Unmanned vehicle positioning method, device and equipment and computer readable storage medium

Abstract

The invention discloses an unmanned vehicle positioning method, device and equipment and a computer readable storage medium. According to the method, the distance between the unmanned vehicle and each signal station is obtained according to the signal intensity of m signals sent by the signal station and received by the unmanned vehicle and a distance calculation formula, the signal stations are all arranged in a mining area, and m is a positive integer larger than 2; according to the signal-to-noise ratio of each signal station, endowing each signal station with a corresponding weight; the first coordinate of the unmanned vehicle is obtained according to the distance between the unmanned vehicle and each signal station, the coordinate of each signal station and the weight given to each signal station, and high-precision and high-robustness positioning of the unmanned vehicle in the mining area scene is achieved.

Description

Technical Field

[0001] This application relates to the field of signal positioning, specifically to a method, apparatus, device, and computer-readable storage medium for positioning unmanned vehicles. Background Technology

[0002] With the mining industry prioritizing safe production and efficient operation, and the continuous maturation of technologies such as artificial intelligence, 5G, and sensing, which provide technical support for environmental perception, precise positioning, efficient communication, and intelligent decision-making of unmanned vehicles in mining areas, there is an urgent need in the mining industry for scientific and efficient unmanned vehicle operation methods to promote the intelligent transformation and upgrading of the mining industry, improve the level of mine safety production, reduce operating costs, and increase operational efficiency.

[0003] Among related technologies, the positioning technology that can be used for unmanned vehicles often relies on high-precision hardware (such as dedicated GPS modules), which leads to high costs per vehicle. Furthermore, GPS signal instability is prone to occur in the complex environment of mining areas, making it difficult to support the practical and feasible application of unmanned vehicles in mining areas. Summary of the Invention

[0004] This application provides a method, apparatus, device, and computer-readable storage medium for unmanned vehicle positioning, which can solve the technical problem of inaccurate positioning of unmanned vehicles caused by unstable GPS signals in the prior art.

[0005] In a first aspect, embodiments of this application provide a method for locating an unmanned vehicle, characterized in that the method includes: Based on the signal strength and distance calculation formula of the m signal stations received by the unmanned vehicle, the distance between the unmanned vehicle and each signal station is obtained. The signal stations are all located in the mining area, and m is a positive integer greater than 2. Each signal station is assigned a corresponding weight based on its signal-to-noise ratio. The first coordinates of the autonomous vehicle are obtained based on the distance between the autonomous vehicle and each signal station, the coordinates of each signal station, and the weight assigned to each signal station.

[0006] In conjunction with the first aspect, in one implementation, obtaining the distance between each unmanned vehicle and each signal station includes: The first received by the driverless car Substituting the signal strength of the signal transmitted by each signal station into the distance calculation formula, we obtain the distance between the unmanned vehicle and the first signal station. The distance between signal stations, wherein the distance calculation formula is:

[0007] in, For the unmanned vehicle and the first The distance between signal stations To preset reference distance, For transmitting power, It is Gaussian noise. The first time the unmanned vehicle received The signal strength of the signals transmitted by each signal station This is the path loss index.

[0008] In conjunction with the first aspect, in one implementation, the higher the signal-to-noise ratio of the signal station, the greater the weight assigned to the constraint data of the signal station.

[0009] In conjunction with the first aspect, in one implementation, obtaining the first coordinates of each unmanned vehicle based on the distance between the unmanned vehicle and each signal station, the coordinates of each signal station, and the weight assigned to each signal station includes: Based on the distance between the autonomous vehicle and each signal station, the coordinates of each signal station, and the weight assigned to each signal station, the weighted normal equation is constructed as follows: B Specifically, based on the constraint data weights of each signal station, a diagonal weight matrix W is constructed. The diagonal elements of the k-th row of the weight matrix W represent the weights of the high-weight signal stations in the combinations corresponding to the k-th row of matrix A. The number of rows in W is... ; The k-th row element of coefficient matrix A is the constraint data. , And the number of rows is ,in, Let be the coordinate difference between the i-th and j-th signal stations corresponding to the k-th row. This represents the coordinate difference between the i-th and j-th signal stations corresponding to the k-th row; The k-th row element of the right-hand vector matrix B is ,in, Let i be the distance between the unmanned vehicle and signal station i. Let j be the distance between the unmanned vehicle and signal station j. , Let be the coordinates of the i-th signal station. , Let be the coordinates of the j-th signal station; Solving the weighted normal equation yields the first coordinates of the autonomous vehicle. , ).

[0010] In conjunction with the first aspect, in one implementation, after obtaining the first coordinates of each autonomous vehicle, the method further includes: For each autonomous vehicle, the first coordinates of the previously determined autonomous vehicle and the coordinates of each autonomous vehicle are obtained. Then, the Kalman filter algorithm is used to filter out noisy data to obtain the second coordinates of the autonomous vehicle.

[0011] In conjunction with the first aspect, in one implementation, after obtaining the first coordinates of the unmanned vehicle, the method further includes: Receive a job request, wherein the job request includes the target job location and the maximum waiting time; For each unmanned vehicle, if the unmanned vehicle is in an idle state, the first time required for the unmanned vehicle to reach the target work point from time t is determined based on the target work point location, the first coordinate of the unmanned vehicle, and the preset speed. If the unmanned vehicle is in operation, the unmanned vehicle with the smallest sum of the required second and third time durations will be selected as the target unmanned vehicle. The second time duration is the time required for the unmanned vehicle to complete the remaining tasks, and the third time duration is the time required for the unmanned vehicle to travel from its current location to the target work point after completing the remaining tasks.

[0012] In conjunction with the first aspect, in one embodiment, the method further includes: The remaining battery power of the target unmanned vehicle is detected to determine whether the available working time is greater than the expected working time. If it is greater, the vehicle is identified as the target unmanned vehicle. The working request also includes the expected working time.

[0013] Secondly, embodiments of this application provide an unmanned vehicle positioning device, the unmanned vehicle positioning device comprising: The first calculation module is used to calculate the distance between the unmanned vehicle and each signal station based on the signal strength and distance calculation formula of the signals emitted by m signal stations received by the unmanned vehicle. The signal stations are all arranged in the mining area, and m is a positive integer greater than 2. The weighting module is used to assign corresponding weights to each signal station based on the signal-to-noise ratio of each signal station. The second calculation module obtains the first coordinates of the unmanned vehicle based on the distance between the unmanned vehicle and each signal station, the coordinates of each signal station, and the weight assigned to each signal station.

[0014] Thirdly, embodiments of this application provide an unmanned vehicle positioning device, which includes a processor, a memory, and an unmanned vehicle positioning program stored in the memory and executable by the processor, wherein when the unmanned vehicle positioning program is executed by the processor, it implements the steps of the unmanned vehicle positioning method described in the first aspect.

[0015] Fourthly, embodiments of this application provide a computer-readable storage medium, characterized in that the computer-readable storage medium stores an unmanned vehicle positioning program, wherein when the unmanned vehicle positioning program is executed by a processor, it implements the steps of the unmanned vehicle positioning method as described in the first aspect.

[0016] The beneficial effects of the technical solutions provided in this application include: By calculating the signal strength and distance of the m signal stations received by the unmanned vehicle (UAV) using the formula, the distance between the UAV and each signal station is obtained. The signal stations are all located in the mining area, and m is a positive integer greater than 2. Based on the signal-to-noise ratio of each signal station, a corresponding weight is assigned to each signal station. Based on the distance between the UAV and each signal station, the coordinates of each signal station, and the weight assigned to each signal station, the first coordinates of the UAV are obtained, thus achieving high-precision and robust positioning of the UAV in the mining area scenario. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating an embodiment of the unmanned vehicle positioning method of this application; Figure 2 This is a diagram illustrating vehicle dispatching. Figure 3 This is a schematic diagram of the functional modules of an embodiment of the unmanned vehicle positioning device of this application; Figure 4 This is a schematic diagram of the hardware structure of the unmanned vehicle positioning device involved in the embodiments of this application. Detailed Implementation

[0018] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0020] Firstly, embodiments of this application provide a method for locating unmanned vehicles.

[0021] In one embodiment, reference is made to Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the unmanned vehicle positioning method of this application. Figure 1 As shown, the autonomous vehicle positioning methods include: Step S10: Based on the signal strength and distance calculation formula of the signals emitted by m signal stations received by the unmanned vehicle, the distance between the unmanned vehicle and each signal station is obtained. The signal stations are all arranged in the mining area, and m is a positive integer greater than 2. In one embodiment, due to problems such as dust obstruction and electromagnetic interference in the mining environment, the wireless signals emitted by the signal station are prone to attenuation and distortion during propagation, directly affecting the accuracy of the correspondence between signal strength and distance. Therefore, the signal station in this application is arranged next to the mining area or transportation road. When the unmanned vehicle is operating in the mining area, it receives wireless signals emitted by at least three signal stations in real time, synchronously collects the real-time signal strength of each signal station, and substitutes the real-time signal strength of each signal station into a preset distance calculation formula. The straight-line distance between the unmanned vehicle and the signal station is obtained by calculating the mapping relationship between signal strength and propagation distance through the formula.

[0022] Furthermore, in one embodiment, obtaining the distance between each unmanned vehicle and each signal station includes: The first received by the driverless car Substituting the signal strength of the signal transmitted by each signal station into the distance calculation formula, we obtain the distance between the unmanned vehicle and the first signal station. The distance between signal stations, wherein the distance calculation formula is:

[0023] in, For the unmanned vehicle and the first The distance between signal stations To preset reference distance, For transmitting power, It is Gaussian noise. The first time the unmanned vehicle received The signal strength of the signals transmitted by each signal station This is the path loss index.

[0024] Step S20: Assign a corresponding weight to each signal station based on the signal-to-noise ratio of each signal station; Furthermore, in one embodiment, the higher the signal-to-noise ratio of the signal station, the greater the weight assigned to the constraint data of the signal station.

[0025] In one embodiment, the unmanned vehicle synchronously collects real-time signal-to-noise ratio (SNR) data from each signal station, sampling each station 3-5 times consecutively with a sampling interval of 50ms, and calculates the ratio of the standard deviation to the mean of the sampled data. If the coefficient of variation is ≤0.15, the SNR of the signal station is determined to be stable, and the sample mean is taken as the effective SNR. If the coefficient of variation is >0.15, abnormal sampled values ​​are removed and resampling is performed until the stability requirement is met. Three preset weight intervals are used, corresponding one-to-one with the effective SNR range: if the effective SNR is ≥35dB, the corresponding weight value range is 8-10; if 25dB ≤ effective SNR <35dB, the corresponding weight value range is 4-7; if the effective SNR <25dB, the corresponding weight value range is 1-3. Within the same interval, the larger the SNR, the closer the weight value is to the upper limit of the interval. The effective SNR of each signal station is substituted into the above mapping rule to determine the corresponding weight value. This weight is directly associated with the positioning constraint data of the signal station. That is, in subsequent calculations, the distance constraint of the signal station will participate in the positioning solution according to this weight. In this embodiment, a correspondence between signal station signal quality and weight is established, providing accurate constraint weight support for subsequent weighted least squares positioning.

[0026] Step S30: Based on the distance between the unmanned vehicle and each signal station, the coordinates of each signal station, and the weight assigned to each signal station, the first coordinates of the unmanned vehicle are obtained.

[0027] Further, in one embodiment, obtaining the first coordinates of each unmanned vehicle based on the distance between the unmanned vehicle and each signal station, the coordinates of each signal station, and the weight assigned to each signal station includes: Based on the distance between the autonomous vehicle and each signal station, the coordinates of each signal station, and the weight assigned to each signal station, the weighted normal equation is constructed as follows: B Specifically, a diagonal weight matrix W is constructed based on the constraint data weights of each signal station. The diagonal elements of the k-th row of the weight matrix W represent the weights of the high-weight signal stations in the k-th row combination of the corresponding matrix A. The number of rows in W is... ; The k-th row element of coefficient matrix A is the constraint data. , And the number of rows is ,in, Let be the coordinate difference between the i-th and j-th signal stations corresponding to the k-th row. This represents the coordinate difference between the i-th and j-th signal stations corresponding to the k-th row; The k-th row element of the right-hand vector matrix B is ,in, Let i be the distance between the unmanned vehicle and signal station i. Let j be the distance between the unmanned vehicle and signal station j. , Let be the coordinates of the i-th signal station. , Let be the coordinates of the j-th signal station; Solving the weighted normal equation yields the first coordinates of the autonomous vehicle. , ).

[0028] In one embodiment, firstly, based on the distance between the unmanned vehicle and any two of the at least three signal stations, and the coordinates of the two signal stations, a constraint equation is constructed from the coordinate equation. The coordinate equation is as follows:

[0029] in,( , Let be the coordinates of the i-th signal station. , Given the coordinates of the unmanned vehicle, find ( ). , Then, by selecting any two signal stations i and j and simultaneously solving their coordinate equations, we obtain the constraint equations, which are:

[0030] in,( , Let be the coordinates of signal station i among any two signal stations. , Let be the coordinates of signal station j among any two signal stations. Let i be the distance between the unmanned vehicle and signal station i. To determine the distance between the unmanned vehicle and signal station j, construct a coefficient matrix A: with the following number of rows. The k-th row corresponds to a valid combination of signal stations (the i-th signal station, the j-th signal station), and the elements in this row are the coordinate differences ( - , ),in( , ) represents the preset calibration coordinates of the i-th valid signal station. , (where is the preset calibration coordinate of the j-th valid signal station); construct a diagonal weight matrix W: the number of rows and columns are both 1. The kth diagonal element represents the weight of the high-weight signal station in the combination of the kth row of matrix A. For example, if the kth row of matrix A is... Signal station 2 has a weight of 6, and signal station 3 has a weight of 7. Since signal station 3 has a higher weight in this row combination, the diagonal element of the k-th row of W is the weight of signal station 3 (7), and the off-diagonal elements are 0, ensuring that the constraint of the high-reliability signal station dominates; construct the right-hand vector B: number of rows... The element in the k-th row is .

[0031] Based on the above matrices A, W, and B, construct the weighted normal equation: B, using a regularized inverse algorithm to solve the equation, if the residual r of the calculated solution is less than a preset threshold (e.g., r ≤ 0.5m, which meets the positioning accuracy requirements of unmanned vehicles in the mining area), then the corresponding (X) , () serves as the first coordinate of the driverless car.

[0032] In this embodiment, the distance between the unmanned vehicle and each signal station is obtained by calculating the signal strength and distance of the signals emitted by m signal stations received by the unmanned vehicle. The signal stations are all located in the mining area, and m is a positive integer greater than 2. Each signal station is assigned a corresponding weight based on its signal-to-noise ratio. The first coordinates of the unmanned vehicle are obtained based on the distance between the unmanned vehicle and each signal station, the coordinates of each signal station, and the weight assigned to each signal station. This achieves high-precision and robust positioning of the unmanned vehicle in the mining area scenario.

[0033] Furthermore, in one embodiment, after obtaining the first coordinates of each autonomous vehicle, the method further includes: For each autonomous vehicle, the first coordinates of the previously determined autonomous vehicle and the coordinates of each autonomous vehicle are obtained. Then, the Kalman filter algorithm is used to filter out noisy data to obtain the second coordinates of the autonomous vehicle.

[0034] In one embodiment, the first coordinates of the previously determined unmanned vehicle are combined with the coordinates of each unmanned vehicle, and a Kalman filter algorithm is used to filter out noisy data, dynamically update the position estimate, and suppress noise.

[0035] Furthermore, in one embodiment, after obtaining the first coordinates of the unmanned vehicle, the method further includes: Receive a job request, wherein the job request includes the target job location and the maximum waiting time; For each unmanned vehicle, if the unmanned vehicle is in an idle state, the first time required for the unmanned vehicle to reach the target work point from time t is determined based on the target work point location, the first coordinate of the unmanned vehicle, and the preset speed. If the unmanned vehicle is in operation, the unmanned vehicle with the smallest sum of the required second and third time durations will be selected as the target unmanned vehicle. The second time duration is the time required for the unmanned vehicle to complete the remaining tasks, and the third time duration is the time required for the unmanned vehicle to travel from its current location to the target work point after completing the remaining tasks.

[0036] In one embodiment, since there are multiple scattered work points in the mining area and the unmanned vehicles need to perform diverse tasks, there is a tendency for work points to wait too long and unmanned vehicles to be idle and wasted. Therefore, this step combines the real-time positioning (first coordinate) of the unmanned vehicle with the work status and achieves efficient scheduling through precise duration calculation, which meets the multi-task collaboration needs of the mining area.

[0037] Specifically, such as Figure 2 As shown, Figure 2 This is a schematic diagram of vehicle dispatching. The intelligent roadside system includes various sensors and communication modules for sensing road environment information. The sensors are used to perceive road environment information and transmit it to the unmanned operation vehicles through the communication modules. The communication modules are used to assist the unmanned operation vehicles in obtaining their own precise location information. The dispatch cloud platform is used to obtain real-time information on various operation points, service points, and unmanned operation vehicles in the mining area and to manage them in a coordinated manner according to the operational needs of the mining area. The unmanned operation vehicles include an intelligent driving control system, an on-board communication terminal, and a drive-by-wire chassis. The intelligent driving control system controls the vehicle based on the demand and environmental information obtained from the dispatch cloud platform and the roadside intelligent system. The on-board network terminal is used to interact with the dispatch cloud platform and the intelligent roadside system. First, the system receives the work request from the mining area dispatch cloud platform and extracts core information: the location of the mining point (target work point) and the maximum waiting time (e.g., 15 minutes). Simultaneously, it acquires the real-time working status (idle / working) of all networked unmanned vehicles. Based on the target work point location and the first coordinate of the unmanned vehicle, it calculates the actual travel distance within the mining area between the two points. Combining this with the safe operating speed of the unmanned vehicle in the mining area (preset speed value 3~5 m / s), it obtains the time required to reach the target work point from the current time t. Based on the unmanned vehicle's current work type, remaining workload, and historical work efficiency (e.g., 50 tons / hour, 2 km / hour), it determines the time required to complete the remaining work. Take the endpoint location of the unmanned vehicle after completing the remaining tasks, and calculate the driving distance and corresponding driving time between the two points in the mining area, based on the location of the target work point. Add them together to equal the total time. Select candidate vehicles from all unmanned vehicles whose total time is less than or equal to the maximum waiting time. For idle vehicles, the first time is used as the selection criterion, and for working vehicles, the total time is used as the selection criterion. The unmanned vehicle with the smallest total time after selection is determined as the target unmanned vehicle to ensure the shortest waiting time at the work point and improve the efficiency of mining area operations. Send a dispatch instruction to the target unmanned vehicle, including the location of the target work point and the optimal driving route (based on the first coordinate planning), and simultaneously record it with the dispatch center to achieve a closed-loop connection between positioning and dispatch.

[0038] Furthermore, in one embodiment, the method further includes: The remaining battery power of the target unmanned vehicle is detected to determine whether the available working time is greater than the expected working time. If it is greater, the vehicle is identified as the target unmanned vehicle. The working request also includes the expected working time.

[0039] In one embodiment, due to the wide operating area of ​​the mining area and the concentration of charging facilities in fixed maintenance areas, if the unmanned vehicle stops midway due to insufficient remaining power, it will cause serious problems such as work interruption and road congestion. Therefore, this step adds a power endurance check to the time-based screening to ensure that the target unmanned vehicle can complete the operation completely and meet the endurance guarantee requirements of the mining area; additionally, the expected operation time (i.e., the estimated time required to complete the task at the target operation point) is extracted; the real-time remaining power of the initially determined target unmanned vehicle (e.g., 80%) is obtained, and combined with the energy consumption standard of this vehicle type in the mining area, the remaining power-capable operation time is calculated as: remaining power ÷ energy consumption per unit time; if the remaining power-capable operation time is greater than or equal to the expected operation time, then the unmanned vehicle is directly determined as the final target unmanned vehicle; If the remaining battery power allows for less than the above threshold, then the candidate vehicles will be re-selected: priority will be given to unmanned vehicles with the second shortest total time and remaining battery power allows for more than or equal to the expected working time.

[0040] Secondly, embodiments of this application also provide an unmanned vehicle positioning device.

[0041] In one embodiment, reference is made to Figure 3 , Figure 3 This is a functional module diagram of an embodiment of the unmanned vehicle positioning device of this application. Figure 3 As shown, the unmanned vehicle positioning device includes: The first calculation module 10 is used to obtain the distance between the unmanned vehicle and each signal station according to the signal strength and distance calculation formula of the signals emitted by m signal stations received by the unmanned vehicle. The signal stations are all arranged in the mining area, and m is a positive integer greater than 2. The weighting module 20 is used to assign a corresponding weight to each signal station based on the signal-to-noise ratio of each signal station. The second calculation module 30 obtains the first coordinates of the unmanned vehicle based on the distance between the unmanned vehicle and each signal station, the coordinates of each signal station, and the weight assigned to each signal station.

[0042] Furthermore, in one embodiment, the first computing module 10 is used for: The first received by the driverless car Substituting the signal strength of the signal transmitted by each signal station into the distance calculation formula, we obtain the distance between the unmanned vehicle and the first signal station. The distance between signal stations, wherein the distance calculation formula is:

[0043] in, For the unmanned vehicle and the first The distance between signal stations To preset reference distance, For transmitting power, It is Gaussian noise. The first time the unmanned vehicle received The signal strength of the signals transmitted by each signal station This is the path loss index.

[0044] Furthermore, in one embodiment, the weighting module 20 is used to: The higher the signal-to-noise ratio of a signal station, the greater the weight assigned to the constraint data of the signal station.

[0045] Furthermore, in one embodiment, the second computing module 30 is used for: Based on the distance between the autonomous vehicle and each signal station, the coordinates of each signal station, and the weight assigned to each signal station, the weighted normal equation is constructed as follows: B Specifically, a diagonal weight matrix W is constructed based on the constraint data weights of each signal station. The diagonal elements of the k-th row of the weight matrix W represent the weights of the high-weight signal stations in the k-th row combination of the corresponding matrix A. The number of rows in W is... ; The k-th row element of coefficient matrix A is the constraint data. , And the number of rows is ,in, Let be the coordinate difference between the i-th and j-th signal stations corresponding to the k-th row. This represents the coordinate difference between the i-th and j-th signal stations corresponding to the k-th row; The k-th row element of the right-hand vector matrix B is ,in, Let i be the distance between the unmanned vehicle and signal station i. Let j be the distance between the unmanned vehicle and signal station j. , Let be the coordinates of the i-th signal station. , Let be the coordinates of the j-th signal station; Solving the weighted normal equation yields the first coordinates of the autonomous vehicle. , ).

[0046] Furthermore, in one embodiment, the unmanned vehicle positioning device further includes an acquisition module, used for: For each autonomous vehicle, the first coordinates of the previously determined autonomous vehicle and the coordinates of each autonomous vehicle are obtained. Then, the Kalman filter algorithm is used to filter out noisy data to obtain the second coordinates of the autonomous vehicle.

[0047] Furthermore, in one embodiment, the unmanned vehicle positioning device further includes a receiving module, used for: Receive a job request, wherein the job request includes the target job location and the maximum waiting time; For each unmanned vehicle, if the unmanned vehicle is in an idle state, the first time required for the unmanned vehicle to reach the target work point from time t is determined based on the target work point location, the first coordinate of the unmanned vehicle, and the preset speed. If the unmanned vehicle is in operation, the unmanned vehicle with the smallest sum of the required second and third time durations will be selected as the target unmanned vehicle. The second time duration is the time required for the unmanned vehicle to complete the remaining tasks, and the third time duration is the time required for the unmanned vehicle to travel from its current location to the target work point after completing the remaining tasks.

[0048] Furthermore, in one embodiment, the unmanned vehicle positioning device further includes a determining module, used for: The remaining battery power of the target unmanned vehicle is detected to determine whether the available working time is greater than the expected working time. If it is greater, the vehicle is identified as the target unmanned vehicle. The working request also includes the expected working time.

[0049] The functions of each module in the above-mentioned unmanned vehicle positioning device correspond to the steps in the above-mentioned unmanned vehicle positioning method embodiment, and their functions and implementation processes will not be described in detail here.

[0050] Thirdly, embodiments of this application provide an unmanned vehicle positioning device, which may be a vehicle controller, body controller, or other similar devices.

[0051] Reference Figure 4 , Figure 4 This is a schematic diagram of the hardware structure of the unmanned vehicle positioning device involved in the embodiments of this application. In the embodiments of this application, the unmanned vehicle positioning device may include a processor, a memory, a communication interface, and a communication bus.

[0052] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0053] Communication interfaces include input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting internal components of the autonomous vehicle positioning device, as well as interfaces used for interconnecting the autonomous vehicle positioning device with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0054] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0055] The processor can be a general-purpose processor, which can call the autonomous vehicle localization program stored in the memory and execute the autonomous vehicle localization method provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the autonomous vehicle localization program is called can be referred to in the various embodiments of the autonomous vehicle localization method of this application, and will not be repeated here.

[0056] Those skilled in the art will understand that Figure 4 The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0057] Fourthly, embodiments of this application also provide a computer-readable storage medium.

[0058] The present application has a computer-readable storage medium storing an unmanned vehicle positioning program, wherein when the unmanned vehicle positioning program is executed by a processor, it implements the steps of the unmanned vehicle positioning method described above.

[0059] The method implemented when the unmanned vehicle positioning program is executed can be referred to in various embodiments of the unmanned vehicle positioning method of this application, and will not be repeated here.

[0060] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0061] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0062] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0063] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0064] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0065] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0066] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for locating unmanned vehicles, characterized in that, The unmanned vehicle positioning method includes: Based on the signal strength and distance calculation formula of the m signal stations received by the unmanned vehicle, the distance between the unmanned vehicle and each signal station is obtained. The signal stations are all located in the mining area, and m is a positive integer greater than 2. Each signal station is assigned a corresponding weight based on its signal-to-noise ratio. The first coordinates of the autonomous vehicle are obtained based on the distance between the autonomous vehicle and each signal station, the coordinates of each signal station, and the weight assigned to each signal station.

2. The unmanned vehicle positioning method as described in claim 1, characterized in that, The distance between each unmanned vehicle and each signal station includes: The first received by the driverless car Substituting the signal strength of the signal transmitted by each signal station into the distance calculation formula, we obtain the distance between the unmanned vehicle and the first signal station. The distance between signal stations, wherein the distance calculation formula is: in, For the unmanned vehicle and the first The distance between signal stations To preset reference distance, For transmitting power, It is Gaussian noise. The first time the unmanned vehicle received The signal strength of the signals transmitted by each signal station This is the path loss index.

3. The unmanned vehicle positioning method as described in claim 1, characterized in that, The higher the signal-to-noise ratio of a signal station, the greater the weight assigned to the constraint data of the signal station.

4. The unmanned vehicle positioning method as described in claim 3, characterized in that, The process of obtaining the first coordinates of each unmanned vehicle based on the distance between the unmanned vehicle and each signal station, the coordinates of each signal station, and the weight assigned to each signal station includes: Based on the distance between the autonomous vehicle and each signal station, the coordinates of each signal station, and the weight assigned to each signal station, the weighted normal equation is constructed as follows: B Specifically, based on the constraint data weights of each signal station, a diagonal weight matrix W is constructed. The diagonal elements of the k-th row of the weight matrix W represent the weights of the high-weight signal stations in the combinations corresponding to the k-th row of matrix A. The number of rows in W is... ; The k-th row element of coefficient matrix A is the constraint data. , And the number of rows is ,in, Let be the coordinate difference between the i-th and j-th signal stations corresponding to the k-th row. This represents the coordinate difference between the i-th and j-th signal stations corresponding to the k-th row; The k-th row element of the right-hand vector matrix B is ,in, Let i be the distance between the unmanned vehicle and signal station i. Let j be the distance between the unmanned vehicle and signal station j. , Let be the coordinates of the i-th signal station. , Let be the coordinates of the j-th signal station; Solving the weighted normal equation yields the first coordinates of the autonomous vehicle. , ).

5. The unmanned vehicle positioning method as described in claim 1, characterized in that, After obtaining the first coordinates of each driverless vehicle, the following is also included: For each autonomous vehicle, the first coordinates of the previously determined autonomous vehicle and the coordinates of each autonomous vehicle are obtained. Then, the Kalman filter algorithm is used to filter out noisy data to obtain the second coordinates of the autonomous vehicle.

6. The unmanned vehicle positioning method as described in claim 1, characterized in that, After obtaining the first coordinates of the driverless car, the following is also included: Receive a job request, wherein the job request includes the target job location and the maximum waiting time; For each unmanned vehicle, if the unmanned vehicle is in an idle state, the first time required for the unmanned vehicle to reach the target work point from time t is determined based on the target work point location, the first coordinate of the unmanned vehicle, and the preset speed. If the unmanned vehicle is in operation, the unmanned vehicle with the smallest sum of the required second and third time durations will be selected as the target unmanned vehicle. The second time duration is the time required for the unmanned vehicle to complete the remaining tasks, and the third time duration is the time required for the unmanned vehicle to travel from its current location to the target work point after completing the remaining tasks.

7. The unmanned vehicle positioning method as described in claim 6, characterized in that, The method further includes: The remaining battery power of the target unmanned vehicle is detected to determine whether the available working time is greater than the expected working time. If it is greater, the vehicle is identified as the target unmanned vehicle. The working request also includes the expected working time.

8. A positioning device for an unmanned vehicle, characterized in that, The unmanned vehicle positioning device includes: The first calculation module is used to calculate the distance between the unmanned vehicle and each signal station based on the signal strength and distance calculation formula of the signals emitted by m signal stations received by the unmanned vehicle. The signal stations are all arranged in the mining area, and m is a positive integer greater than 2. The weighting module is used to assign corresponding weights to each signal station based on the signal-to-noise ratio of each signal station. The second calculation module obtains the first coordinates of the unmanned vehicle based on the distance between the unmanned vehicle and each signal station, the coordinates of each signal station, and the weight assigned to each signal station.

9. A positioning device for unmanned vehicles, characterized in that, The unmanned vehicle positioning device includes a processor, a memory, and an unmanned vehicle positioning program stored in the memory and executable by the processor, wherein when the unmanned vehicle positioning program is executed by the processor, it implements the steps of the unmanned vehicle positioning method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores an unmanned vehicle positioning program, wherein when the unmanned vehicle positioning program is executed by a processor, it implements the steps of the unmanned vehicle positioning method as described in any one of claims 1 to 7.

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