Mobile parking platform applied to indoor inspection unmanned aerial vehicle and control method

Abstract

The invention provides a mobile parking platform applied to an indoor inspection unmanned aerial vehicle and a control method. The mobile parking platform comprises a platform body; the energy storage unit is located on the platform body and used for storing electric energy; the charging unit is located on the platform body and used for charging the unmanned aerial vehicle; the walking unit is located on the platform body and used for driving the platform body to walk; and the controller is in communication connection with the energy storage unit, the charging unit and the walking unit, the controller is used for controlling the walking unit to work under the condition that the unmanned aerial vehicle needs to be charged, so that the mobile parking platform walks to the position of the unmanned aerial vehicle, and the controller is further used for controlling the energy storage unit to discharge and controlling the charging unit to work, so that the unmanned aerial vehicle is charged. According to the scheme, the problem of short endurance time of the inspection unmanned aerial vehicle in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of drone charging technology, and more specifically, to a mobile docking platform for indoor inspection drones, a control method for the mobile docking platform for indoor inspection drones, a control device for the mobile docking platform for indoor inspection drones, and an indoor charging system. Background Art

[0002] An inspection drone is an unmanned aerial vehicle that is specially designed to perform various inspection tasks. It is usually equipped with various sensors and cameras, which can fly in the air and monitor, record and transmit data of the target area in real time. These drones can be used for many different inspection tasks, such as inspection of facilities such as buildings, bridges, power lines, pipelines, wind turbines, as well as environmental monitoring, security patrols, etc. Their use can improve efficiency, reduce costs, and provide safer and more reliable inspection solutions.

[0003] However, for indoor inspections such as explosive warehouses, traditional flying drones are usually not suitable, because the indoor space is usually small and there are many obstacles, and the flight of drones may be restricted, posing safety risks. Moreover, due to the particularity of the goods stored in explosive warehouses, the accidental fall of inspection drones when the battery runs out may cause greater safety risks. Therefore, at this stage, ground mobile or crawling inspection robots are still widely used for inspections of explosive warehouses.

[0004] With the development of related technologies, some manufacturers have developed some flying drones specially designed for indoor environments, such as mini quadcopters, which are usually smaller and lighter than traditional large drones and can fly in relatively narrow spaces. These drones are usually used for tasks such as photography, surveillance and security inspections in indoor environments. However, the above drones also have the problem of short flight time. Summary of the invention

[0005] The main purpose of the present application is to provide a mobile docking platform for indoor inspection drones, a control method for a mobile docking platform for indoor inspection drones, a control device for a mobile docking platform for indoor inspection drones, and an indoor charging system, so as to at least solve the problem of short flight time of inspection drones in the prior art.

[0006] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a mobile docking platform for indoor inspection drones is provided, including: a platform body; an energy storage unit, located on the platform body, and the energy storage unit is used to store electric energy; a charging unit, located on the platform body, and the charging unit is used to charge the drone; a walking unit, located on the platform body, and the walking unit is used to drive the platform body to walk; a controller, which is respectively communicated with the energy storage unit, the charging unit and the walking unit, and the controller is used to control the walking unit to work when the drone needs to be recharged, so that the mobile docking platform walks to the position of the drone, and the controller is also used to control the discharge of the energy storage unit and control the charging unit to work, so as to charge the drone.

[0007] Optionally, the walking unit includes: a walking wheel, used to drive the platform body to walk; a servo motor, which is communicatively connected to the controller and electrically connected to the walking wheel, and the servo motor is used to drive the walking wheel to walk.

[0008] Optionally, the mobile docking platform further includes: an image acquisition device for acquiring images inside the explosive warehouse.

[0009] According to another aspect of the present application, a control method for a mobile docking platform of any one of the above-described indoor inspection drones is provided, the method comprising: obtaining the remaining power of the drone; when the remaining power is less than a preset power threshold, controlling the mobile docking platform to move to the location of the drone; and controlling the mobile docking platform to discharge so as to charge the drone.

[0010] Optionally, controlling the mobile docking platform to move to the position of the UAV includes: obtaining a first position of the mobile docking platform; obtaining a second position of the UAV; taking the first position as a starting point and the second position as an end point, and using a path planning algorithm to determine an optimal path between the starting point and the end point, wherein the path planning algorithm includes one or more of an A* algorithm, a Dijkstra algorithm, and a Bellman-Ford algorithm; and controlling the mobile docking platform to move to the position of the UAV along the optimal path.

[0011] Optionally, the mobile docking platform also includes an image acquisition device, which is used to acquire images inside the explosive warehouse. During the process of controlling the mobile docking platform to move to the position of the drone along the optimal path, the method further includes: acquiring the images inside the explosive warehouse in real time during the process of controlling the mobile docking platform to move; performing visual analysis on the images to determine whether the optimal path includes obstacles; and if the optimal path includes the obstacles, updating the optimal path to obtain an updated optimal path.

[0012] Optionally, controlling the mobile docking platform to move to the position of the drone includes: obtaining a first speed of the drone; and using a second speed to control the mobile docking platform to move to the position of the drone, wherein the second speed is greater than the first speed.

[0013] Optionally, controlling the mobile docking platform to discharge so as to charge the drone includes: obtaining a target distance, wherein the target distance is the distance between the first position and the second position; when the target distance is less than a preset distance threshold, sending a hovering command to the drone and obtaining a final position of the drone, wherein the hovering command is used to control the drone to maintain a flight state in which the spatial position remains unchanged; controlling the mobile docking platform to walk to the final position, and controlling the mobile docking platform to discharge so as to charge the drone.

[0014] According to another aspect of the present application, there is provided a control device for a mobile docking platform of any one of the above-described indoor inspection drones, the device comprising: a first acquisition unit for acquiring the remaining power of the drone; a first control unit for controlling the mobile docking platform to move to the location of the drone when the remaining power is less than a preset power threshold; and a second control unit for controlling the mobile docking platform to discharge so as to charge the drone.

[0015] According to another aspect of the present application, an indoor charging system is provided, which includes: a drone; a mobile docking platform, wherein the mobile docking platform is any one of the mobile docking platforms applied to indoor inspection drones; and a processor, which is respectively communicated with the drone and the mobile docking platform, and the processor is used to execute any one of the control methods for the mobile docking platform applied to indoor inspection drones.

[0016] Applying the technical solution of the present application, a mobile docking platform is designed. When the drone needs to be charged, the mobile docking platform moves to the location of the drone and charges the drone, so that the drone can be fully replenished without leaving the inspection route, avoiding repeated returns, increasing the effective inspection time, and improving the inspection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 A schematic diagram showing the positional relationship between the drone and the mobile docking platform;

[0019] Figure 2 The overall structural schematic diagram of the mobile docking platform is shown;

[0020] Figure 3 A side structural schematic diagram of a mobile docking platform is shown;

[0021] Figure 4 A front structural schematic diagram of a mobile docking platform is shown;

[0022] Figure 5 A hardware structure block diagram of a mobile terminal for executing a control method for a mobile docking platform applied to an indoor inspection drone provided in an embodiment of the present application is shown;

[0023] Figure 6 A flow chart of a control method for a mobile docking platform of an indoor inspection drone provided in accordance with an embodiment of the present application is shown;

[0024] Figure 7 A structural block diagram of a control device for a mobile docking platform of an indoor inspection drone provided in accordance with an embodiment of the present application is shown;

[0025] Figure 8 A schematic diagram of the structure of an indoor charging system is shown.

[0026] The above drawings include the following reference numerals:

[0027] 102, processor; 104, memory; 106, transmission device; 108, input and output device; 10, platform body; 11, charging unit; 12, drone; 13, chassis; 14, running wheels; 15, servo motor; 16, image acquisition device; DETAILED DESCRIPTION

[0028] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0029] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] When using small drones for indoor inspections in existing explosive warehouses, the problem of frequent charging may arise. In the existing solutions, the fixed docking stations commonly used in the existing solutions require the drones to return to the docking station for charging during use, regardless of their location, which actually makes the inspection efficiency relatively low. The existing docking stations for temporary docking and recharging of drones are all fixed, and there are no mobile docking stations in the existing technology.

[0032] As introduced in the background technology, the inspection drones in the prior art have a short flight time. To solve the above problems, the embodiments of the present application provide a mobile docking platform for indoor inspection drones, a control method for the mobile docking platform for indoor inspection drones, a control device for the mobile docking platform for indoor inspection drones, and an indoor charging system.

[0033] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0034] This application provides a mobile docking platform for indoor inspection drones, such as Figure 1 , Figure 2 , Figure 3 and Figure 4 As shown, including:

[0035] Platform body 10;

[0036] An energy storage unit, located on the platform body, and used to store electrical energy;

[0037] A charging unit 11, located on the platform body, and used to charge the drone 12;

[0038] A walking unit, located on the platform body, and used to drive the platform body to walk;

[0039] The controller is respectively connected to the energy storage unit, the charging unit and the walking unit for communication. When the UAV needs to be recharged, the controller controls the walking unit to operate so that the mobile docking platform moves to the location of the UAV. The controller is also used to control the energy storage unit to discharge and the charging unit to operate so as to charge the UAV.

[0040] In the above system, a mobile docking platform is designed. When the drone needs to be charged, the mobile docking platform moves to the location of the drone and charges the drone, so that the power of the drone can be fully replenished without leaving the inspection route, avoiding repeated returns, increasing the effective inspection time, and improving the inspection efficiency.

[0041] Specifically, compared with fixed-point docking stations, the AGV car dynamically moves to the location of the drone that needs charging to perform charging operations, so that the power of the drone can be fully replenished without leaving the inspection route, avoiding repeated returns, increasing the effective inspection time, and improving inspection efficiency.

[0042] Specifically, Figure 1 , Figure 2 , Figure 3 and Figure 4 As shown, the mobile docking platform includes a chassis 13, on which an energy storage unit for energy storage can be carried, and the energy storage unit includes an energy storage battery. The energy storage unit can be a wireless charging plate, and the wireless charging plate can be used for the drone to dock and charge.

[0043] Specifically, the platform body is made of high-strength aluminum alloy material to ensure sufficient load-bearing capacity and structural stability while maintaining lightweight. Its appearance is designed as a flat rectangle, which is easy to move in narrow warehouse aisles.

[0044] Specifically, the energy storage unit consists of a group of high-performance lithium-ion batteries with a total capacity of 5000mAh, which can provide a complete charging cycle for at least two drones. The battery pack uses an intelligent power management system to ensure that the battery life is maximized when charging the drone.

[0045] Specifically, the charging unit is a wireless charging pad that uses the Qi wireless charging standard and has a maximum output power of 50W, ensuring that the drone can be quickly recharged in a short period of time. The charging pad is equipped with a precise positioning sensor that can automatically adjust its position to match the drone's charging point for efficient charging.

[0046] In the specific implementation process, Figure 1 , Figure 2 , Figure 3 and Figure 4 As shown, the walking unit includes walking wheels 14 and a servo motor 15, the walking wheels are used to drive the platform body to walk; the servo motor is communicatively connected to the controller and electrically connected to the walking wheels, and the servo motor is used to drive the walking wheels to walk.

[0047] In this solution, the walking unit moves precisely in the warehouse according to the instructions of the controller, ensuring the efficiency and safety of the mobile docking platform. This design allows the platform to move flexibly in complex environments, providing timely charging services for drones, and greatly improving the efficiency of indoor inspection tasks.

[0048] Specifically, the walking unit includes four independently driven universal wheels (i.e., walking wheels), which are controlled by servo motors and can achieve 360-degree free steering. The walking unit is also integrated with obstacle avoidance sensors such as ultrasonic radar and infrared sensors, as well as cameras for environmental recognition, to ensure safe and efficient movement in the complex environment of the warehouse.

[0049] Specifically, four running wheels are installed at the bottom of the platform body, each wheel is made of high-performance materials, such as polyurethane, to ensure wear resistance and low noise. The running wheels adopt a double-layer design, with the outer layer being a soft material to reduce vibration when moving on hard ground, and the inner layer being a hard material to provide the structural strength of the wheel.

[0050] Specifically, the traveling wheels may be designed as universal wheels, and the four wheels thereof may be independently steerable, which enables the platform to move freely in any direction without complicated steering operations.

[0051] Specifically, the wheel size and load capacity: the wheel diameter is designed to be 20cm and the wheel width is 5cm, ensuring good grip on different ground materials. The overall load capacity is designed to be 100kg, which is enough to carry the weight of the platform body and the drone.

[0052] Specifically, each traveling wheel is connected to a servo motor, which uses a high-torque, low-noise DC motor and can provide precise speed and steering control.

[0053] Specifically, the servo motor is connected to a central controller through a programmable controller (such as a PLC or microcontroller). The controller sends speed and direction instructions to the servo motor through a CAN bus or similar communication protocol to ensure precise control.

[0054] Specifically, the servo motor is embedded with position and speed sensors, which can provide real-time feedback of the actual position and speed of the motor to the controller. The controller adjusts the drive instructions of the motor according to the feedback information to achieve closed-loop control and ensure the stability and precise movement of the platform.

[0055] Specifically, the central controller uses a path planning algorithm (such as the A* algorithm) to calculate the optimal path based on the received drone location information and the preset map in the warehouse. After the path calculation is completed, the controller sends instructions to the servo motor of the walking unit through the communication protocol to execute the movement. The walking unit is also equipped with an ultrasonic sensor and a visual obstacle avoidance system. When the sensor detects an obstacle, the controller will immediately adjust the motor drive command so that the platform can bypass the obstacle and avoid collision. The controller can dynamically adjust the drive speed of the servo motor based on the speed information of the drone to ensure that the platform approaches the drone at an appropriate speed, while controlling the direction of the platform to move along the calculated path.

[0056] In the specific implementation process, Figure 1 , Figure 2 , Figure 3 and Figure 4 As shown, the mobile docking platform further includes an image acquisition device 16, which is used to acquire images in the explosive warehouse.

[0057] In this solution, the image acquisition equipment not only enhances the obstacle avoidance capability and environmental adaptability of the mobile docking platform, but also provides additional safety monitoring means for inspection tasks, further improving the efficiency and safety of warehouse inspections.

[0058] Specifically, the image acquisition device includes a high-definition camera. The platform is equipped with two high-definition cameras as image acquisition devices, one of which is a wide-angle lens for providing a panoramic vision of the warehouse, and the other is a zoom lens for close observation and identification of details in the warehouse.

[0059] Specifically, the camera is selected with high resolution (at least 1080p) and wide-angle field of view (more than 120 degrees) to ensure that it can clearly capture images of the surrounding environment. At the same time, in order to adapt to the possible low-light environment in the warehouse, the camera also has night vision function and built-in infrared LED fill light to ensure clear images in the dark.

[0060] Specifically, the image acquisition device is tightly integrated with the control system of the mobile docking platform, and through real-time image analysis, obstacles in the warehouse are identified and tracked. This visual obstacle avoidance function is based on a deep learning algorithm that can automatically learn and understand the warehouse environment and improve the platform's autonomous obstacle avoidance capabilities.

[0061] Specifically, in addition to obstacle avoidance, image acquisition equipment is also used to monitor the status of the warehouse, including monitoring the integrity of the shelves, the stacking of goods, and whether there are any abnormal conditions in the warehouse, such as fire, leakage, etc., which provides an additional source of information for warehouse safety management.

[0062] Specifically, the image acquisition device can also assist in the positioning of the drone. Through visual feature matching, it can provide more accurate drone location information, enhance the positioning accuracy between the drone and the mobile docking platform, and ensure that the drone can accurately dock on the charging platform.

[0063] Specifically, the controller on the platform integrates an image processing and analysis system, which uses advanced computer vision algorithms to analyze images captured by image acquisition devices in real time. This includes functions such as target detection, obstacle recognition, and environmental monitoring.

[0064] Specifically, in order to improve the accuracy and reliability of image analysis, the platform uses pre-trained deep learning models that have been trained on a large amount of warehouse environment image data. These models can automatically identify common warehouse obstacles and abnormal situations and reduce false alarm rates.

[0065] Specifically, the platform is also equipped with a large-capacity storage device that can save the collected image data for subsequent analysis. At the same time, the image data and analysis results can be transmitted back to the inspection control center in real time through wireless communication technology (such as 4G / 5G network or Wi-Fi), providing support for real-time monitoring and management of the warehouse.

[0066] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 5 1 is a hardware structure block diagram of a mobile terminal for controlling a mobile docking platform of an indoor inspection drone according to an embodiment of the present invention. Figure 5 As shown, the mobile terminal may include one or more ( Figure 5 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 5The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 5 More or fewer components as shown, or with Figure 5 Different configurations are shown.

[0067] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the display method of device information in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific examples of the network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0068] In this embodiment, a control method for a mobile docking platform for indoor inspection drones running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0069] Figure 6 FIG. 1 is a flow chart of a control method for a mobile docking platform of an indoor inspection drone according to an embodiment of the present application. Figure 6 As shown, the method comprises the following steps:

[0070] Step S201, obtaining the remaining power of the drone;

[0071] Specifically, the voltage, current and temperature of the drone battery are continuously monitored to estimate the remaining power. This information is sent to the controller of the docking platform through the drone. During the inspection process, the drone automatically reports the power information to the controller at regular intervals (such as every minute) to achieve continuous power monitoring.

[0072] Step S202, when the remaining power is less than a preset power threshold, controlling the mobile docking platform to move to the location of the UAV;

[0073] Specifically, when a signal is received that the remaining power of the drone is lower than a preset power threshold (for example, lower than 20%), the docking procedure is immediately started. First, real-time map data of the warehouse is obtained from the inspection control center, including shelf layout, channel conditions, and obstacle locations. Then, the current precise location of the docking platform is determined using the positioning equipment on the platform (such as GPS, indoor positioning system). After obtaining the drone location and its own location information, a path planning algorithm (such as the A* algorithm) is used to calculate the shortest and safest path from the current position of the docking platform to the drone location. Path planning also takes into account the moving speed, dynamic obstacles in the warehouse (such as staff, other mobile devices), and possible charging time to ensure that the platform can efficiently reach the drone location without affecting the normal operation of the warehouse.

[0074] Step S203, controlling the mobile docking platform to discharge so as to charge the drone.

[0075] Specifically, after the docking platform arrives near the location of the drone, the image acquisition device is used to further accurately locate the drone to ensure the precise alignment of the wireless charging plate and the drone charging port. The visual module on the docking platform, through the binocular camera system, can build a three-dimensional model of the drone's docking area, calculate the relative position and angle, and ensure efficient wireless charging. Once the drone is accurately aligned with the charging plate, the controller of the docking platform starts the discharge of the energy storage unit and charges the drone through the charging unit. During the charging process, the controller continuously monitors the drone's power status to ensure that charging automatically stops when the power is fully replenished to avoid the risk of battery damage caused by overcharging.

[0076] Through this embodiment, when the drone needs to be charged, the mobile docking platform is controlled to move to the location of the drone and charge the drone, so that the power of the drone can be fully replenished without leaving the inspection route, avoiding repeated returns, increasing the effective inspection time, and improving the inspection efficiency.

[0077] Specifically, when estimating the remaining battery capacity, the remaining capacity can be estimated by measuring the voltage of the battery. There is usually a certain relationship between the voltage of the battery and its remaining capacity, which can be estimated based on the voltage and a known battery characteristic curve. If it is determined that the remaining power is lower than a threshold, for example, lower than 20%, it is necessary to control the sending of the current location information to the mobile docking station.

[0078] Specifically, the execution subject of the above solution can be a controller, or a processor, a server, etc., and the specific execution subject is not limited.

[0079] During the specific implementation process, controlling the above-mentioned mobile docking platform to move to the position of the above-mentioned UAV can be achieved through the following steps: obtaining the first position of the above-mentioned mobile docking platform; obtaining the second position of the above-mentioned UAV; taking the above-mentioned first position as the starting point and the above-mentioned second position as the end point, and using a path planning algorithm to determine the optimal path between the above-mentioned starting point and the above-mentioned end point, wherein the above-mentioned path planning algorithm includes one or more of the A* algorithm, the Dijkstra algorithm, and the Bellman-Ford algorithm; controlling the above-mentioned mobile docking platform to move to the position of the above-mentioned UAV according to the above-mentioned optimal path.

[0080] In this solution, the mobile docking platform can intelligently plan the path and safely approach the drone during the inspection process, providing timely charging services, greatly improving the efficiency and safety of drone inspections. The platform can flexibly respond to complex and changing warehouse environments through real-time location information updates and dynamic path optimization, ensuring mission continuity and equipment safety.

[0081] Specifically, the platform uses a positioning system based on Wi-Fi signal strength, Bluetooth beacon positioning, or visual SLAM (Simultaneous Localization and Mapping) technology to obtain its precise location in real time. The drone continuously monitors and records its real-time location, and sends it to the controller of the docking platform along with the remaining battery information. The controller can update the drone's location information in real time to ensure accurate path planning.

[0082] Specifically, after receiving the location information, the controller uses the preloaded warehouse map data, which contains the warehouse layout information, obstacle locations, and possible paths of dynamic obstacles (such as other mobile devices or workers). The controller uses the A* algorithm, a widely used pathfinding algorithm that combines the traversal of the Dijkstra algorithm and the efficiency of heuristic search, to calculate a shortest path that avoids obstacles, starting from the first location and ending at the second location.

[0083] Specifically, the controller can select the most suitable path planning algorithm based on the complexity and real-time changes of the warehouse. For example, in a static warehouse environment, the A* algorithm may be most effective; while in a dynamic environment, it may be necessary to combine the Bellman-Ford algorithm to consider the impact of dynamic obstacles.

[0084] Specifically, the controller sends instructions to the servo motor of the walking unit according to the calculated optimal path to start the platform moving. The four universal wheels of the walking unit can independently turn and adjust the speed according to the instructions of the controller to accurately execute the planned route.

[0085] Specifically, during the movement, the image acquisition device continuously captures environmental images and detects obstacles and dynamic changes through computer vision algorithms to ensure safety. Once an unexpected obstacle is detected, the controller immediately adjusts the walking path to ensure that the platform can safely avoid the obstacle and continue to the drone location.

[0086] Specifically, the mobile docking platform monitors the change of the second position of the drone in real time while heading to the drone's location. If the drone adjusts its position during the inspection, the controller will update the path planning in real time to ensure that the platform can always approach the drone along the optimal path.

[0087] Specifically, the controller can also predict the future position of the drone based on the drone's real-time speed and altitude information, adjust the platform's movement speed in advance, ensure that the platform can reach its location before the drone's battery runs out, and provide timely charging services.

[0088] Specifically, when the mobile docking platform approaches the drone, a distance sensor (such as an infrared or ultrasonic sensor) monitors the distance between the drone and the drone. Once a preset safe distance is reached, the controller sends a command to the drone, requiring it to hover in order to dock accurately.

[0089] Specifically, the charging unit of the platform automatically aligns with the charging port of the drone, and the energy storage unit begins to discharge, replenishing the drone with power through wireless or contact charging technology. During this process, the communication between the platform and the drone continues to ensure the smooth progress of the charging process.

[0090] In the specific implementation process, the above-mentioned mobile docking platform also includes an image acquisition device, and the above-mentioned image acquisition device is used to collect images inside the explosive warehouse. In the process of controlling the above-mentioned mobile docking platform to walk to the position of the above-mentioned UAV along the above-mentioned optimal path, the above-mentioned method also includes the following steps: in the process of controlling the above-mentioned mobile docking platform to walk, the above-mentioned images inside the above-mentioned explosive warehouse are acquired in real time; visual analysis is performed on the above-mentioned images to determine whether the above-mentioned optimal path includes obstacles; if the above-mentioned obstacles are included in the above-mentioned optimal path, the above-mentioned optimal path is updated to obtain an updated optimal path.

[0091] In this solution, in the complex environment of the explosives warehouse, obstacles are detected in real time and the optimal path is dynamically updated, ensuring the safe and efficient movement of the platform to the drone location. The tight integration of the image acquisition device and the controller not only improves the autonomy and safety of the platform, but also provides additional monitoring means for warehouse management, enhancing the overall safety management level. This dynamic path planning and obstacle detection capability is not available on fixed docking platforms, greatly improving the efficiency and safety of drone inspections.

[0092] Specifically, when the drone begins to move toward its location, the image acquisition device on the mobile docking platform continues to work, capturing real-time images of the environment in the warehouse. These images are sent to the docking platform controller in real time, ensuring that the controller can obtain the latest environmental information in a timely manner.

[0093] Specifically, after receiving the image, the controller immediately starts the image processing and analysis process. Advanced computer vision algorithms such as target detection, moving object recognition and semantic segmentation are used to classify and locate objects in the image. In particular, the algorithm is able to identify static obstacles (such as shelves, equipment) and dynamic obstacles (such as moving staff, other inspection robots). Through visual analysis, it is determined whether there are new obstacles or changes in the position of obstacles in the current optimal path.

[0094] Specifically, when an obstacle is detected, the controller immediately updates the current optimal path. A path planning algorithm (such as the A* algorithm) is used to recalculate the path to ensure that obstacles are avoided. When updating the path, the controller considers the type, size, and dynamic characteristics of the obstacle, as well as the size, speed, and steering capabilities of the mobile docking platform to find a path that is both safe and efficient. The updated optimal path is sent to the servo motor of the walking unit in real time to adjust the platform's moving direction and speed.

[0095] Specifically, in addition to detecting current obstacles, it is also possible to predict potential dynamic obstacles based on image analysis and machine learning algorithms. For example, by analyzing the movement patterns of people in the warehouse, the controller can predict their possible routes in advance, thereby preventively planning the path of the mobile docking platform to avoid future collision risks.

[0096] Specifically, during the movement, the image acquisition device continuously monitors the surrounding environment to ensure safety. If new obstacles or environmental changes are found during the movement, the platform will immediately stop moving, re-perform visual analysis and path planning, and continue moving after ensuring safety. In addition, image data can be used to monitor the safety status of the warehouse, such as monitoring whether unauthorized personnel enter, or detecting potential safety hazards (such as leaks, fires, etc.).

[0097] In some embodiments, controlling the mobile docking platform to move to the position of the UAV can be achieved through the following steps: obtaining a first speed of the UAV; using a second speed to control the mobile docking platform to move to the position of the UAV, wherein the second speed is greater than the first speed.

[0098] In this solution, the mobile docking platform can respond faster and quickly approach the drone based on the drone's speed information, providing timely charging services for the drone, greatly improving the continuity and efficiency of the drone's inspection mission.

[0099] Specifically, when the drone is performing an inspection mission, it will continuously monitor its first speed (i.e., flight speed). This information is crucial for planning the movement of the docking platform. When the remaining power of the drone is lower than the preset threshold, the drone's real-time position and first speed information will be sent to the controller of the mobile docking platform.

[0100] Specifically, after the controller of the mobile docking platform receives the first speed information sent by the drone, it immediately starts the quick response program. The controller calculates the distance from the current first position of the docking platform to the second position of the drone, and determines a reasonable second speed based on the first speed of the drone and the distance between the platform and the drone. This second speed is set to be greater than the first speed of the drone, ensuring that the docking platform can quickly approach the drone and reduce the time the drone hovers and waits due to low battery.

[0101] Specifically, in order to achieve efficient movement, the walking unit (i.e., servo motor and walking wheel) of the docking platform operates at a second speed. The servo motor accurately controls the rotation speed of the walking wheel according to the speed command sent by the controller to ensure that the platform can move quickly at the set second speed. At the same time, considering the complexity and safety of the warehouse environment, the controller will monitor the environment around the platform in real time, obtain real-time images in the warehouse through image acquisition equipment, and perform visual analysis to ensure that no obstacles will be encountered or safety risks will be generated when moving at a faster speed.

[0102] Specifically, during the movement, the docking platform controller continuously receives the real-time position and first speed information of the drone. If the drone adjusts its flight path or speed, the controller will immediately update its movement strategy and adjust the second speed and walking path to maintain a quick response to the drone. This dynamic speed adjustment capability enables the docking platform to quickly and accurately reach the drone's location when the drone's flight speed and position change.

[0103] Specifically, when the docking platform approaches the drone and reaches a preset safe distance, the controller sends a hovering command to the drone. At the same time, the platform slows down for precise alignment to ensure accurate docking between the wireless charging plate and the drone's charging port. Once the alignment is complete, the energy storage unit begins to discharge and the drone is charged through the charging unit. During the charging process, the docking platform continues to monitor the surrounding environment to ensure the safety of the charging process.

[0104] In some embodiments, controlling the mobile docking platform to discharge so as to charge the drone can be specifically achieved through the following steps: obtaining a target distance, wherein the target distance is the distance between the first position and the second position; when the target distance is less than a preset distance threshold, sending a hovering command to the drone and obtaining the final position of the drone, wherein the hovering command is used to control the drone to maintain a flight state in which the spatial position remains unchanged; controlling the mobile docking to move to the final position, and controlling the mobile docking platform to discharge so as to charge the drone.

[0105] In this solution, the mobile docking platform can accurately measure the distance between it and the drone. When the distance reaches the preset threshold, it can safely align with the drone and charge it through hovering instructions and final position confirmation. This precise distance detection and control capability ensures the safety and efficiency of the charging process, while reducing the time the drone interrupts its inspection mission due to return charging, thereby improving the overall efficiency of the inspection mission.

[0106] Specifically, after the controller of the mobile docking platform determines the first position of the platform and the second position of the drone, it calculates the straight-line distance between the two points, i.e., the target distance. The controller uses built-in distance sensors (such as ultrasonic sensors, infrared ranging sensors, or lidar) that can provide high-precision distance measurements, ensuring that the platform can adjust its movement strategy based on real-time distance information.

[0107] Specifically, once the target distance is calculated, the controller compares it with a preset distance threshold. The preset distance threshold is determined based on the safe charging distance between the drone and the docking platform and the minimum adjustment distance of the platform movement. For example, the threshold can be set to 2 meters to ensure a safe distance between the two during charging while allowing the platform to make subtle position adjustments. If the target distance is less than the preset threshold, the controller determines that it is close to the completion stage and begins to prepare to send a hovering command to the drone.

[0108] Specifically, after determining that the target distance is less than the threshold, the controller sends a hovering command to the drone through the second communication unit, requiring the drone to enter a flight state that maintains a constant spatial position. After receiving the command, the drone's flight control unit will adjust its flight attitude and propulsion to stabilize the hover. At the same time, the drone will continuously update and send its final position information to the controller of the docking platform to ensure that the platform can track the precise position of the drone.

[0109] Specifically, the docking platform accurately controls the walking unit at a lower speed (usually less than 0.5 m / s) to move to the final position of the drone based on the final position information obtained. In order to ensure the safety and efficiency of the charging process, the docking platform will continue to use image acquisition equipment for visual assistance while moving to ensure the accurate alignment of the charging plate and the drone charging port. Once the docking platform reaches the final position and completes the alignment, the controller starts the discharge of the energy storage unit and provides charging services for the drone through the charging unit. During the charging process, the platform's controller continuously monitors the drone's power status to ensure that charging is complete.

[0110] Specifically, when the drone is fully charged, it sends a charging completion signal to the controller of the docking platform. After receiving the signal, the controller stops discharging the energy storage unit and sends a separation command to the drone through the communication unit. The drone resumes normal flight mode from the hovering state, separates from the docking platform, and continues to perform its inspection mission. The docking platform returns to the standby position to prepare for the next charging service.

[0111] The embodiment of the present application also provides a control device for a mobile docking platform applied to an indoor inspection drone. It should be noted that the control device for a mobile docking platform applied to an indoor inspection drone in the embodiment of the present application can be used to execute the control method for a mobile docking platform applied to an indoor inspection drone provided in the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and those that have been described will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0112] The following introduces the control device of the mobile docking platform of the indoor inspection drone provided in the embodiment of the present application.

[0113] Figure 7 1 is a structural block diagram of a control device for a mobile docking platform of an indoor inspection drone according to an embodiment of the present application. Figure 7 As shown, the device comprises:

[0114] The first acquisition unit 100 is used to acquire the remaining power of the drone;

[0115] The first control unit 200 is used to control the mobile docking platform to move to the position of the UAV when the remaining power is less than a preset power threshold;

[0116] The second control unit 300 is used to control the mobile docking platform to discharge so as to charge the drone.

[0117] Through this embodiment, when the drone needs to be charged, the mobile docking platform is controlled to move to the location of the drone and charge the drone, so that the power of the drone can be fully replenished without leaving the inspection route, avoiding repeated returns, increasing the effective inspection time, and improving the inspection efficiency.

[0118] In the specific implementation process, the first control unit includes a first acquisition module, a second acquisition module, a determination module and a first control module, the first acquisition module is used to obtain the first position of the above-mentioned mobile docking platform; the second acquisition module is used to obtain the second position of the above-mentioned UAV; the determination module is used to take the above-mentioned first position as the starting point and the above-mentioned second position as the end point, and use a path planning algorithm to determine the optimal path between the above-mentioned starting point and the above-mentioned end point, wherein the above-mentioned path planning algorithm includes one or more of the A* algorithm, the Dijkstra algorithm, and the Bellman-Ford algorithm; the first control module is used to control the above-mentioned mobile docking platform to walk to the position of the above-mentioned UAV according to the above-mentioned optimal path.

[0119] In this solution, the mobile docking platform can intelligently plan the path and safely approach the drone during the inspection process, providing timely charging services, greatly improving the efficiency and safety of drone inspections. The platform can flexibly respond to complex and changing warehouse environments through real-time location information updates and dynamic path optimization, ensuring mission continuity and equipment safety.

[0120] In the specific implementation process, the above-mentioned mobile docking platform also includes an image acquisition device, and the above-mentioned image acquisition device is used to collect images inside the explosive warehouse. The above-mentioned device also includes a second acquisition unit, a determination unit and an update unit. The second acquisition unit is used to control the above-mentioned mobile docking platform to walk to the position of the above-mentioned drone along the above-mentioned optimal path, and in the process of controlling the above-mentioned mobile docking platform to walk, to obtain the above-mentioned images inside the above-mentioned explosive warehouse in real time; the determination unit is used to perform visual analysis on the above-mentioned images to determine whether the above-mentioned optimal path includes obstacles; the second acquisition unit is used to update the above-mentioned optimal path when the above-mentioned optimal path includes the above-mentioned obstacles, so as to obtain the updated optimal path.

[0121] In this solution, in the complex environment of the explosives warehouse, obstacles are detected in real time and the optimal path is dynamically updated, ensuring the safe and efficient movement of the platform to the drone location. The tight integration of the image acquisition device and the controller not only improves the autonomy and safety of the platform, but also provides additional monitoring means for warehouse management, enhancing the overall safety management level. This dynamic path planning and obstacle detection capability is not available on fixed docking platforms, greatly improving the efficiency and safety of drone inspections.

[0122] In some embodiments, the first control unit includes a third acquisition module and a second control module, the third acquisition module is used to obtain the first speed of the above-mentioned UAV; the second control module is used to use a second speed to control the above-mentioned mobile docking platform to move to the position of the above-mentioned UAV, wherein the above-mentioned second speed is greater than the above-mentioned first speed.

[0123] In this solution, the mobile docking platform can respond faster and quickly approach the drone based on the drone's speed information, providing timely charging services for the drone, greatly improving the continuity and efficiency of the drone's inspection mission.

[0124] In some embodiments, the second control unit includes a fourth acquisition module, a fifth acquisition module and a third control module, the fourth acquisition module is used to acquire the target distance, wherein the target distance is the distance between the first position and the second position; the fifth acquisition module is used to send a hovering command to the UAV and acquire the final position of the UAV when the target distance is less than a preset distance threshold, wherein the hovering command is used to control the UAV to maintain a flight state in which the spatial position remains unchanged; the third control module is used to control the mobile docking to walk to the final position, and control the mobile docking platform to discharge so as to charge the UAV.

[0125] In this solution, the mobile docking platform can accurately measure the distance between it and the drone. When the distance reaches the preset threshold, it can safely align with the drone and charge it through hovering instructions and final position confirmation. This precise distance detection and control capability ensures the safety and efficiency of the charging process, while reducing the time the drone interrupts its inspection mission due to return charging, thereby improving the overall efficiency of the inspection mission.

[0126] The control device for the mobile docking platform of the indoor inspection drone includes a processor and a memory. The first acquisition unit, the first control unit, the second control unit, etc. are stored in the memory as program units, and the processor executes the program units stored in the memory to implement the corresponding functions. The modules are all located in the same processor; or, the modules are located in different processors in any combination.

[0127] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and the problem of short flight time of the inspection drone in the prior art can be solved by adjusting the kernel parameters.

[0128] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0129] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the control method for the mobile docking platform applied to indoor inspection drones.

[0130] An embodiment of the present invention provides a processor, which is used to run a program, wherein the program executes the control method for the mobile docking platform applied to indoor inspection drones when running.

[0131] An embodiment of the present invention provides a device, the device includes a processor, a memory, and a program stored in the memory and executable on the processor, and when the processor executes the program, at least the control method steps for a mobile docking platform for indoor inspection drones are implemented. The device in this article can be a server, a PC, a PAD, a mobile phone, etc.

[0132] A computer program product includes a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the control method of the mobile docking platform applied to indoor inspection drones in various embodiments of the present application are implemented.

[0133] The present application provides an indoor charging system, the charging system comprising a drone, a mobile docking platform and a processor. The mobile docking platform is any one of the mobile docking platforms for indoor inspection drones; the processor is respectively connected to the drone and the mobile docking platform for communication, and the processor is used to execute any one of the control methods for the mobile docking platform for indoor inspection drones.

[0134] In another embodiment of the present application, a mobile docking station is provided, which is a ground-moving AGV trolley. Similar to a general AGV trolley, it includes a walking mechanism and a charging platform mounted on the walking mechanism for docking a drone. The walking mechanism is different in that the mobile includes a chassis, walking wheels, and a servo motor. The chassis can carry a main power supply for the movement of the trolley and path planning, as well as an energy storage module, i.e., an energy storage battery, for charging and replenishing energy to the drone. The charging platform itself is a wireless charging plate, which can be used for the drone to dock and charge and replenish energy.

[0135] like Figure 8 As shown, the applicable UAV includes a flight control unit, a first positioning unit, a charge and discharge management unit and a first communication unit, wherein the first communication unit is configured to communicate with the inspection control center and the mobile docking station, and the first positioning unit is specifically a Bluetooth positioning module, which performs Bluetooth positioning in cooperation with the Bluetooth beacon in the warehouse to obtain the real-time location information of the UAV in the warehouse. The charge and discharge management unit is used to obtain the battery remaining information in real time, and when the battery remaining reaches the preset value, the current location information, path information and current speed information are sent to the mobile docking station. Specifically, when estimating the battery remaining capacity, the remaining capacity can be estimated by measuring the battery voltage. There is usually a certain relationship between the voltage of the battery and its remaining capacity, which can be estimated based on the voltage and the known battery characteristic curve. If it is judged that the remaining power is lower than the threshold, for example, lower than 20%, it is necessary to control the sending of the current location information to the mobile docking station. At this time, the mobile docking station will perform the following steps:

[0136] 1) Obtain a preset map of the warehouse, mark the current location information and path information of the drone on the preset map, and construct a cost map. The cost map is a grid map, and each grid unit represents an area in the warehouse environment and obstacles in the area;

[0137] 2) Using the current location information of the drone as the end point, and obtaining the current second location information of the mobile stop through the second positioning unit as the starting point, the starting point and the end point are marked on the above cost map;

[0138] 3) In the cost map, calculate the cost value of each grid cell. The cost value can be determined based on the type of grid cell (such as obstacle, free space) and other factors (such as terrain, dynamic obstacles);

[0139] 4) Use the path planning algorithm (A* algorithm) to search for the best path from the starting point to the end point in the cost map. The algorithm evaluates and selects the path based on the cost value in the cost map;

[0140] 5) Convert the selected path into the robot's motion instructions and execute the path; during the execution process, obtain the latest position information of the drone in real time, and determine whether the stop has arrived on the drone inspection path. If so, obtain the estimated position of the drone in real time based on the path information and speed information fed back by the drone, and go to the estimated position of the drone at a second speed greater than the drone speed based on the drone inspection path information;

[0141] 6) Determine the distance between the docking station and the drone based on its own position. When the distance is less than the preset value, send a hovering command to the drone and obtain the final position information of the drone;

[0142] 7) When the drone reaches its final location, send a charging command.

[0143] In addition, the parking station of the present invention also includes a visual module, which is used to collect environmental information and specifically includes two high-definition cameras to form binocular vision. The specific steps include:

[0144] 1. First, stereo vision matching is performed on the images obtained by the two cameras to obtain the depth information of the objects in the environment. The goal of stereo vision matching is to find the correspondence between the corresponding pixels in the image to calculate the position of the object in three-dimensional space.

[0145] 2. Then, through stereo vision matching, the depth information of the object is obtained to generate a depth map. The depth map shows the distance from each pixel in the environment to the camera and provides information about the position and shape of the object.

[0146] 3. Then, based on the depth map, obstacles in the environment are detected and segmented. Image segmentation algorithms or object detection algorithms can be used to identify obstacle areas in the depth map and extract their position and shape information.

[0147] 4. Finally, based on the location and shape information of the obstacles, an obstacle avoidance strategy is developed. This may include adjusting the robot's speed and direction to avoid obstacles or bypass them to ensure that the robot can safely pass through the environment.

[0148] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0149] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0150] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0151] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0152] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0153] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0154] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0155] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0156] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0157] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A mobile docking platform for indoor inspection drones, characterized in that: include: Platform body; An energy storage unit, located on the platform body, and used to store electrical energy; A charging unit, located on the platform body, and used to charge the drone; A walking unit, located on the platform body, and used to drive the platform body to walk; The controller is respectively connected to the energy storage unit, the charging unit and the walking unit for communication. The controller is used to control the walking unit to operate when the UAV needs to be recharged, so that the mobile docking platform moves to the location of the UAV. The controller is also used to control the energy storage unit to discharge and control the charging unit to operate, so as to charge the UAV.

2. The mobile docking platform according to claim 1, characterized in that: The walking unit comprises: Walking wheels, used to drive the platform body to walk; The servo motor is communicatively connected with the controller and electrically connected with the walking wheel, and the servo motor is used to drive the walking wheel to walk.

3. The mobile docking platform according to claim 1, characterized in that: The mobile docking platform also includes: Image acquisition equipment is used to capture images inside explosive warehouses.

4. A control method for a mobile docking platform for an indoor inspection drone according to any one of claims 1 to 3, characterized in that: The method comprises: Obtaining the remaining power of the drone; When the remaining power is less than a preset power threshold, controlling the mobile docking platform to move to the location of the UAV; The mobile docking platform is controlled to discharge so as to charge the drone.

5. The method according to claim 4, characterized in that Controlling the mobile docking platform to move to the position of the drone includes: Acquire a first position of the mobile docking platform; Obtaining a second position of the drone; Taking the first position as a starting point and the second position as an end point, a path planning algorithm is used to determine an optimal path between the starting point and the end point, wherein the path planning algorithm includes one or more of an A* algorithm, a Dijkstra algorithm, and a Bellman-Ford algorithm; The mobile docking platform is controlled to move to the position of the UAV according to the optimal path.

6. The method according to claim 5, characterized in that The mobile docking platform further includes an image acquisition device, which is used to acquire images in the explosive warehouse. In the process of controlling the mobile docking platform to walk to the position of the drone along the optimal path, the method further includes: In the process of controlling the movement of the mobile docking platform, acquiring the image in the explosive warehouse in real time; Performing visual analysis on the image to determine whether the optimal path includes an obstacle; When the optimal path includes the obstacle, the optimal path is updated to obtain an updated optimal path.

7. The method according to claim 5, characterized in that Controlling the mobile docking platform to move to the position of the drone includes: Obtaining a first speed of the drone; The mobile docking platform is controlled to move to the position of the UAV using a second speed, wherein the second speed is greater than the first speed.

8. The method according to claim 5, characterized in that Controlling the mobile docking platform to discharge so as to charge the drone includes: Acquire a target distance, wherein the target distance is a distance between the first position and the second position; When the target distance is less than a preset distance threshold, a hovering instruction is sent to the drone, and a final position of the drone is obtained, wherein the hovering instruction is used to control the drone to maintain a flight state in which the spatial position remains unchanged; The mobile docking device is controlled to move to the final position, and the mobile docking platform is controlled to discharge so as to charge the drone.

9. A control device for a mobile docking platform of an indoor inspection drone according to any one of claims 1 to 3, characterized in that: The device comprises: A first acquisition unit, used to acquire the remaining power of the drone; A first control unit, configured to control the mobile docking platform to move to the position of the UAV when the remaining power is less than a preset power threshold; The second control unit is used to control the mobile docking platform to discharge so as to charge the drone.

10. An indoor charging system, characterized in that: The charging system comprises: Drones; A mobile docking platform, wherein the mobile docking platform is a mobile docking platform for indoor inspection drones as described in any one of claims 1 to 3; A processor is respectively connected to the UAV and the mobile docking platform for communication, and the processor is used to execute the control method for the mobile docking platform of the indoor inspection UAV according to any one of claims 4 to 5.

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