Cruise control method and device of inspection robot and inspection robot

Abstract

The invention relates to the technical field of robots, and discloses a cruise control method and device of an inspection robot and the inspection robot. The cruise control method comprises the following steps: determining a target inspection route according to a target inspection area; controlling the inspection robot to move according to the target inspection route; in the inspection process, obtaining positioning information of the inspection robot in the target inspection area; the moving distance of the inspection robot is obtained; determining the current position of the inspection robot in the target inspection area according to the positioning information and the moving distance; and based on the current position, controlling the inspection robot to operate, so that the inspection robot moves according to the target inspection route. The method can improve the positioning precision of the inspection robot, and ensures that the inspection robot accurately moves according to the target inspection route.

Description

Technical Field

[0001] The present application relates to the field of robot technology, for example, to a cruise control method and device for an inspection robot and an inspection robot. Background Art

[0002] Inspection robots are robot systems that integrate multiple advanced technologies and are used to automatically inspect specific areas or equipment. Before deploying inspection robots, it is often necessary to plan inspection routes in advance so that the inspection robots can effectively inspect within the mission area.

[0003] In the related technology, an intelligent patrol robot and its implementation method are disclosed, including: obtaining a navigation request from a terminal device, the navigation request including a target position and a current position; creating a three-dimensional network space based on the map data built into the intelligent patrol robot, wherein each grid unit of the three-dimensional network space corresponds to a position point; calculating an attractive potential field and a repulsive potential field based on the current position and the target position, and combining the attractive potential field and the repulsive potential field into a total potential field; calculating the gradient of each of the position points according to the total potential field, wherein the negative direction of the gradient indicates the direction in which the robot moves; setting the position point where the gradient drops the fastest as the next moving target of the intelligent patrol robot, and constructing an optimal path based on the moving target, and controlling the intelligent patrol robot to patrol along the optimal path.

[0004] In the process of implementing the above embodiments, it is found that in the related art, although the path planning method based on gradient descent realizes the optimal path planning, it cannot ensure that the inspection robot can accurately perform inspections along the optimal path. Summary of the invention

[0005] In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key / critical components or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

[0006] The embodiments of the present disclosure provide a cruise control method and device for an inspection robot, and an inspection robot, which can improve the positioning accuracy of the inspection robot, thereby ensuring that the inspection robot moves accurately along a target inspection route.

[0007] In some embodiments, a cruise control method for a patrol robot is provided, including: determining a target patrol route based on a target patrol area; controlling the patrol robot to move along the target patrol route; during the patrol process, obtaining positioning information of the patrol robot in the target patrol area; and obtaining the moving distance of the patrol robot; determining the current position of the patrol robot in the target patrol area based on the positioning information and the moving distance; and based on the current position, controlling the operation of the patrol robot so that the patrol robot moves along the target patrol route.

[0008] Optionally, based on the current position, the steps of controlling the operation of the patrol robot so that the patrol robot moves along a target patrol route include: when the current position is on the target patrol route, controlling the patrol robot to continue moving along the target patrol route; when the current position deviates from the target patrol route, adjusting the motion parameters of the patrol robot so that the patrol robot returns to the target patrol route and continues moving.

[0009] Optionally, multiple wireless base stations are set up in the target inspection area, and the inspection robot includes a positioning antenna that is communicatively connected to the wireless base station; the step of obtaining the positioning information of the inspection robot in the target inspection area includes: controlling the positioning antenna to send a positioning request to the wireless base station; and receiving the positioning information fed back by the wireless base station based on the positioning request.

[0010] Optionally, a plurality of positioning parts are provided in the target inspection area, and the inspection robot includes a proximity switch; the step of obtaining the positioning information of the inspection robot in the target inspection area includes: obtaining feedback information of successful docking with the positioning part from the proximity switch; and generating the positioning information of the inspection robot based on the feedback information.

[0011] Optionally, the proximity switch includes an electromagnetic switch and a photoelectric switch; the step of determining whether the proximity switch and the positioning member are successfully docked includes: obtaining the current value and current direction fed back by the electromagnetic switch; and obtaining the distance value fed back by the photoelectric switch; when the distance value is less than a distance threshold and the current value is greater than a current threshold, or the distance value is less than the distance threshold and the current direction changes, determining that the proximity switch and the positioning member are successfully docked.

[0012] Optionally, the inspection robot includes walking wheels; the step of obtaining the moving distance of the inspection robot includes: obtaining the wheel diameter and the number of rotations of the walking wheels; and determining the moving distance of the inspection robot based on the wheel diameter and the number of rotations.

[0013] Optionally, the step of determining the current position of the inspection robot in the target inspection area based on the positioning information and the moving distance includes: converting the positioning information into the first position coordinates of the inspection robot in the target inspection area; calculating the second position coordinates of the inspection robot in the target inspection area based on the moving distance and the target inspection route; calculating the average or weighted average of the first position coordinates and the second position coordinates to obtain the third position coordinates; and using the third position coordinates as the current position of the inspection robot in the target inspection area.

[0014] Optionally, the inspection robot includes a sensing mechanism; the cruise control method also includes: controlling the sensing mechanism to detect the detection target and obtain the detection result; when the detection result is that the detection target is abnormal, obtaining the position information of the detection target; correcting the target inspection route according to the position information of the detection target, and controlling the inspection robot to move according to the corrected target inspection route.

[0015] Optionally, the inspection robot includes a radar component; the cruise control method also includes: controlling the radar component to detect obstacles in the direction of travel of the inspection robot to obtain obstacle parameters; the obstacle parameters include obstacle volume and obstacle type; when the obstacle volume is greater than a volume threshold and the obstacle type is a fixed obstacle, obtaining the location information of the obstacle; correcting the target inspection route according to the obstacle location information, and controlling the inspection robot to move according to the corrected target inspection route.

[0016] Optionally, multiple charging base stations are set up in the target inspection area; the cruise control method also includes: obtaining the remaining power of the inspection robot; when the remaining power is less than or equal to the power threshold, obtaining the location information of the target charging base station; the target charging base station refers to the charging base station closest to the current position of the inspection robot; according to the location information of the target charging base station, the target inspection route is corrected, and the inspection robot is controlled to move according to the corrected target inspection route.

[0017] Optionally, the step of determining a target inspection route according to the target inspection area includes: obtaining an equipment layout diagram of the target inspection area; and generating a target inspection route of the inspection robot according to the equipment layout diagram.

[0018] Optionally, the step of generating a target inspection route for the inspection robot based on the equipment layout diagram includes: obtaining operating parameters of the equipment and environmental parameters of the target inspection area; determining the target detection device based on the environmental parameters and operating parameters; and generating the target inspection route for the inspection robot based on the position coordinates of the target detection device in the equipment layout diagram.

[0019] In some embodiments, a cruise control device for an inspection robot is provided, comprising: a determination module, configured to determine a target inspection route based on a target inspection area; a control module, configured to control the inspection robot to move along the target inspection route; an acquisition module, configured to acquire positioning information of the inspection robot in the target inspection area during the inspection process; and acquire the moving distance of the inspection robot; an analysis module, configured to determine the current position of the inspection robot in the target inspection area based on the positioning information and the moving distance; and an adjustment module, configured to control the operation of the inspection robot based on the current position, so that the inspection robot moves along the target inspection route.

[0020] In some embodiments, a cruise control device for an inspection robot is provided, comprising a processor and a memory storing program instructions, wherein the processor is configured to execute the cruise control method for the inspection robot as described in the above embodiments when running the program instructions.

[0021] In some embodiments, a patrol robot is provided, comprising: a robot body; and a cruise control device of the patrol robot as described in the above embodiments, wherein the cruise control device is installed on the robot body.

[0022] The cruise control method, device and patrol robot provided by the embodiments of the present disclosure can achieve the following technical effects:

[0023] The disclosed embodiment can obtain the positioning information of the inspection robot in the target inspection area and the movement distance of the inspection robot in real time during the inspection process in which the inspection robot moves along the target inspection route, and then determine the current position of the inspection robot in the target inspection area by combining the positioning information and the movement distance, thereby realizing the accurate acquisition of the current position of the inspection robot. Compared with the related art in which the current position of the robot is determined based on the navigation request from the terminal device, the disclosed embodiment improves the positioning accuracy of the inspection robot by combining the positioning information and the movement distance. By improving the positioning accuracy of the inspection robot, it is possible to determine whether the inspection robot deviates from the target inspection route, and to correct it in time when it deviates from the route, so as to ensure that the inspection robot can accurately move along the target inspection route, thereby improving the inspection efficiency and effectiveness.

[0024] The above general description and the following description are exemplary and explanatory only and are not intended to limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] One or more embodiments are exemplarily described by corresponding drawings, which do not limit the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements, and the drawings do not constitute a scale limitation, and wherein:

[0026] Figure 1 is a schematic diagram of the structure of an inspection robot provided by an embodiment of the present disclosure;

[0027] Figure 2 yes Figure 1 A schematic diagram of a portion of the structure of the inspection robot in the illustrated embodiment;

[0028] Figure 3 is a schematic diagram of an inspection robot provided by an embodiment of the present disclosure deployed on a tubular belt transport system and a track;

[0029] Figure 4 is a schematic diagram of a cruise control method for an inspection robot provided by an embodiment of the present disclosure;

[0030] Figure 5 is a schematic diagram of a cruise control method for an inspection robot provided by another embodiment of the present disclosure;

[0031] Figure 6 is a schematic diagram of a cruise control method for an inspection robot provided by another embodiment of the present disclosure;

[0032] Figure 7 is a structural schematic diagram of a first acoustic model provided by an embodiment of the present disclosure;

[0033] Figure 8 is a schematic diagram of a convolution block provided by an embodiment of the present disclosure;

[0034] Fig. 9 is a schematic diagram of a dense block provided by an embodiment of the present disclosure;

[0035] Fig.10 is a schematic diagram of a cruise control method for an inspection robot provided by another embodiment of the present disclosure;

[0036] Fig.11 is a schematic diagram of a cruise control method for an inspection robot provided by another embodiment of the present disclosure;

[0037] Fig.12 is a schematic diagram of a cruise control device of an inspection robot provided by an embodiment of the present disclosure;

[0038] Fig.13 It is a schematic diagram of a cruise control device of an inspection robot provided in another embodiment of the present disclosure.

[0039] Reference numerals:

[0040] 10 inspection robot; 100 robot body; 101 box; 410 walking wheel;

[0041] 500 sensing mechanism; 513 image acquisition device; 514 positioning antenna; 510 proximity switch; 511 electromagnetic switch; 512 photoelectric switch; 517 radar component; 518 single-point laser radar; 520 ultrasonic radar; 522 microphone;

[0042] 120 a cruise control device of the inspection robot; 121 a determination module; 122 a control module; 123 an acquisition module; 124 an analysis module; 125 an adjustment module; 126 a correction module;

[0043] 130 cruise control device of the inspection robot; 131 processor; 132 memory; 133 communication interface; 134 bus;

[0044] 50 tubular belt conveyor; 424 track; 503 positioning piece. DETAILED DESCRIPTION

[0045] In order to be able to understand the features and technical contents of the embodiments of the present disclosure in more detail, the implementation of the embodiments of the present disclosure is described in detail below in conjunction with the accompanying drawings. The attached drawings are for reference only and are not used to limit the embodiments of the present disclosure. In the following technical description, for the convenience of explanation, a full understanding of the disclosed embodiments is provided through multiple details. However, one or more embodiments can still be implemented without these details. In other cases, to simplify the drawings, well-known structures and devices can be simplified for display.

[0046] The terms "first", "second", etc. in the specification and claims of the embodiments of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged where appropriate, so that the embodiments of the embodiments of the present disclosure described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions.

[0047] Unless otherwise stated, the term "plurality" means two or more.

[0048] In the embodiment of the present disclosure, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B indicates: A or B.

[0049] The term "and / or" is a description of the association relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A, B, A and B.

[0050] The term "correspondence" may refer to an association relationship or a binding relationship. The correspondence between A and B means that there is an association relationship or a binding relationship between A and B.

[0051] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other.

[0052] Combination Figure 1 As shown, the embodiment of the present disclosure provides an inspection robot 10, including a robot body 100 and a cruise control device 120 (130) of the inspection robot. The cruise control device 120 (130) of the inspection robot is installed on the robot body 100.

[0053] In the disclosed embodiment, the cruise control device 120 (130) of the inspection robot is installed on the robot body 100. The installation relationship described here is not limited to being placed inside the robot body 100, but also includes the installation connection with other components of the inspection robot 10, including but not limited to physical connection, electrical connection or signal transmission connection, etc. It can be understood by those skilled in the art that the cruise control device 120 (130) of the inspection robot can be adapted to a feasible inspection robot 10, thereby realizing other feasible embodiments.

[0054] Alternatively, if Figure 1 As shown, the robot body 100 includes a box 101, a walking wheel 410 and a sensing mechanism 500. The walking wheel 410 is arranged at the bottom of the box 101 along the height direction of the inspection robot 10. The sensing mechanism 500 and the cruise control device 120 (130) of the inspection robot are arranged in the box 101 at intervals.

[0055] Alternatively, if Figure 1 As shown, the sensing mechanism 500 includes an image acquisition device 513, a positioning antenna 514, a proximity switch 510, a radar assembly 517 and a microphone 522. The image acquisition device 513 and the positioning antenna 514 are arranged at intervals on the top of the box 101 along the height direction of the inspection robot 10. The proximity switch 510 and the walking wheel 410 are arranged at intervals on the bottom of the box 101 along the height direction of the inspection robot 10. The radar assembly 517 and the microphone 522 are arranged at intervals on the side of the box 101 along the moving direction of the inspection robot 10.

[0056] Alternatively, if Figure 1 As shown, the radar assembly 517 includes a single-point laser radar 518 and an ultrasonic radar 520. The single-point laser radar 518 and the ultrasonic radar 520 are arranged on the side of the box 101 at intervals.

[0057] Alternatively, if Figure 2 As shown, the proximity switch 510 includes an electromagnetic switch 511 and a photoelectric switch 512. The electromagnetic switch 511 and the photoelectric switch 512 are arranged at the bottom of the box body 101 at intervals.

[0058] Optionally, the cruise control device 130 of the inspection robot includes a processor 131. The processor 131 can determine a target inspection route according to the target inspection area; can control the inspection robot to move according to the target inspection route; can obtain positioning information of the inspection robot in the target inspection area during the inspection process; can obtain the movement distance of the inspection robot; can determine the current position of the inspection robot in the target inspection area according to the positioning information and the movement distance; can control the operation of the inspection robot based on the current position so that the inspection robot moves according to the target inspection route.

[0059] Combination Figure 1 and Figure 2 As shown, the embodiment of the present disclosure provides a cruise control method for an inspection robot, such as Figure 4 As shown, the cruise control method includes:

[0060] S401, the processor determines a target inspection route according to a target inspection area.

[0061] In this step, the target inspection area refers to a specific geographical or spatial range that needs to be inspected. The specific geographical or spatial range can be a factory workshop, a section of road, a building complex, or any other place that needs to be monitored and maintained. For example, Figure 3 As shown, when the inspection robot 10 in the embodiment of the present disclosure is deployed in a tubular belt conveyor system, the target inspection area can be the factory where the tubular belt conveyor equipment 50 is located. The target inspection route refers to a pre-planned or dynamically generated inspection path in the target inspection area according to inspection requirements (such as inspection point distribution, obstacle location, equipment distribution, etc.). The target inspection route is intended to ensure that the inspection robot can fully and completely cover the target inspection points and complete the inspection task.

[0062] In some embodiments, a target inspection route is pre-planned in the target inspection area. In an actual application, different inspection routes are pre-planned in different inspection areas. Then, the step of determining the target inspection route according to the target inspection area includes: using the inspection route corresponding to the target inspection area as the target inspection route. Figure 3 As shown, when the inspection robot 10 is deployed on the track 242, the target inspection route is the distribution route of the track 424 in the target inspection area. Then, according to the target inspection area, the steps of determining the target inspection route include: obtaining the distribution route of the track in the target inspection area, and using the distribution route of the track as the target inspection route.

[0063] In this embodiment, by pre-planning the inspection route corresponding to the inspection area, or taking the distribution route of the track in the target inspection area as the target inspection route, the target inspection route can be quickly acquired, the response speed of the inspection robot is improved, and thus the inspection efficiency of the inspection robot is improved.

[0064] In some embodiments, the step of determining a target inspection route according to a target inspection area includes: obtaining an equipment layout diagram of the target inspection area; and generating a target inspection route of the inspection robot according to the equipment layout diagram.

[0065] In this embodiment, the equipment layout diagram refers to a drawing that details all the equipment, facilities and their relative positions in the target inspection area. The equipment layout diagram can be obtained by the technician through input from an electronic device that is connected to the inspection robot for communication, or by using modern technology (such as drone aerial photography, laser scanning, etc.) to generate a three-dimensional model, and extract the equipment layout information from it to obtain the equipment layout diagram. By obtaining the equipment layout diagram of the target inspection area, this embodiment can accurately understand the distribution and relative position of the equipment in the area, thereby generating a more accurate inspection route, so that the inspection robot can follow each checkpoint of the target inspection route to avoid omissions or misjudgments. In addition, this embodiment can dynamically and autonomously generate the target inspection route of the inspection robot based on the equipment layout diagram, thereby improving the autonomy and intelligence of the inspection robot.

[0066] Optionally, the step of generating a target inspection route for the inspection robot based on the equipment layout diagram includes: obtaining operating parameters of the equipment and environmental parameters of the target inspection area; determining the target detection device based on the environmental parameters and operating parameters; and generating the target inspection route for the inspection robot based on the position coordinates of the target detection device in the equipment layout diagram.

[0067] In this embodiment, the target detection equipment refers to the equipment distributed in the target inspection area that the inspection robot needs to detect. The equipment operation parameters refer to the parameters that reflect the performance and health status of the equipment, including but not limited to the equipment's operating temperature, working hours, number of historical failures, etc. The environmental parameters of the target inspection area refer to the parameters that have an important impact on the stable operation of the equipment, including but not limited to temperature, humidity, vibration, electromagnetic interference, etc.

[0068] Different environmental parameters and operating parameters will affect the failure probability of the equipment. For example, too high or too low temperature will lead to performance degradation or failure of the equipment. For example, the equipment is easily damaged due to overheating when it continues to work at high temperature, while low temperature will cause some parts of the equipment to become fragile; excessive humidity will cause internal corrosion or short circuits in the equipment; continuous vibration will cause loose connections or wear of parts inside the equipment, increasing the risk of failure; strong electromagnetic fields will interfere with the normal operation of the equipment, causing communication failures or data errors; equipment with long working hours is prone to failure due to wear or aging; equipment with a high number of historical failures and frequent failures has design defects or improper maintenance problems, and has a higher probability of failure.

[0069] In this embodiment, a relationship mapping table between environmental parameters and operating parameters and equipment failure probability is pre-saved, and the relationship mapping table is used to reflect the impact of environmental parameters and operating parameters on equipment failure probability. By comparing different equipment operating parameters and environmental parameters, equipment that is in a high-risk state and more prone to failure (such as equipment whose failure probability exceeds a preset failure probability threshold) is identified, so that the equipment that is in a high-risk state and more prone to failure is determined as a target detection device. After determining the target detection device, refer to the equipment layout diagram, obtain the position coordinates of the target detection device in the target inspection area as the detection point, and use a path planning algorithm (such as a breadth-first search algorithm, a Dijkstra algorithm, an ant colony algorithm, etc.) to generate a target inspection route to ensure that the inspection robot can efficiently cover all target detection devices, i.e., detection points, while avoiding unnecessary duplication and omissions.

[0070] S402, the processor controls the inspection robot to move along the target inspection route.

[0071] S403, during the inspection process, the processor obtains the positioning information of the inspection robot in the target inspection area; and obtains the moving distance of the inspection robot.

[0072] S404: The processor determines the current position of the inspection robot in the target inspection area according to the positioning information and the moving distance.

[0073] S405: The processor controls the inspection robot to operate based on the current position, so that the inspection robot moves along the target inspection route.

[0074] The cruise control method for the inspection robot provided in the embodiment of the present disclosure can obtain the positioning information of the inspection robot in the target inspection area and the movement distance of the inspection robot in real time during the inspection process of the inspection robot moving along the target inspection route, and then determine the current position of the inspection robot in the target inspection area by combining the positioning information and the movement distance, thereby realizing the accurate acquisition of the current position of the inspection robot. Compared with the related art, in which the current position of the robot is determined based on the navigation request from the terminal device, the embodiment of the present disclosure improves the positioning accuracy of the inspection robot by combining the positioning information and the movement distance. By improving the positioning accuracy of the inspection robot, it is possible to determine whether the inspection robot deviates from the target inspection route, and to correct it in time when it deviates from the route, so as to ensure that the inspection robot can accurately move along the target inspection route, thereby improving the inspection efficiency and effectiveness.

[0075] In some embodiments, Figure 1 As shown, a plurality of wireless base stations are arranged in the target inspection area, and the inspection robot 10 includes a positioning antenna 514 which is communicatively connected to the wireless base station. The step of obtaining the positioning information of the inspection robot in the target inspection area includes: controlling the positioning antenna to send a positioning request to the wireless base station; and receiving the positioning information fed back by the wireless base station based on the positioning request.

[0076] In this embodiment, when the inspection robot needs to obtain its own positioning information, the positioning antenna is controlled to send a positioning request to a nearby wireless base station. The positioning request contains the unique identifier of the inspection robot, timestamp and other necessary information so that the wireless base station can correctly identify and respond to the request. After receiving the positioning request, the wireless base station will calculate the location information of the inspection robot based on the location information of the wireless base station, the communication delay with the inspection robot, the signal strength and other parameters, and send the location information as positioning information feedback to the inspection robot, so that the inspection robot can obtain its own position in the target inspection area. In this embodiment, the inspection robot can send a positioning request to the wireless base station in real time and quickly receive the positioning information fed back by the base station. This real-time performance improves the response speed and inspection efficiency of the inspection robot.

[0077] In some embodiments, Figure 1 and Figure 2 As shown, a plurality of positioning members 503 are provided in the target inspection area, and the inspection robot 10 includes a proximity switch 510. The step of obtaining the positioning information of the inspection robot in the target inspection area includes: obtaining feedback information of successful docking with the positioning member fed back by the proximity switch; and generating the positioning information of the inspection robot according to the feedback information.

[0078] In this embodiment, a plurality of positioning members are provided in the target inspection area. When the inspection robot moves to a certain positioning member and the proximity switch docks with the positioning member, the proximity switch detects the presence of the positioning member and triggers the switch, and then outputs feedback information indicating successful docking. The feedback information may be an electrical signal, a digital signal, or a signal in other forms to indicate that the inspection robot has successfully docked with the positioning member. After receiving the feedback information output by the proximity switch, the inspection robot converts the feedback information into specific positioning information to characterize the position information of the inspection robot in the target inspection area. This embodiment, based on the physical docking of the proximity switch and the positioning member, does not require complex communication protocols or algorithm support, is simple and reliable, is not easily affected by environmental interference, and can ensure the accuracy and stability of the positioning information.

[0079] In a specific application, a marker, such as a QR code, an RFID (Radio Frequency Identification) tag, etc., is attached to the positioning member 503, and the QR code or RFID tag records the position information of the positioning member 503 in the target inspection area. The proximity switch 510 includes an encoder, which is used to identify the marker attached to the positioning member 503 when the proximity switch 510 is docked with the positioning member 503, so as to obtain the position information recorded in the marker as the positioning information of the inspection robot 10.

[0080] In a specific application, a plurality of positioning members 503 are arranged at a preset spacing. When the proximity switch 510 is docked with the positioning member 503, a feedback message indicating successful docking is output. The moving distance of the inspection robot 10 is obtained by recording the number of times the feedback message is received and calculating the product of the number of feedback messages and the preset spacing. After obtaining the moving distance of the inspection robot 10, the position coordinates of the inspection robot 10 in the target inspection area can be calculated as the positioning information of the inspection robot 10 according to the moving distance and the target inspection route. The specific steps of calculating the position coordinates of the inspection robot in the target inspection area according to the moving distance and the target inspection route refer to the steps of calculating the second position coordinates of the inspection robot in the target inspection area according to the moving distance and the target inspection route in the following embodiment, which will not be repeated here.

[0081] Alternatively, if Figure 2 As shown, the proximity switch 510 includes an electromagnetic switch 511 and a photoelectric switch 512. The step of determining whether the proximity switch and the positioning member are successfully docked includes: obtaining the current value and current direction fed back by the electromagnetic switch; and obtaining the distance value fed back by the photoelectric switch; when the distance value is less than the distance threshold and the current value is greater than the current threshold, or when the distance value is less than the distance threshold and the current direction changes, determining that the proximity switch and the positioning member are successfully docked.

[0082] In this embodiment, the proximity switch includes an electromagnetic switch and a photoelectric switch. The electromagnetic switch works on the principle of electromagnetic induction. When approaching a metal object (positioning piece), an eddy current effect is generated, thereby triggering the switch action. The photoelectric switch detects the presence and distance of the positioning piece by emitting and receiving light beams.

[0083] When the electromagnetic switch approaches the positioning part and docks with the positioning part, the electromagnetic switch triggers electromagnetic induction and generates current. As the docking area gradually increases, the current value gradually increases. When the docking area is the largest, the current value also reaches the maximum value. Then the electromagnetic switch begins to move away from the positioning part, the docking area begins to decrease, and the direction of the current changes. Therefore, it is possible to determine whether it is close to the positioning part and the degree of docking by obtaining the current value and current direction fed back by the electromagnetic switch. The photoelectric switch emits a light beam and receives the reflected light beam, and calculates the distance value to the positioning part by measuring the round-trip time of the light beam. When the photoelectric switch begins to dock with the positioning part, the distance value fed back by the photoelectric switch becomes smaller.

[0084] In this embodiment, a distance threshold and a current threshold are pre-set as conditions for judging whether the docking is successful. When the distance value is less than the distance threshold and the current value is greater than the current threshold, or the distance value is less than the distance threshold and the current direction changes, it is considered that the proximity switch and the positioning member are successfully docked. In this embodiment, two types of proximity switches, electromagnetic switches and photoelectric switches, are combined to ensure the accuracy of successful docking through double detection. In addition, the embodiment of the present disclosure adopts two independent detection conditions to judge whether the docking is successful, namely, the distance value is less than the distance threshold and the current value is greater than the current threshold, or the distance value is less than the distance threshold and the current direction changes. Such multiple judgment conditions improve the reliability of the judgment and reduce the possibility of misjudgment and missed judgment.

[0085] In some embodiments, the positioning information fed back by the wireless base station based on the positioning request is defined as the first position information, and the positioning information of the inspection robot generated according to the feedback information of successful docking with the positioning member fed back by the proximity switch is defined as the second position information. Then the step of obtaining the positioning information of the inspection robot in the target inspection area includes: determining the positioning information of the inspection robot in the target inspection area according to the first position information and the second position information.

[0086] In this embodiment, the wireless base station and the proximity switch are used simultaneously to obtain the position information of the inspection robot. By combining the positioning information of the wireless base station and the proximity switch to integrate the advantages of both, the positioning accuracy of the inspection robot is improved.

[0087] Optionally, the step of determining the positioning information of the inspection robot in the target inspection area based on the first position information and the second position information includes: converting the first position information into the fourth position coordinates of the inspection robot in the target inspection area; converting the second position information into the fifth position coordinates of the inspection robot in the target inspection area; calculating the average value or weighted average value of the first position coordinates and the second position coordinates to obtain the first position coordinates; and using the first position coordinates as the current position of the inspection robot in the target inspection area.

[0088] In this embodiment, the first position coordinate is obtained by converting the first position information and the second position information into the fourth position coordinate and the fifth position coordinate of the inspection robot in the target inspection area, respectively, and then performing an average value or a weighted average value calculation. The average value calculation is to add the corresponding components of the fourth position coordinate and the fifth position coordinate and divide by 2, and the weighted average value calculation is to assign different weights according to the reliability or importance of the two coordinates, and then perform a weighted average. The first position coordinate obtained after performing the average value or the weighted average value calculation is used as the current position of the inspection robot, which realizes the fusion of the first position information and the second position information and improves the positioning accuracy of the inspection robot.

[0089] In some embodiments, Figure 1 and Figure 2 As shown, the inspection robot 10 includes a running wheel 410. The step of obtaining the moving distance of the inspection robot includes: obtaining the wheel diameter and the number of rotations of the running wheel; and determining the moving distance of the inspection robot according to the wheel diameter and the number of rotations.

[0090] In this embodiment, the number of rotations can be monitored and recorded by an encoder or sensor that is communicatively connected to the running wheel. After obtaining the wheel diameter and the number of rotations, the moving distance of the inspection robot can be calculated based on the wheel diameter and the number of rotations. Specifically, the moving distance = wheel diameter × π × number of rotations to accurately reflect the actual distance of the inspection robot during movement. The wheel diameter and the number of rotations are stable physical quantities. The method of calculating the moving distance based on the wheel diameter and the number of rotations has high stability, reliability and accuracy. In addition, since the wheel diameter and the number of rotations of the running wheel can be monitored in real time, the real-time performance is better.

[0091] In some embodiments, in combination Figure 1 and Figure 2 As shown, when there are multiple running wheels 410 and the multiple running wheels 410 are the same, it is sufficient to obtain the wheel diameter and the number of rotations of one of the running wheels 410 .

[0092] Optionally, the step of determining the current position of the inspection robot in the target inspection area based on the positioning information and the moving distance includes: converting the positioning information into the first position coordinates of the inspection robot in the target inspection area; calculating the second position coordinates of the inspection robot in the target inspection area based on the moving distance and the target inspection route; calculating the average or weighted average of the first position coordinates and the second position coordinates to obtain the third position coordinates; and using the third position coordinates as the current position of the inspection robot in the target inspection area.

[0093] In this embodiment, the first position coordinate and the second position coordinate are averaged or weighted averaged to fuse the obtained positioning information and the moving distance, and the third position coordinate is accurately obtained, that is, the current position of the inspection robot in the target inspection area is accurately obtained. By combining the positioning information and the moving distance to calculate the current position of the inspection robot, the accuracy of the positioning of the inspection robot is improved, so as to determine whether the inspection robot deviates from the target inspection route, and correct it in time when it deviates from the route, so as to ensure that the inspection robot can accurately move according to the target inspection route, and improve the inspection efficiency and effectiveness.

[0094] Optionally, the step of calculating the second position coordinates of the inspection robot in the target inspection area based on the moving distance and the target inspection route includes: obtaining the starting position coordinates of the inspection robot; calculating the second position coordinates of the inspection robot in the target inspection area based on the starting position coordinates, the moving distance and the target inspection route.

[0095] In this embodiment, when the starting position coordinates, moving distance and target inspection route of the inspection robot are known, the second position coordinates of the inspection robot in the target inspection area can be calculated through path planning and navigation algorithms (such as Dijkstra algorithm, fast random exploration tree algorithm, dynamic window method, etc.), so as to achieve accurate acquisition of the second position coordinates.

[0096] Combination Figure 5 As shown, the embodiment of the present disclosure provides another cruise control method of an inspection robot, comprising:

[0097] S501, the processor determines a target inspection route according to a target inspection area.

[0098] S502, the processor controls the inspection robot to move along the target inspection route.

[0099] S503, during the inspection process, the processor obtains the positioning information of the inspection robot in the target inspection area; and obtains the moving distance of the inspection robot.

[0100] S504: The processor determines the current position of the inspection robot in the target inspection area according to the positioning information and the moving distance.

[0101] S505: When the current position of the processor is on the target inspection route, the processor controls the inspection robot to continue to move along the target inspection route.

[0102] S506: When the current position deviates from the target inspection route, the processor adjusts the motion parameters of the inspection robot so that the inspection robot returns to the target inspection route and continues to move.

[0103] The cruise control method for the inspection robot provided in the embodiment of the present disclosure can control the inspection robot to continue to move along the target inspection route when the current position of the inspection robot is still located in the target inspection route. When the current position of the inspection robot deviates from the target inspection route, the motion parameters of the inspection robot (such as steering angle, acceleration, etc.) are adjusted to guide the inspection robot to return to the target inspection route, and the path correction of the inspection robot is implemented to ensure that the inspection robot always moves along the target inspection route, avoid unnecessary deviations and repeated inspections, improve the efficiency of inspections, and reduce inspection costs. By accurately judging the relationship between the current position of the inspection robot and the target inspection route, the embodiment of the present disclosure can ensure that the inspection robot will not deviate from the target inspection route when performing the inspection task, miss any important areas, and improve the accuracy of the inspection.

[0104] Combination Figure 1 As shown, in some embodiments, the inspection robot 10 includes a sensing mechanism 500. The disclosed embodiment provides another cruise control method of the inspection robot, such as Figure 6 As shown, the cruise control method includes:

[0105] S601, the processor determines a target inspection route according to a target inspection area.

[0106] S602, the processor controls the inspection robot to move along the target inspection route.

[0107] S603, during the inspection process, the processor obtains the positioning information of the inspection robot in the target inspection area; and obtains the moving distance of the inspection robot.

[0108] S604: The processor determines the current position of the inspection robot in the target inspection area according to the positioning information and the moving distance.

[0109] S605: The processor controls the inspection robot to run based on the current position, so that the inspection robot moves along the target inspection route.

[0110] S606, the processor controls the sensing mechanism to detect the detection target and obtain the detection result.

[0111] In this step, the detection target refers to all devices in the target inspection area and located within the target inspection route. The detection target includes the target detection device described in the above embodiment.

[0112] In some embodiments, in combination Figure 1 As shown, the sensing mechanism 500 includes a microphone 522. The steps of controlling the sensing mechanism to detect the detection target and obtaining the detection result include: obtaining environmental data and real-time audio of the detection target during operation; the environmental data includes weather, location and time; determining the target first acoustic model in the acoustic detection model according to the environmental data; wherein the acoustic detection model includes multiple first acoustic models; adjusting the target first acoustic model to be in an activated state, and adjusting other first acoustic models of the non-target first acoustic model to be in a deactivated state; inputting the real-time audio into the adjusted acoustic detection model to obtain the detection result.

[0113] The acoustic detection model is trained in the following manner: a first training audio set is obtained; the first training audio set is an audio data set including normal audio generated during normal operation and abnormal audio generated during abnormal operation of the detection target in the target inspection area; the audio data in the first training audio set is classified according to the labeling information of the audio data to obtain multiple training subsets; the labeling information includes weather, location and time; multiple first acoustic models are trained respectively using multiple training subsets to obtain trained acoustic detection models.

[0114] In this embodiment, the acoustic detection model is trained using the audio generated by the actual operation of the detection target in the target inspection area, that is, the first training audio set. The trained acoustic detection model is more adaptable to the detection target, and the detection accuracy of the acoustic detection model on the running state of the detection target is improved. Before the training, the audio data in the first training audio set is classified according to the label information of the audio data (i.e., weather, location and time) to consider the impact of environmental factors on the acoustic characteristics, so that each training subset can accurately reflect the acoustic characteristics of the detection target running in the target inspection area under a specific environment. In addition, each first acoustic model is optimized for its corresponding specific environment, so as to better capture and identify the acoustic characteristics in the specific environment, improve the pertinence of the acoustic detection model, and further improve the accuracy of the acoustic detection model.

[0115] Optionally, multiple training subsets are used to train multiple first acoustic models respectively, including: extracting features from the audio data in each training subset to obtain Mel-cepstral coefficients corresponding to each audio data; inputting the Mel-cepstral coefficients corresponding to each audio data in each training subset into the corresponding first acoustic model to train the first acoustic model.

[0116] Mel-Frequency Cepstral Coefficients (MFCC) are the coefficients that make up the Mel-Frequency Cepstrum, which is derived from the cepstrum of an audio clip. The Mel-Frequency Cepstrum is a linear transformation of the logarithmic power spectrum based on the nonlinear mel scale of the sound frequencies.

[0117] MFCC uses the Mel scale, which is consistent with the nonlinear frequency perception of the human ear. The frequency bands divided equally on the Mel scale can better approximate the human auditory system, which enables MFCC to more accurately reflect the way the human ear perceives audio signals, thereby providing a feature representation that is more in line with human auditory characteristics in audio classification and recognition, and more accurately capturing the detailed features of audio signals. MFCC has a certain degree of robustness to noise and channel changes, and can maintain good recognition performance in different environments, making MFCC more reliable and stable in audio classification and recognition tasks.

[0118] In this embodiment, the Mel-cepstral coefficients are input into the first acoustic model as the feature representation of each audio data, so that the first acoustic model can learn the features of each audio data by taking advantage of the Mel-cepstral coefficients, thereby improving the accuracy, reliability and stability of the first acoustic model in recognizing the audio data. In this embodiment, the Mel-cepstral coefficients corresponding to each audio data in each training subset are a set, and the Mel-cepstral coefficient set corresponding to each training subset is obtained, and then each Mel-cepstral coefficient set is respectively input into the first acoustic model corresponding to its training subset to train the first acoustic model.

[0119] In a practical application, the steps of extracting features from the audio data in each training subset and obtaining the Mel cepstral coefficients corresponding to each audio data include: preprocessing: including pre-emphasis, framing, windowing, etc., to remove noise, highlight high-frequency signals, and make the audio signal more suitable for subsequent processing; Fourier transform (FFT): transforming the time domain signal to the frequency domain to obtain the spectrum of the audio signal; Mel filter bank: using a group of triangular bandpass filters equidistantly distributed on the Mel scale to filter the spectrum, and the frequency range covered by each filter is approximately a critical bandwidth of the human ear; logarithmic operation: calculating the logarithmic energy of the output of each filter to simulate the nonlinear perception of the human ear to the sound intensity; discrete cosine transform (DCT): performing DCT transform on the logarithmic energy to remove the correlation between the audio signals of each dimension, and reduce the dimension to obtain the final MFCC coefficients.

[0120] Optionally, combined Figure 7 As shown, the first acoustic model includes: a feature extraction module, a learning module, an adaptive average pooling layer and a classification module. The feature extraction module is used to extract features from the input audio data and output first feature data. The learning module is used to perform deep learning on the input first feature data and output second feature data. The adaptive average pooling layer, such as Figure 7 The one-dimensional adaptive average pooling layer (Adaptive-AvgPool-1d) in is used to adjust the feature size of the input second feature data to generate the third feature data. The classification module is used to perform feature reuse and classification on the input third feature data to realize audio data recognition.

[0121] In this embodiment, the feature extraction module is used to capture the long-term information of the input audio data to achieve feature extraction. The learning module achieves deep learning by further extracting and transforming the first feature data. The adaptive average pooling layer can perform adaptive average pooling on the second feature data to generate a third feature data of a fixed size so as to be compatible with the subsequent classification module. The classification module enhances feature reuse and gradient flow through dense connections, that is, the core idea of ​​densely connected networks (DenseNet), improves the learning ability and efficiency of the first acoustic model, and realizes audio data recognition.

[0122] In this embodiment, the first acoustic model mainly combines the characteristics of convolutional neural networks (CNN) and densely connected networks (DenseNet), and realizes feature extraction and classification tasks through multiple levels and modules, forming a powerful network for processing one-dimensional sequence data (i.e., audio data). The first acoustic model not only utilizes the feature extraction capability of convolutional neural networks, but also combines the advantages of densely connected networks, improving its performance in processing complex sequence data, thereby improving the accuracy of its audio data recognition and classification. When the first acoustic model is used to detect the operating status of the detection target, the accuracy is higher.

[0123] Optionally, there are multiple learning modules. By increasing the number of learning modules, the learning ability and efficiency of the first acoustic model are further improved, and the recognition accuracy and reliability of the first acoustic model are improved.

[0124] Optionally, there are multiple classification modules. By increasing the number of classification modules, the learning ability and efficiency of the first acoustic model are further improved, and the recognition accuracy and reliability of the first acoustic model are improved.

[0125] Exemplarily, the number of learning modules is 3 and the number of classification modules is 3.

[0126] Optionally, combined Figure 7 As shown, the feature extraction module includes multiple one-dimensional convolutional layers (Conv1d). In this embodiment, Conv1d can perform convolution operations along one axis of the input data (such as the sequence axis) to capture the local dependencies of the input data, thereby effectively capturing the local features in the one-dimensional data (such as the audio signal) and converting it into a higher-level feature representation. In this embodiment, the input data (i.e., audio data) is subjected to local feature extraction through multiple Conv1ds respectively, and the local features output by each Conv1d are concatenated (Concat) to capture the long-term information of the input audio data, and the concatenated feature data is output as the first feature data.

[0127] Optionally, combined Figure 7 As shown, the learning module includes a convolution block (Conv block) and a one-dimensional average pooling layer (AvgPool-1d). Conv block is used to perform deep learning on the input feature data. AvgPool-1d is used to reduce the dimension of the feature data. In this embodiment, the feature extraction and conversion of the input feature data are realized through the cooperation of Conv block and AvgPool-1d, so as to realize deep learning.

[0128] Optionally, combined Figure 7As shown, the number of convolution blocks is multiple. In this embodiment, the learning ability and efficiency of the learning module are improved by increasing the number of Conv blocks, thereby improving the learning ability and efficiency of the first acoustic model. Exemplarily, the number of convolution blocks is 4.

[0129] Optionally, combined Figure 8 As shown, the convolution block includes a one-dimensional convolution layer (Conv1d). Conv1d is followed by an activation function (in this embodiment, the activation function is a Relu activation function) and regularization (Dropout), and a residual connection is made between the input of the one-dimensional convolution layer and the output of the regularization. In this embodiment, learning of the input feature data is achieved through Conv1d and the Relu activation function. Dropout is used to prevent overfitting. A residual connection is made between the input of Conv1d and the output of Dropout to alleviate the gradient disappearance in the deep network.

[0130] Optionally, combined Figure 7 As shown in the figure, the classification module includes a dense block. Dense Block is a key component in DenseNet, which aims to improve the efficiency of information transmission in the network and the reusability of features. The classification module enhances feature reuse through dense blocks to achieve audio data classification, thereby achieving audio data recognition.

[0131] Optionally, combined Figure 7 As shown, the number of dense blocks is multiple. By increasing the number of dense blocks, the feature reuse strength and classification accuracy of the classification module are enhanced, thereby improving the learning ability and recognition accuracy of the first acoustic model.

[0132] Optionally, combined Fig. 9 As shown, the dense block includes a linear layer (Linear). The Linear is followed by an activation function (in this embodiment, the activation function is a Relu activation function) and regularization (Dropout), and the input of the linear layer is residually connected to the regularized output. In this embodiment, the Linear of the last Dense block is used to convert the features extracted by the previous layer into the final output to achieve classification and recognition of audio data. The Linear of the non-last Dense block layer is used to further convert and combine features to improve the accuracy of subsequent classification.

[0133] In some embodiments, in combination Figure 1As shown, the sensing mechanism 500 includes an image acquisition device 513. The steps of controlling the sensing mechanism to detect the detection target and obtaining the detection result include: controlling the image acquisition device to acquire the image of the detection target to obtain the image to be detected; preprocessing the image to be detected; the preprocessing includes one or more of image enhancement, denoising, graying, and binarization; inputting the preprocessed image to be detected into a pre-trained image detection model to obtain the detection result.

[0134] Optionally, the image detection model includes a YOLO model. The YOLO model is an algorithm that converts the target detection problem into a regression problem, which can simultaneously predict multiple bounding boxes (Bounding Box) and their categories in a single network. The YOLO series of algorithms include multiple versions such as YOLOv1, YOLOv2 (YOLO9000), YOLOv3, YOLOv4 to YOLOv10, each version is improved and optimized based on the previous version, improving the detection speed and accuracy.

[0135] The image detection model is trained in the following manner: training samples are obtained; the training samples are image data of the detection target with annotations, including image data without damage and with damage; more training samples are generated through data enhancement to obtain a training data set; data enhancement includes rotation, scaling, translation, brightness adjustment, contrast adjustment, noise addition, etc.; the training samples in the training data set are input into the image detection model for training to obtain a trained image detection model.

[0136] In this embodiment, the image of the detection target in the target inspection area and the pre-trained image detection model can be used to detect damage on the surface of the detection target, thereby improving the functionality and reliability of the inspection robot. In addition, before the image to be detected is input into the pre-trained image detection model, pre-processing is performed to improve the image quality and contrast, reduce noise and interference, and improve the accuracy and robustness of the image detection model.

[0137] S607: When the detection result is that the detection target is abnormal, the processor obtains the position information of the detection target.

[0138] S608, the processor corrects the target inspection route according to the position information of the detection target, and controls the inspection robot to move according to the corrected target inspection route.

[0139] The cruise control method of the inspection robot provided in the embodiment of the present disclosure can detect all the equipment, i.e., detection targets, encountered along the way when the inspection robot moves along the target inspection route, so as to timely discover potential safety hazards and improve the accuracy and comprehensiveness of the inspection. Among them, when it is found that the detection target is abnormal (such as equipment failure), the location information of the detection target is obtained to increase the detection points and correct the target inspection route. The process of correcting the target inspection route according to the location information of the detection target can be understood as the process of adding detection points and regenerating the target inspection route. Therefore, the process of correcting the target inspection route according to the location information of the detection target can refer to the process of generating the target inspection route in the above embodiment, which will not be repeated here. The corrected target inspection route can cover the abnormal area more accurately, and the inspection robot is controlled to move according to the corrected target inspection route, which further improves the effectiveness and reliability of the inspection.

[0140] Combination Figure 1 As shown, in some embodiments, the inspection robot 10 includes a radar component 517. The disclosed embodiment provides another cruise control method of the inspection robot, such as Fig.10 As shown, the cruise control method includes:

[0141] S101, the processor determines a target inspection route according to a target inspection area.

[0142] S102, the processor controls the inspection robot to move along the target inspection route.

[0143] S103, during the inspection process, the processor obtains the positioning information of the inspection robot in the target inspection area; and obtains the moving distance of the inspection robot.

[0144] S104: The processor determines the current position of the inspection robot in the target inspection area according to the positioning information and the moving distance.

[0145] S105, the processor controls the inspection robot to operate based on the current position, so that the inspection robot moves along the target inspection route.

[0146] S106, the processor controls the radar component to detect obstacles in the traveling direction of the inspection robot to obtain obstacle parameters.

[0147] In this step, obstacle parameters include obstacle volume and obstacle type.

[0148] Optionally, the step of obtaining obstacle parameters includes: controlling a radar component to transmit a radar signal; receiving a reflected signal after the radar signal encounters an obstacle; determining the obstacle volume and obstacle moving speed based on the reflected signal; and determining the obstacle type based on the obstacle moving speed.

[0149] In this embodiment, the phase change of the reflected signal can be analyzed to construct the point cloud data of the obstacle, and the volume of the obstacle can be calculated by analyzing the distribution and shape of the point cloud data. The frequency change of the reflected signal can be analyzed to determine the moving speed of the obstacle, and then the obstacle with a moving speed of 0 is considered a fixed obstacle, and the obstacle with a moving speed greater than 0 is considered a mobile obstacle, so as to determine the obstacle type.

[0150] In some embodiments, in combination Figure 1 As shown, radar component 517 includes single-point laser radar 518 and ultrasonic radar 520. The phase change of the reflected signal of single-point laser radar 518 can be analyzed to construct point cloud data of the obstacle and calculate the volume of the obstacle. The frequency change of the reflected signal of ultrasonic radar 520 can be analyzed to determine the moving speed of the obstacle.

[0151] S107: When the obstacle volume is greater than the volume threshold and the obstacle type is a fixed obstacle, the processor obtains the position information of the obstacle.

[0152] S108, the processor corrects the target inspection route according to the location information of the obstacle, and controls the inspection robot to move along the corrected target inspection route.

[0153] In this step, the process of correcting the target inspection route according to the location information of the obstacle can be understood as deleting the coordinate points corresponding to the location information of the obstacle, adding the coordinate points of the movable area around the obstacle, and regenerating the target inspection route according to the updated coordinate points. Therefore, the process of correcting the target inspection route according to the location information of the obstacle can refer to the process of generating the target inspection route in the above embodiment, and will not be repeated here.

[0154] The cruise control method of the inspection robot provided in the embodiment of the present disclosure can use the radar component to detect obstacles in the direction of travel, and when there are obstacles in the direction of travel of the inspection robot, the obstacle parameters are obtained, including the obstacle volume and obstacle type. If the obstacle volume is greater than the preset volume threshold, and the obstacle type is a fixed obstacle (such as a wall, equipment, etc.), the location information of the obstacle, such as the coordinates of the obstacle, is further obtained. Then, based on the location information of the obstacle, the target inspection route is corrected so that the corrected target inspection route can bypass the obstacle. Then, the inspection robot is controlled to move according to the corrected target inspection route to realize automatic obstacle avoidance of the inspection robot, so that the inspection robot can maintain an efficient and safe operating state in a complex environment, and improve the inspection flexibility.

[0155] In some embodiments, multiple charging base stations are provided in the target inspection area. Fig.11As shown, the embodiment of the present disclosure provides another cruise control method of an inspection robot, comprising:

[0156] S111, the processor determines a target inspection route according to the target inspection area.

[0157] S112, the processor controls the inspection robot to move along the target inspection route.

[0158] S113, during the inspection process, the processor obtains the positioning information of the inspection robot in the target inspection area; and obtains the moving distance of the inspection robot.

[0159] S114, the processor determines the current position of the inspection robot in the target inspection area according to the positioning information and the moving distance.

[0160] S115, the processor controls the operation of the inspection robot based on the current position, so that the inspection robot moves along the target inspection route.

[0161] S116, the processor obtains the remaining power of the inspection robot.

[0162] S117: When the remaining power is less than or equal to the power threshold, the processor obtains the location information of the target charging base station.

[0163] In this step, the target charging base station refers to the charging base station closest to the current position of the inspection robot.

[0164] S118, the processor corrects the target inspection route according to the location information of the target charging base station, and controls the inspection robot to move along the corrected target inspection route.

[0165] In this step, the process of correcting the target inspection route according to the location information of the target charging base station can be understood as the process of adding detection points (location information of the target charging base station) and regenerating the target inspection route. Therefore, the process of correcting the target inspection route according to the location information of the target charging base station can refer to the process of generating the target inspection route in the above embodiment, which will not be repeated here.

[0166] The cruise control method for the inspection robot provided in the embodiment of the present disclosure can obtain the remaining power of the inspection robot in real time, and when the remaining power is less than or equal to the power threshold, obtain the location information of the charging base station closest to the current position of the inspection robot as the location information of the target charging base station. Then, the target inspection route is corrected according to the location information of the target charging base station, so that the corrected target inspection route can pass through the target charging base station so that the inspection robot can be charged. The embodiment of the present disclosure obtains the remaining power of the inspection robot in real time, and promptly corrects the target inspection route to the charging base station when the power is insufficient, so as to avoid the inspection robot interrupting the inspection task due to exhaustion of power, thereby improving the inspection efficiency and endurance, and ensuring that the inspection robot can complete the inspection task for a longer time.

[0167] Combination Fig.12 As shown, the embodiment of the present disclosure provides a cruise control device 120 for an inspection robot, including a determination module 121, a control module 122, an acquisition module 123, an analysis module 124, and an adjustment module 125. The determination module 121 is configured to determine a target inspection route according to a target inspection area; the control module 122 is configured to control the inspection robot to move according to the target inspection route; the acquisition module 123 is configured to obtain positioning information of the inspection robot in the target inspection area during the inspection process; and obtain the movement distance of the inspection robot; the analysis module 124 is configured to determine the current position of the inspection robot in the target inspection area according to the positioning information and the movement distance; the adjustment module 125 is configured to control the operation of the inspection robot based on the current position, so that the inspection robot moves according to the target inspection route.

[0168] The cruise control device 120 of the inspection robot provided in the embodiment of the present disclosure can implement the cruise control method of the inspection robot described in the above embodiments. Therefore, the technical effects possessed by the cruise control method of the inspection robot described in the above embodiments are also possessed by this embodiment and will not be repeated here.

[0169] Optionally, the determination module 121 is further configured to obtain an equipment layout diagram of the target inspection area; and generate a target inspection route of the inspection robot according to the equipment layout diagram.

[0170] Optionally, the determination module 121 is also configured to obtain the operating parameters of the equipment and the environmental parameters of the target inspection area; determine the target detection equipment based on the environmental parameters and the operating parameters; and generate the target inspection route of the inspection robot based on the position coordinates of the target detection equipment in the equipment layout diagram.

[0171] Optionally, the acquisition module 123 is further configured to control the positioning antenna to send a positioning request to the wireless base station; and receive positioning information fed back by the wireless base station based on the positioning request.

[0172] Optionally, the acquisition module 123 is further configured to acquire feedback information of successful docking with the positioning member fed back by the proximity switch; and generate positioning information of the inspection robot according to the feedback information.

[0173] Optionally, the acquisition module 123 is also configured to obtain the current value and current direction fed back by the electromagnetic switch; and to obtain the distance value fed back by the photoelectric switch; when the distance value is less than the distance threshold and the current value is greater than the current threshold, or the distance value is less than the distance threshold and the current direction changes, it is determined that the proximity switch and the positioning member are successfully docked.

[0174] Optionally, the acquisition module 123 is further configured to acquire the wheel diameter and the number of rotations of the walking wheel; and determine the moving distance of the inspection robot according to the wheel diameter and the number of rotations.

[0175] Optionally, the analysis module 124 is also configured to convert the positioning information into the first position coordinates of the inspection robot in the target inspection area; calculate the second position coordinates of the inspection robot in the target inspection area based on the moving distance and the target inspection route; calculate the average or weighted average of the first position coordinates and the second position coordinates to obtain the third position coordinates; and use the third position coordinates as the current position of the inspection robot in the target inspection area.

[0176] Optionally, the adjustment module 125 is also configured to control the inspection robot to continue moving along the target inspection route when the current position is on the target inspection route; and to adjust the motion parameters of the inspection robot so that the inspection robot returns to the target inspection route and continues moving when the current position deviates from the target inspection route.

[0177] In some embodiments, the cruise control device 120 of the inspection robot further includes a correction module 126 .

[0178] Optionally, the correction module 126 is configured to control the sensing mechanism to detect the detection target and obtain the detection result; when the detection result is that the detection target is abnormal, obtain the position information of the detection target; correct the target inspection route according to the position information of the detection target, and control the inspection robot to move according to the corrected target inspection route.

[0179] Optionally, the correction module 126 is configured to control the radar component to detect obstacles in the direction of travel of the inspection robot and obtain obstacle parameters; the obstacle parameters include obstacle volume and obstacle type; when the obstacle volume is greater than the volume threshold and the obstacle type is a fixed obstacle, obtain the location information of the obstacle; correct the target inspection route according to the obstacle location information, and control the inspection robot to move according to the corrected target inspection route.

[0180] Optionally, the correction module 126 is configured to obtain the remaining power of the inspection robot; when the remaining power is less than or equal to the power threshold, obtain the location information of the target charging base station; the target charging base station refers to the charging base station closest to the current position of the inspection robot; according to the location information of the target charging base station, the target inspection route is corrected, and the inspection robot is controlled to move according to the corrected target inspection route.

[0181] Combination Fig.13 As shown, the embodiment of the present disclosure provides a cruise control device 130 for an inspection robot, including a processor 131 and a memory 132. Optionally, the device 130 may also include a communication interface 133 and a bus 134. The processor 131, the communication interface 133, and the memory 132 may communicate with each other through the bus 134. The communication interface 133 may be used for information transmission. The processor 131 may call the logic instructions in the memory 132 to execute the cruise control method for the inspection robot of the above embodiment.

[0182] In addition, the logic instructions in the memory 132 described above may be implemented in the form of software functional units and when sold or used as independent products, may be stored in a computer-readable storage medium.

[0183] The memory 132 is a computer-readable storage medium that can be used to store software programs and computer executable programs, such as program instructions / modules corresponding to the method in the embodiment of the present disclosure. The processor 131 executes the function application and data processing by running the program instructions / modules stored in the memory 132, that is, the cruise control method of the inspection robot in the above embodiment is implemented.

[0184] The memory 132 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory 132 may include a high-speed random access memory and may also include a non-volatile memory.

[0185] An embodiment of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured to execute the cruise control method of the inspection robot.

[0186] The technical solution of the embodiment of the present disclosure can be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiment of the present disclosure. The aforementioned storage medium may be a non-transient storage medium, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes.

[0187] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent only possible changes. Unless explicitly required, separate components and functions are optional, and the order of operation may vary. The parts and features of some embodiments may be included in or replace the parts and features of other embodiments. Moreover, the words used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and the claims, unless the context clearly indicates, the singular forms of "a", "an" and "the" are intended to include plural forms as well. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of listings containing one or more associated ones. In addition, when used in the present application, the term "comprise" and its variants "comprises" and / or comprising refer to the presence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof. In the absence of further restrictions, the elements defined by the sentence "comprising a ..." do not exclude the presence of other identical elements in the process, method or device comprising the elements. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the various embodiments may refer to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can refer to the description of the method part.

[0188] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods for each specific application to implement the described functions, but such implementations should not be considered to exceed the scope of the embodiments of the present disclosure. The technicians may clearly understand that, for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above may refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0189] In the embodiments disclosed herein, the disclosed methods and products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units can be only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to implement this embodiment. In addition, each functional unit in the embodiment of the present disclosure may be integrated in a processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit.

[0190] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A cruise control method for an inspection robot, characterized in that: include: Determine the target inspection route based on the target inspection area; Control the inspection robot to move along the target inspection route; During the inspection process, the positioning information of the inspection robot in the target inspection area is obtained; as well as Get the movement distance of the inspection robot; Determine the current position of the inspection robot in the target inspection area based on the positioning information and the moving distance; Based on the current position, the inspection robot is controlled to move along the target inspection route.

2. The cruise control method according to claim 1, characterized in that: The steps of controlling the inspection robot to move according to the target inspection route based on the current position include: When the current position is on the target inspection route, the inspection robot is controlled to continue to move along the target inspection route; When the current position deviates from the target inspection route, the motion parameters of the inspection robot are adjusted so that the inspection robot returns to the target inspection route and continues to move.

3. The cruise control method according to claim 1 or 2, characterized in that: A plurality of wireless base stations are arranged in the target inspection area, and the inspection robot includes a positioning antenna which is communicatively connected with the wireless base stations; The step of obtaining the positioning information of the inspection robot in the target inspection area includes: controlling the positioning antenna to send a positioning request to the wireless base station; receiving the positioning information fed back by the wireless base station based on the positioning request; and / or, A plurality of positioning parts are arranged in the target inspection area, and the inspection robot includes a proximity switch; the step of obtaining the positioning information of the inspection robot in the target inspection area includes: obtaining feedback information of successful docking with the positioning parts fed back by the proximity switch; and generating the positioning information of the inspection robot according to the feedback information.

4. The cruise control method according to claim 3, characterized in that: Proximity switches include electromagnetic switches and photoelectric switches; The steps to confirm successful docking between the proximity switch and the positioning piece include: Obtaining the current value and current direction fed back by the electromagnetic switch; and Get the distance value fed back by the photoelectric switch; When the distance value is less than the distance threshold and the current value is greater than the current threshold, or when the distance value is less than the distance threshold and the current direction changes, it is determined that the proximity switch is successfully docked with the positioning member.

5. The cruise control method according to claim 1 or 2, characterized in that: The inspection robot includes walking wheels; the step of obtaining the moving distance of the inspection robot includes: Get the wheel diameter and number of rotations of the running wheel; The moving distance of the inspection robot is determined based on the wheel diameter and the number of rotations.

6. The cruise control method according to claim 1 or 2, characterized in that: The step of determining the current position of the inspection robot in the target inspection area according to the positioning information and the moving distance includes: Convert the positioning information into the first position coordinates of the inspection robot in the target inspection area; Calculate the second position coordinates of the inspection robot in the target inspection area according to the moving distance and the target inspection route; Calculate the average value or weighted average value of the first position coordinate and the second position coordinate to obtain a third position coordinate; The third position coordinate is used as the current position of the inspection robot in the target inspection area.

7. The cruise control method according to claim 1 or 2, characterized in that: The inspection robot includes a sensing mechanism; the cruise control method further includes: controlling the sensing mechanism to detect a detection target and obtaining a detection result; when the detection result is that the detection target is abnormal, obtaining position information of the detection target; correcting the target inspection route according to the position information of the detection target, and controlling the inspection robot to move according to the corrected target inspection route; and / or, The inspection robot includes a radar component; the cruise control method further includes: controlling the radar component to detect obstacles in the direction of travel of the inspection robot to obtain obstacle parameters; the obstacle parameters include obstacle volume and obstacle type; when the obstacle volume is greater than a volume threshold and the obstacle type is a fixed obstacle, obtaining the location information of the obstacle; correcting the target inspection route according to the location information of the obstacle, and controlling the inspection robot to move according to the corrected target inspection route; and / or, There are multiple charging base stations set up in the target inspection area; the cruise control method also includes: obtaining the remaining power of the inspection robot; when the remaining power is less than or equal to the power threshold, obtaining the location information of the target charging base station; the target charging base station refers to the charging base station closest to the current position of the inspection robot; according to the location information of the target charging base station, the target inspection route is corrected, and the inspection robot is controlled to move according to the corrected target inspection route.

8. The cruise control method according to claim 1 or 2, characterized in that: According to the target inspection area, the steps to determine the target inspection route include: Obtain the equipment layout diagram of the target inspection area; Generate the target inspection route of the inspection robot according to the equipment layout diagram.

9. The cruise control method according to claim 8, characterized in that: The steps of generating a target inspection route of the inspection robot according to the equipment layout diagram include: Obtain the operating parameters of the equipment and the environmental parameters of the target inspection area; Determine the target detection equipment based on environmental parameters and operating parameters; According to the position coordinates of the target detection device in the equipment layout diagram, a target inspection route for the inspection robot is generated.

10. A cruise control device for an inspection robot, characterized in that: include: A determination module is configured to determine a target inspection route according to a target inspection area; A control module is configured to control the inspection robot to move according to a target inspection route; An acquisition module is configured to acquire positioning information of the inspection robot in the target inspection area during the inspection process; and acquire the movement distance of the inspection robot; An analysis module is configured to determine the current position of the inspection robot in the target inspection area according to the positioning information and the moving distance; The adjustment module is configured to control the operation of the inspection robot based on the current position so that the inspection robot moves according to the target inspection route.

11. A cruise control device for an inspection robot, comprising a processor and a memory storing program instructions, characterized in that: The processor is configured to execute the cruise control method of the inspection robot according to any one of claims 1 to 9 when running the program instructions.

12. A patrol robot, characterized in that: include: Robot body; According to the cruise control device of the inspection robot as described in claim 10 or 11, the cruise control device is installed on the robot body.

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