlled to acquire images of obstacles;

[0104] The obstacle features are identified based on the obstacle image, and the robotic arm is controlled to remove the obstacle based on the obstacle features.

[0105] As a further limitation of the technical solution of the present invention, the robot's chassis includes a chassis adjustment device, and the robotic arm is controlled to clear obstacles based on the sensing data of the obstacle sensor. Specifically, the method further includes the following steps:

[0106] The robot uses ultrasonic sensors to detect whether there are obstacles within a preset height range in its direction of travel.

[0107] If so, control the chassis adjustment device to adjust the height of the chassis.

[0108] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0109] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0110] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0111] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.

[0112] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A control method for a substation inspection and obstacle clearing robot based on BeiDou satellite, characterized in that, The robot is equipped with a Beidou positioning module, obstacle sensors, and a robotic arm. The robot communicates with the Beidou Cloud 3D vision system, specifically including the following steps: During the robot's inspection and obstacle removal process, the control Beidou positioning module transmits positioning signals to the Beidou satellite and receives the sensing signals returned by the Beidou satellite after responding to the positioning signals. The target position of the robot is determined based on the sensing signals; Determine whether the robot has deviated from the preset inspection path based on the target location; If so, control the robot so that it performs inspections according to the preset inspection path; If not, load a 3D stereoscopic image of the area where the robot is located from the Beidou Cloud 3D Vision System according to the target location; The obstacle sensor on the robot and the 3D stereo image are used to detect whether there are obstacles in the robot's direction of travel; When an obstacle is detected, the robotic arm is controlled to remove the obstacle based on the sensing data from the obstacle sensor; The Beidou Cloud 3D Vision System includes a power base station subsystem, a data sharing subsystem, a Beidou base station subsystem, and an inspection subsystem. The power base station subsystem is used to receive data from the BeiDou satellite system and generate 3D stereoscopic images. The data sharing subsystem is used for anomaly detection based on the 3D stereoscopic images generated by the power base station subsystem; The Beidou base station subsystem is used to provide a communication channel for the area within a preset range where the target location is located; The inspection subsystem is used to upload data, videos, and images collected by various cameras and sensors to the Beidou base station subsystem via a wireless image transmission module.

2. The control method for a substation inspection and obstacle clearing robot based on BeiDou satellite as described in claim 1, characterized in that, During the robot's inspection and obstacle removal process, the control of the BeiDou positioning module to transmit positioning signals to the BeiDou satellite and receive the sensing signals returned by the BeiDou satellite after responding to the positioning signals specifically includes the following steps: During the robot's inspection and obstacle removal process, the control Beidou positioning module transmits positioning signals to at least three Beidou satellites and receives sensing signals returned by at least three Beidou satellites after responding to the positioning signals.

3. The control method for a substation inspection and obstacle clearing robot based on BeiDou satellite as described in claim 1, characterized in that, Determining the target position of the robot based on the sensing signals specifically includes the following steps: The distance from each Beidou satellite to the robot is calculated based on the sensing signals of each Beidou satellite; Three circles are generated with the BeiDou satellite as the center and the distance from at least three BeiDou satellites to the robot as the radius. The intersection of the three circles is taken as the target position of the robot.

4. The control method for a substation inspection and obstacle clearing robot based on BeiDou satellite as described in claim 1, wherein a 3D stereoscopic image of the area where the robot is located is loaded from the BeiDou cloud 3D vision system according to the target location, specifically includes the following steps: The target location is sent to the BeiDou satellite system via BeiDou short message. The BeiDou satellite system then sends the target location to the BeiDou cloud 3D vision system. The BeiDou cloud 3D vision system generates a 3D stereoscopic scan image of the area where the robot is located based on the target location and performs visual monitoring based on the 3D stereoscopic scan image. The system receives 3D stereoscopic scan images of the area where the robot is located from the Beidou Cloud 3D vision system.

5. The control method for a substation inspection and obstacle clearing robot based on BeiDou satellite according to claim 4, after determining the target position of the robot based on the sensing signal, further includes the following steps: The target location is sent to the user terminal, which then updates the network map loaded on the user terminal based on the target location.

6. The control method for a substation inspection and obstacle removal robot based on Beidou satellite according to claim 4, wherein the obstacle sensor includes an ultrasonic sensor, a screening camera, and an obstacle capture camera, and the robot detects whether there are obstacles in the robot's travel direction through the obstacle sensor on the robot and the 3D stereoscopic image, specifically including the following steps: The ultrasonic sensor, the 3D stereoscopic scanning image, and the screening camera are used to determine whether there are obstacles within a preset distance of the robot's travel direction.

7. The control method for a substation inspection and obstacle removal robot based on BeiDou satellite as described in claim 6, wherein the robotic arm is controlled to remove obstacles according to the sensing data of the obstacle sensor, specifically includes the following steps: The obstacle-capturing camera is controlled to acquire images of obstacles; The obstacle features are identified based on the obstacle image, and the robotic arm is controlled to remove the obstacle based on the obstacle features.

8. The control method for a substation inspection and obstacle removal robot based on Beidou satellite according to claim 7, wherein the robot's chassis includes a chassis adjustment device, and the robotic arm is controlled to remove obstacles based on the sensing data from the obstacle sensor, specifically further comprising the following steps: The robot uses ultrasonic sensors to detect whether there are obstacles within a preset height range in its direction of travel. If so, control the chassis adjustment device to adjust the height of the chassis.

9. A substation inspection and obstacle clearing robot control system based on BeiDou satellite, characterized in that, The robot is equipped with a BeiDou positioning module, obstacle sensors, and a robotic arm. It communicates with the BeiDou Cloud 3D vision system, specifically including the following modules: The Beidou positioning module is used to control the Beidou positioning module to transmit positioning signals to the Beidou satellite and receive the sensing signals returned by the Beidou satellite after responding to the positioning signals during the robot's inspection and obstacle removal process. A target location determination module is used to determine the target location of the robot based on the sensing signal; The deviation judgment module is used to determine whether the robot has deviated from the preset inspection path based on the target position. If yes, the path control module is executed; if no, the stereo image loading module is executed. A path control module is used to control the robot so that the robot performs inspections according to the preset inspection path. The stereo image loading module is used to load a 3D stereo image of the area where the robot is located from the Beidou Cloud 3D vision system according to the target location. The obstacle detection module uses the obstacle sensor on the robot and the 3D stereo image to detect whether there are obstacles in the robot's travel direction. If so, it executes the obstacle clearing control module. The obstacle removal control module is used to control the robotic arm to remove obstacles based on the sensing data from the obstacle sensor when an obstacle is detected. The Beidou Cloud 3D Vision System includes a power base station subsystem, a data sharing subsystem, a Beidou base station subsystem, and an inspection subsystem. The power base station subsystem is used to receive data from the BeiDou satellite system and generate 3D stereoscopic images. The data sharing subsystem is used for anomaly detection based on the 3D stereoscopic images generated by the power base station subsystem; The Beidou base station subsystem is used to provide a communication channel for the area within a preset range where the target location is located; The inspection subsystem is used to upload data, videos, and images collected by various cameras and sensors to the Beidou base station subsystem via a wireless image transmission module.

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