Inspection robot and control method thereof

Abstract

The invention discloses an inspection robot and a control method thereof, and relates to the field of electric power system inspection, an unmanned aerial vehicle nest of the inspection robot comprises an unmanned aerial vehicle, and a control module obtains an image collected by a terrain sensing module to determine a real-time terrain map. The target advancing path is determined based on the real-time terrain map, the current position of the inspection robot and the preset unmanned aerial vehicle take-off point, so that the inspection robot moves to the preset unmanned aerial vehicle take-off point according to the path, the unmanned aerial vehicle nest is controlled to release the unmanned aerial vehicle, and inspection under various high altitudes or complex terrains can be adapted; a private protocol communication link between the unmanned aerial vehicle and the control module is established by means of the offline communication module, so that bidirectional communication between the control module and the unmanned aerial vehicle is realized according to the private protocol communication link, and the inspection robot is enabled to serve as a relay base station to ensure reliable communication with the unmanned aerial vehicle and prevent the unmanned aerial vehicle from losing communication.

Description

Technical Field

[0001] This invention relates to the field of power system inspection technology, and in particular to an inspection robot and its control method. Background Technology

[0002] In a power system, high-voltage electricity output from power plants is transmitted to substations via transmission lines for power distribution. To ensure reliable power transmission and supply, transmission lines need to be inspected and patrolled regularly. Traditional power line inspections are conducted manually. However, some power lines are located in high-altitude, mountainous terrain with steep slopes and dense vegetation, making manual inspections highly dangerous. While wheeled / tracked robots have been explored as alternatives, they are ill-suited for terrains such as rocky areas, ravines, and steep slopes. Another approach involves using multi-rotor small drones to transport personnel for inspections. However, these drones rely on GPS (Global Positioning System) and common communication protocols to maintain contact with the remote controller, which is operated by a human. High-altitude areas have signal blind spots, making drones prone to losing contact. This loss of contact can lead to drone loss of control and even crashes, especially during long-distance operations with obstructions or signal interference. Furthermore, improper human operation during beyond-line-of-sight operations can also cause drones to lose contact and crash. Therefore, developing a more effective inspection method is a pressing issue. Summary of the Invention

[0003] In view of this, the present invention provides an inspection robot and its control method, which is conducive to inspection in various high-altitude or complex terrains; and relies on an offline communication module to establish a private protocol communication link between the UAV and the control module, so that the inspection robot can act as a relay base station to ensure reliable communication with the UAV and prevent the UAV from losing contact.

[0004] To address the aforementioned technical issues, this application provides an inspection robot, comprising a body, an unmanned aerial vehicle (UAV) nest, a control module, an offline communication module, a terrain perception module, and a walking module.

[0005] The drone nest is located outside the fuselage and is used to load drones; the control module is communicatively connected to the offline communication module and the remote control device corresponding to the drone.

[0006] The control module is used to acquire images collected by the terrain perception module, determine a real-time terrain map based on the images and a preset terrain construction strategy, and determine a target travel path based on the real-time terrain map, the current position of the inspection robot and the preset drone take-off point, so as to drive the walking module to make the inspection robot move to the preset drone take-off point according to the target travel path, and control the drone nest to release the drone.

[0007] The drone is used for autonomous navigation based on pre-stored inspection coordinates and routes.

[0008] The offline communication module is used to establish a private protocol communication link with the UAV, so as to transmit control commands issued by the control module to the UAV according to the private protocol communication link, or to transmit the inspection results returned by the UAV to the control module; the control commands are take-off and landing commands issued by the control module and / or remote control operation commands issued by the remote control device corresponding to the UAV and forwarded by the control module.

[0009] Furthermore, it also includes a local storage module located inside the housing and connected to the control module;

[0010] The local storage module is used to store the target inspection data after the control module processes the inspection results.

[0011] Furthermore, the offline communication module is either a LoRa module or a Wi-Fi Direct module.

[0012] Furthermore, the inspection robot also includes a navigation module connected to the control module, the navigation module including an inertial navigation system;

[0013] The control module is also used to determine the current position of the inspection robot based on the pre-stored high-precision contour map corresponding to the current inspection area and the feature data of the inertial navigation output that predicts the current position of the inspection robot.

[0014] Furthermore, the fuselage also includes a power module for power supply, which is connected to the control module, the offline communication module, the terrain perception module, the walking module, and also to the fast charging module of the drone nest.

[0015] The drone is also used to return to its home base from the preset drone takeoff point when it determines that its own battery level is less than the preset return battery threshold, so as to charge through the fast charging module.

[0016] Furthermore, the inspection robot also includes a power monitoring module; the power monitoring module is connected to both the power supply module and the control module.

[0017] The control module is also used to determine the remaining power of the power module through the power monitoring module, so that when the remaining power is less than a preset minimum return power threshold, the module can return from the current position to the rendezvous point.

[0018] To address the aforementioned technical problems, the present invention also provides a control method for an inspection robot, applied to the control module of the inspection robot as described above, the control method comprising:

[0019] Acquire images captured by the terrain perception module in the inspection robot;

[0020] A real-time terrain map is determined based on the image and a preset terrain construction strategy;

[0021] Based on the real-time terrain map, the current location of the inspection robot, and the preset drone takeoff point, the target travel path is determined to drive the walking module in the inspection robot so that the inspection robot moves to the preset drone takeoff point according to the target travel path.

[0022] The inspection robot controls the release of drones from the drone nest so that control commands can be issued and inspection results transmitted back by the drones can be received through the offline communication module in the inspection robot.

[0023] Furthermore, the terrain perception module includes a binocular vision camera; it determines a real-time terrain map based on the image and a preset terrain construction strategy, including:

[0024] Based on the images captured by the binocular vision camera, a disparity map is generated by calculating the disparity corresponding to each pixel using a preset matching algorithm.

[0025] Based on the parallax and the preset parallax-depth conversion relationship, the depth corresponding to each pixel is calculated to generate a depth map;

[0026] The first point cloud map in the camera coordinate system corresponding to the binocular vision camera is determined based on the depth map and the disparity map.

[0027] The first point cloud map in the camera coordinate system is subjected to coordinate transformation to obtain the second point cloud map in the robot coordinate system;

[0028] Terrain analysis is performed on the second point cloud map to obtain terrain features, which include slope information, obstacle information and roughness information;

[0029] A real-time terrain map is generated based on the terrain features and the second point cloud map.

[0030] Furthermore, after determining the target's travel path based on the real-time terrain map, the current location of the inspection robot, and the preset drone takeoff point, the process also includes:

[0031] The walking gait of the walking module is adjusted based on the slope characteristics, obstacle characteristics, and roughness characteristics.

[0032] Furthermore, determining the target's travel path based on the real-time terrain map, the current location of the inspection robot, and the preset drone takeoff point includes:

[0033] Determine the slope weight corresponding to the slope information, the obstacle weight corresponding to the obstacle information, and the roughness weight corresponding to the roughness information for each region under the real-time terrain map;

[0034] The passage cost for each of the aforementioned regions is determined to be the sum of the slope weight, the obstacle weight, and the roughness weight;

[0035] The target travel path from the current location of the inspection robot to the preset takeoff point of the drone is determined based on the passage cost and the preset path planning algorithm.

[0036] This application provides an inspection robot and its control method. The inspection robot includes a body, a drone nest, a control module, an offline communication module, a terrain perception module, and a walking module. The drone nest contains a drone. The control module is communicatively connected to the offline communication module and the corresponding remote control device of the drone. The control module is used to acquire images collected by the terrain perception module, determine a real-time terrain map based on the images and a preset terrain construction strategy, and determine a target travel path based on the real-time terrain map, the current position of the inspection robot, and a preset drone takeoff point. This drives the walking module to move the inspection robot to the preset drone takeoff point along the target travel path, and controls the drone nest to release the drone. This facilitates inspection in various high-altitude or complex terrains. A private protocol communication link is established between the drone and the control module through the offline communication module. This allows the robot to transmit control commands issued by the control module to the drone or transmit inspection results returned by the drone to the control module. This enables the inspection robot to act as a relay base station to ensure reliable communication with the drone and prevent the drone from losing contact. Transporting the drone to the preset drone takeoff point before releasing it also facilitates the reliable operation of the offline communication and is beneficial for applications in various inspection tasks.

[0037] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application, it can be implemented according to the contents of the specification. In order to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0038] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0039] Figure 1 This invention provides a structural schematic diagram of an inspection robot.

[0040] Figure 2 This invention provides a schematic diagram of the connection relationships between various modules in an inspection robot.

[0041] Figure 3 A flowchart of a control method for an inspection robot provided by the present invention. Detailed Implementation

[0042] The core of this invention is to provide an inspection robot and its control method, which is suitable for inspection in various high-altitude or complex terrains; and relies on an offline communication module to establish a private protocol communication link between the drone and the control module, so that the inspection robot can act as a relay base station to ensure reliable communication with the drone and prevent the drone from losing contact.

[0043] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0044] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0045] Please refer to Figure 1 and Figure 2 , Figure 1 This is a structural schematic diagram of an inspection robot provided by the present invention. Figure 2 This is a schematic diagram showing the connection relationship of various modules in an inspection robot provided by the present invention.

[0046] The inspection robot includes a body 1, a drone nest 2, a control module 5, an offline communication module 6, a terrain perception module 3, and a walking module 4;

[0047] The drone nest 2 is located outside the fuselage 1 and is used to carry the drone 21; the control module 5 is connected to the offline communication module 6 and the remote control device corresponding to the drone 21.

[0048] The control module 5 is used to acquire images collected by the terrain perception module 3, determine a real-time terrain map based on the images and a preset terrain construction strategy, and determine the target travel path based on the real-time terrain map, the current position of the inspection robot and the preset drone take-off point, so as to drive the walking module 4 to make the inspection robot move to the preset drone take-off point according to the target travel path, and control the drone nest 2 to release the drone 21.

[0049] The drone 21 is used for autonomous navigation based on pre-stored inspection coordinates and routes;

[0050] The offline communication module 6 is used to establish a private protocol communication link with the UAV 21 so as to transmit control commands issued by the control module 5 to the UAV 21 according to the private protocol communication link, or to transmit the inspection results returned by the UAV 21 to the control module 5; the control commands are take-off and landing commands issued by the control module 5 and / or remote control operation commands issued by the remote control device corresponding to the UAV 21 forwarded by the control module 5.

[0051] In this embodiment, as Figure 1 As shown, the terrain perception module 3 can specifically be a binocular vision camera 31. To ensure the stability of the acquired images, the terrain perception module 3 can also include a gimbal stabilization unit 32. Furthermore, depending on the actual application requirements, the terrain perception module 3 can also include a lidar unit; no specific limitations are made here. The drone nest 2 can be specifically located on the back of the fuselage 1, and this drone nest 2 is used to carry the drone 21. The drone nest 2 can be as follows: Figure 1As shown in the vertical setting, the takeoff angle of the drone 21 needs to be set so that the drone 21 will not collide with the binocular vision camera 31 when it is released from the drone nest 2 at this takeoff angle. Of course, the drone nest 2 can also be set horizontally so that the drone 21 can take off directly vertically when it is released. There is no special limitation here, and it can be set flexibly according to actual needs. The drone 21 has pre-stored corresponding inspection routes and multiple inspection coordinates (such as the coordinates of power transmission towers) that need to be inspected along the inspection routes. The drone 21 here is a drone with autonomous navigation capabilities. The walking module 4 here can include a drive module and N mechanical feet 41. Specifically, the drive module can be a servo motor, and the mechanical feet 41 can be jointed bionic mechanical feet 41. The control module 5, servo motors, and mechanical feet 41 are connected in sequence. The control module 5 can drive the mechanical feet 41 to move, thereby moving the inspection robot. In addition, pressure sensors can be mounted on the mechanical feet 41. Based on the current foot position on the target walking path planned by the control module 5, the torque of the servo motor is dynamically adjusted in combination with the pressure information fed back by the pressure sensors, so as to further adjust the gait of the mechanical feet 41 for different terrains. For example, when climbing a slope, a climbing gait is adopted to increase the leg flexion and extension angle; when crossing obstacles, an obstacle-crossing gait is adopted to raise the leg height. There are no special limitations here, and it can be flexibly set according to actual needs.

[0052] More specifically, the inspection robot starts from the assembly point, carries the drone nest 2 and moves to the preset drone take-off point according to the target travel path, and controls the drone nest 2 to release the drone 21 at the preset drone take-off point.

[0053] The inspection robot includes an offline communication module 6, which is used to pre-establish a private protocol communication link with the drone 21 to reduce signal interference during communication. It should be noted that... Figure 2 The communication connection between the offline communication module 6 and the UAV 21 is shown by dashed lines; and the control module 5 is also connected to the remote control device corresponding to the UAV 21 (specifically, it can be various wireless communication methods utilizing public networks). Figure 2The communication connection between the control module 5 and the remote control device is shown by a dashed line. When the drone 21 performs an inspection task, the inspection robot acts as a relay base station. To prevent communication loss when the drone 21 and the remote control device are too far apart or are obstructed, the control module 5 in the inspection robot can receive remote control operations sent by the remote control device and forward them to the drone 21 via the offline communication module 6. Of course, it can also send the inspection results of the drone 21 during the inspection task back to the remote control device, which helps to make up for the signal blind spot when the two communicate directly. When obstruction or remote control device failure causes the control module 5 to lose connection with the remote control device, the inspection robot can act as a local control center to directly send control commands to the drone 21 through the offline communication module 6. Specifically, since the control module 5 has a certain number of pre-stored control commands, such as take-off and landing commands and photo-taking commands, it can still ensure communication and control with the drone 21, and avoid the drone 21 crashing or blindly returning due to loss of connection. In addition, the drone 21 can be set with an automatic return-to-home program. In a very special case, when the drone 21 loses contact with the offline communication module 6, the drone 21 can return to the preset drone take-off point based on the automatic return-to-home program to avoid losing control and falling due to loss of target. The drone 21 can also switch to visual follow mode to follow the inspection robot or land in the drone nest 2.

[0054] Understandably, the preset UAV takeoff point can be selected based on the inspection route that UAV 21 needs to execute, to ensure a reliable communication connection between the offline communication module 6 and UAV 21. Furthermore, the private protocol communication link between the offline communication module 6 and UAV 21 can also incorporate channel frequency hopping anti-interference technology. This means that the communication channel used during actual transmission will dynamically switch according to interference conditions, such as hopping from channel 1 to channel 5, to avoid continuous interference causing signal interruption and ensure the stability of control command transmission.

[0055] In summary, this application provides an inspection robot with an integrated drone nest 2 for carrying drone 21. The preset drone takeoff point setting and corresponding control logic facilitate inspection in various high-altitude or complex terrains, providing terrain self-adaptation capability. Furthermore, the offline communication module 6 enables the inspection robot to act as a relay base station, ensuring reliable communication with drone 21 and preventing drone 21 from losing contact. Transporting drone 21 to the preset drone takeoff point before launching it also facilitates the reliable execution of the aforementioned offline communication, thus enabling reliable execution of inspection tasks in various complex environments.

[0056] Based on the above embodiments:

[0057] In some embodiments, a local storage module disposed inside the housing 1 and connected to the control module 5 is also included;

[0058] The local storage module is used to store the target inspection data after the control module 5 processes the inspection results.

[0059] Specifically, considering that when obstruction or remote control device malfunction causes a disconnection between control module 5 and remote control device, the inspection robot can act as a local control center to directly send control commands to drone 21 via offline communication module 6. Furthermore, since the inspection robot has a built-in local storage module, it can save the inspection results (such as images and videos) transmitted by drone 21. Once communication with the remote control device is restored, the inspection results can be synchronized to the remote control device, ensuring that task data is not lost. For example, this local storage module can support a minimum of 1TB of information storage.

[0060] In some embodiments, the offline communication module 6 is a LoRa module or a Wi-Fi Direct module.

[0061] Specifically, the above settings allow the use of offline communication module 6 to replace the traditional method of relying on common communication protocols and public networks, ensuring reliable communication between the inspection robot and drone 21. More specifically, the private protocol communication link can be customized according to the physical layer of the LoRa module or Wi-Fi Direct module.

[0062] In some embodiments, the inspection robot further includes a navigation module 7 connected to the control module 5, the navigation module 7 including an inertial navigation system;

[0063] The control module 5 is also used to determine the current position of the inspection robot based on the pre-stored high-precision contour map corresponding to the current inspection area and the feature data of the inertial navigation output that predicts the current position of the inspection robot.

[0064] Specifically, the navigation module 7 may include GPS, but since GPS may fail in high-altitude or signal-blocked areas, a high-precision contour map is stored in advance for the current inspection area. Based on the feature data predicted by the inertial navigation output, the current position of the inspection robot can be determined, thus achieving GPS-free positioning.

[0065] In some embodiments, the interior of the fuselage 1 also includes a power module for power supply. The power module is connected to the control module 5, the offline communication module 6, the terrain perception module 3, the walking module 4, and is also connected to the fast charging module of the unmanned aerial vehicle nest 2.

[0066] The drone 21 is also used to return to the preset drone takeoff point as the target point when it determines that its own battery power is less than the preset return battery power threshold, so as to charge through the fast charging module.

[0067] In this embodiment, considering that the drone 21 has limited endurance and requires frequent battery replacements during long-distance flights, a power module is set to supply power to the various power modules of the inspection robot and the drone 21 (the power supply to the walking module 4 refers to the power supply to the servo motor in the walking module 4). It should be noted that the specific value of the preset return-home power threshold is not particularly limited here, but the preset return-home power threshold needs to be greater than the minimum power consumption required for the drone 21 to return to the preset drone takeoff point.

[0068] More specifically, preferably, the fast-charging module can be configured as a wireless fast-charging module to quickly recharge the drone 21 after it returns to the drone nest 2. It is evident that this configuration improves patrol efficiency.

[0069] In some embodiments, the inspection robot further includes a power monitoring module; the power monitoring module is connected to the power module and the control module 5 respectively.

[0070] The control module 5 is also used to determine the remaining power of the power module through the power monitoring module, so that when the remaining power is less than the preset minimum return power threshold, it can return from the current position to the rendezvous point.

[0071] Specifically, monitoring the remaining power of the power module helps ensure that the inspection robot reliably returns to the rendezvous point. Understandably, before the inspection robot returns, it is necessary to determine whether drone 21 has returned to drone nest 2. For drone 21 that has not returned, a return control command can be sent to it via the offline communication module 6.

[0072] Please refer to Figure 3 , Figure 3 A flowchart of a control method for an inspection robot provided by the present invention.

[0073] The control method for this inspection robot is applied to the control module of the inspection robot as described above. The control method includes:

[0074] S11: Acquire images collected by the terrain perception module in the inspection robot;

[0075] S12: Determine the real-time terrain map based on the image and the preset terrain construction strategy;

[0076] S13: Determine the target travel path based on the real-time terrain map, the current location of the inspection robot, and the preset drone takeoff point, so as to drive the walking module in the inspection robot to move the inspection robot to the preset drone takeoff point according to the target travel path.

[0077] S14: Control the drone nest in the inspection robot to release the drone so that control commands can be issued and inspection results transmitted back by the drone can be received through the offline communication module in the inspection robot.

[0078] For a description of the control method for the inspection robot provided in this application, please refer to the above-described embodiment of the inspection robot; it will not be repeated here.

[0079] In some embodiments, the terrain perception module includes a binocular vision camera; determining a real-time terrain map based on images and a preset terrain construction strategy includes:

[0080] Based on the images captured by the binocular vision camera, a disparity map is generated by calculating the disparity corresponding to each pixel using a preset matching algorithm.

[0081] A depth map is generated by calculating the depth of each pixel based on the disparity and the preset disparity-depth conversion relationship.

[0082] Determine the first point cloud map in the camera coordinate system corresponding to the binocular vision camera based on the depth map and disparity map;

[0083] The first point cloud image in the camera coordinate system is transformed to obtain the second point cloud image in the robot coordinate system.

[0084] Terrain analysis is performed on the second point cloud map to obtain terrain features, including slope information, obstacle information, and roughness information.

[0085] A real-time terrain map is generated based on terrain features and the second point cloud map.

[0086] In this embodiment, the real-time terrain map can be redefined every 50 meters or at preset intervals to achieve dynamic updates. Without specific limitations, constructing this real-time terrain map helps to understand the terrain information of the area where the inspection robot is currently located, facilitating the subsequent determination of the target's travel path and enabling adaptive adjustments to the travel path. Specifically, it is necessary to synchronize the binocular vision cameras beforehand. Before calculating the disparity corresponding to each pixel using a preset matching algorithm, preprocessing can be performed on the images acquired by the binocular vision cameras. This preprocessing includes distortion correction based on camera calibration parameters, image denoising, contrast enhancement, and binocular epipolar correction to ensure that the matching points are on the horizontal line. Without specific limitations, this process is not limited to these steps.

[0087] The preset matching algorithm here can be either the SGM algorithm (Semi-Global Matching), which balances accuracy and real-time performance, or the ELAS algorithm (Efficient Large-scale Stereo), which is suitable for outdoor scenes. No specific stereo matching algorithm is specified here. The calculation of disparity can be accelerated by parallel computing using GPU (Graphics Processing Unit) or hardware acceleration using FPGA (Field Programmable Gate Array).

[0088] Considering that the spatial two-dimensional coordinates of each pixel can be located through the disparity map, the corresponding three-dimensional coordinates can only be formed by supplementing the depth coordinates under the depth map. Therefore, it is also necessary to generate a depth map. It should be noted that the preset disparity-depth conversion relationship here is depth Z = (focal length f × baseline B) / disparity d. In addition, invalid points can be filtered out. For example, for low-texture areas with small pixel grayscale changes, there may be mismatches due to inaccurate disparity calculation. By removing mismatches in low-texture areas, these invalid points can be effectively filtered out. Of course, other methods can be used to filter invalid points, and no particular limitation is made here.

[0089] The Point Cloud Library (PCL) is used to determine the first point cloud image in the camera coordinate system corresponding to the binocular vision camera based on the depth map and disparity map. Considering that the binocular vision camera is mounted on the head of the inspection robot, there is a positional deviation between the camera coordinate system and the robot coordinate system. Therefore, the first point cloud image in the camera coordinate system can be transformed to obtain the second point cloud image in the robot coordinate system. The position of each point in the second point cloud image directly represents its position relative to the inspection robot. Furthermore, considering that the inspection robot will tilt forward or backward or left or right due to terrain undulations during movement, causing the point cloud image captured by the camera to be tilted, the second point cloud image can also be corrected based on the attitude angles output by the inertial navigation system in real time.

[0090] Furthermore, the steps for terrain analysis of the second point cloud map can be as follows: Divide the second point cloud map into blocks to obtain multiple grids (such as 10cm×10cm grids), use the RANSAC algorithm (Random Sample Consensus Algorithm) to fit a local plane equation Ax+By+Cz+D=0 for each network, where A, B, C, and D are the fitting coefficients, respectively; calculate the slope based on this local plane equation. Specifically, determine the normal vector of the local plane equation (the direction of the normal vector is perpendicular to the plane, reflecting the tilt of the plane), calculate the slope angle by the angle between the normal vector and the vertical direction (i.e., the Z-axis), and then generate a slope map. Inaccessible areas can be marked on the slope map according to the slope angle, such as marking areas with a slope angle greater than 30° as inaccessible areas. A height histogram is plotted for the point cloud height (i.e., Z-coordinate) within each grid, and the mode height is taken as the ground height for that area. Through difference calculation, the obstacle height for each point in each grid is obtained as the actual height of that point minus the ground height. Obstacles are segmented using DBSCAN (Density-Based Spatial Clustering of Applications with Noise), with points having a height of at least 10cm as the segmentation criterion. Furthermore, a minimum bounding box is generated for each clustered obstacle, and its 3D coordinate range is recorded to facilitate the inspection robot's determination of obstacle size and location and planning of its bypass maneuvers. Roughness reflects the unevenness of the terrain surface and can be calibrated based on the dispersion of height values ​​at each point in each grid (e.g., statistical height standard deviation).

[0091] It should also be noted that the real-time terrain map generated here can be a 2.5D elevation grid map, and each grid can store average height, maximum height difference, slope, accessibility, obstacles, roughness, etc.

[0092] In some embodiments, after determining the target's travel path based on a real-time terrain map, the current location of the inspection robot, and a preset drone takeoff point, the method further includes:

[0093] The walking gait of the drive walking module is adjusted according to slope characteristics, obstacle characteristics, and roughness characteristics.

[0094] Specifically, the above settings allow for further adjustments to the gait of the mechanical foot for different terrains. For example, a climbing gait can be used when climbing a slope to increase the leg flexion and extension angle; and an obstacle-crossing gait can be used when crossing obstacles to raise the leg height.

[0095] In some embodiments, determining the target's travel path based on a real-time terrain map, the current location of the inspection robot, and a preset drone takeoff point includes:

[0096] Determine the slope weight corresponding to the slope information, the obstacle weight corresponding to the obstacle information, and the roughness weight corresponding to the roughness information for each region under the real-time terrain map;

[0097] The passage cost for each area is determined to be the sum of the slope weight, obstacle weight, and roughness weight;

[0098] The target travel path from the current location of the inspection robot to the preset drone takeoff point is determined based on the travel cost and the preset path planning algorithm.

[0099] In this embodiment, the slope is determined based on the slope angle. The steeper the slope of the grid area, the greater the corresponding slope weight. Of course, the slope weight can also be set to be different for different slope ranges, as long as the slope and slope weight are positively correlated. The higher the height of the obstacle, the greater the obstacle weight of the grid area. The greater the roughness, the greater the corresponding roughness weight. Of course, the roughness weight can also be set to be different for different roughness ranges, as long as the roughness and roughness weight are positively correlated.

[0100] The goal is to minimize the travel cost along the target path. The preset path planning algorithm can include a global path planning algorithm and a local obstacle avoidance algorithm. Specifically, a cost map can be generated based on the travel cost in the real-time terrain map. The global path planning algorithm determines the path with the lowest global cost from the current position of the inspection robot to the preset drone takeoff point. The local obstacle avoidance algorithm adjusts the local path in real-time based on this lowest-cost global path to avoid dynamic obstacles. The global path planning algorithm can be either the A* algorithm or the D*Lite algorithm; the local obstacle avoidance algorithm can be the TEB algorithm (Timed-Elastic-Band).

[0101] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section. Relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0102] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An inspection robot, characterized in that, It includes the fuselage, unmanned aerial vehicle (UAV) pod, control module, offline communication module, terrain perception module, and mobility module; The drone nest is located outside the fuselage and is used to load drones; the control module is communicatively connected to the offline communication module and the remote control device corresponding to the drone. The control module is used to acquire images collected by the terrain perception module, determine a real-time terrain map based on the images and a preset terrain construction strategy, and determine a target travel path based on the real-time terrain map, the current position of the inspection robot and the preset drone take-off point, so as to drive the walking module to make the inspection robot move to the preset drone take-off point according to the target travel path, and control the drone nest to release the drone. The drone is used for autonomous navigation based on pre-stored inspection coordinates and routes. The offline communication module is used to establish a private protocol communication link with the UAV, so as to transmit the control commands issued by the control module to the UAV according to the private protocol communication link, or to transmit the inspection results returned by the UAV to the control module. The control commands are take-off and landing commands issued by the control module and / or remote control operation commands issued by the remote control device corresponding to the UAV and forwarded by the control module.

2. The inspection robot as described in claim 1, characterized in that, It also includes a local storage module located inside the body and connected to the control module; The local storage module is used to store the target inspection data after the control module processes the inspection results.

3. The inspection robot as described in claim 1, characterized in that, The offline communication module is either a LoRa module or a Wi-Fi Direct module.

4. The inspection robot as described in claim 1, characterized in that, The inspection robot also includes a navigation module connected to the control module, and the navigation module includes an inertial navigation system. The control module is also used to determine the current position of the inspection robot based on the pre-stored high-precision contour map corresponding to the current inspection area and the feature data of the inertial navigation output that predicts the current position of the inspection robot.

5. The inspection robot as described in any one of claims 1 to 4, characterized in that, The fuselage also includes a power module for power supply, which is connected to the control module, the offline communication module, the terrain perception module, the walking module, and also to the fast charging module of the drone nest. The drone is also used to return to its home base from the preset drone takeoff point when it determines that its own battery level is less than the preset return battery threshold, so as to charge through the fast charging module.

6. The inspection robot as described in claim 5, characterized in that, The inspection robot also includes a power monitoring module; the power monitoring module is connected to both the power module and the control module. The control module is also used to determine the remaining power of the power module through the power monitoring module, so that when the remaining power is less than a preset minimum return power threshold, the module can return from the current position to the rendezvous point.

7. A control method for an inspection robot, characterized in that, The control module applied in the inspection robot as described in any one of claims 1 to 6, the control method comprising: Acquire images captured by the terrain perception module in the inspection robot; A real-time terrain map is determined based on the image and a preset terrain construction strategy; Based on the real-time terrain map, the current location of the inspection robot, and the preset drone takeoff point, the target travel path is determined to drive the walking module in the inspection robot so that the inspection robot moves to the preset drone takeoff point according to the target travel path. The inspection robot controls the release of drones from the drone nest so that control commands can be issued and inspection results transmitted back by the drones can be received through the offline communication module in the inspection robot.

8. The control method as described in claim 7, characterized in that, The terrain perception module includes a binocular vision camera; it determines a real-time terrain map based on the image and a preset terrain construction strategy, including: Based on the images captured by the binocular vision camera, a disparity map is generated by calculating the disparity corresponding to each pixel using a preset matching algorithm. Based on the parallax and the preset parallax-depth conversion relationship, the depth corresponding to each pixel is calculated to generate a depth map; The first point cloud map in the camera coordinate system corresponding to the binocular vision camera is determined based on the depth map and the disparity map. The first point cloud map in the camera coordinate system is subjected to coordinate transformation to obtain the second point cloud map in the robot coordinate system; Terrain analysis is performed on the second point cloud map to obtain terrain features, which include slope information, obstacle information and roughness information; A real-time terrain map is generated based on the terrain features and the second point cloud map.

9. The control method as described in claim 8, characterized in that, After determining the target's travel path based on the real-time terrain map, the current location of the inspection robot, and the preset drone takeoff point, the method further includes: The walking gait of the walking module is adjusted based on the slope characteristics, obstacle characteristics, and roughness characteristics.

10. The control method as described in claim 8, characterized in that, Determining the target's travel path based on the real-time terrain map, the current location of the inspection robot, and the preset drone takeoff point includes: Determine the slope weight corresponding to the slope information, the obstacle weight corresponding to the obstacle information, and the roughness weight corresponding to the roughness information for each region under the real-time terrain map; The passage cost for each of the aforementioned regions is determined to be the sum of the slope weight, the obstacle weight, and the roughness weight; The target travel path from the current location of the inspection robot to the preset takeoff point of the drone is determined based on the passage cost and the preset path planning algorithm.

Images