A collaborative communication and positioning system and method for unmanned mining excavators and mining trucks

Abstract

This invention discloses a collaborative communication and positioning system and method for unmanned mining excavators and mining trucks, belonging to the technical field of collaborative operation of multiple unmanned systems. It includes a communication and navigation integrated module, a sensing module, an edge computing module, an energy supply module, a safety and protection module, and a remote and terminal user interface. Through large-scale, high-capacity, scalable communication networking and deployment in large-scale mining scenarios, relative positioning of unmanned excavators and mining trucks based on a communication and navigation network model, a high-precision collaborative positioning method for unmanned excavators and mining trucks combining communication and navigation integration and digital-analog integration, and high-precision collaborative positioning of unmanned excavators and mining trucks using vehicle-road collaboration, it achieves collaborative communication and high-precision positioning between unmanned excavators and mining trucks in complex mining operation scenarios.

Description

Technical Field

[0001] This invention relates to the field of collaborative positioning technology for unmanned mining systems, and in particular to a collaborative communication and positioning system and method between a mining excavator and a mining truck. Background Technology

[0002] In modern mining operations, unmanned equipment, such as unmanned excavating robots and mining trucks, is increasingly being used to improve efficiency and safety. High-precision collaborative positioning enables efficient coordination between unmanned excavating robots and mining trucks, reducing waiting time and idle time, accelerating material transfer, and significantly improving mine production efficiency. However, achieving efficient and accurate communication and positioning between equipment in complex electromagnetic environments remains a major challenge. Existing technologies struggle to meet the requirements of large-scale, high-capacity, and high-precision environments, especially in urban buildings and mining environments where multipath effects are severe and non-line-of-sight (NLOS) errors are significant. When excavating robots operate in complex environments such as urban canyons and mines, GPS and 5G signals are unstable. Using LiDAR and cameras to extract features from the surrounding environment to determine the location of collaborative work objects also faces challenges such as large mining areas with indistinct features and unstable dynamic mining characteristics. With advancements in vehicle-to-vehicle (V2V) technology, it is expected to be applied to excavators and trucks for collaborative positioning.

[0003] In the area of ​​excavator-mining truck collaborative positioning, current solutions primarily rely on RTK positioning devices installed on both the excavator and the mining truck for collaborative operation. Chinese Utility Model CN202121962283.X discloses a bucket wheel excavator-truck collaborative loading alignment device, which utilizes high-precision GNSS positioning sensors to collect position information of the bucket wheel excavator and the mining truck, directly completing the loading process. Chinese Invention CN202110958558.0 discloses a bucket wheel excavator-truck collaborative loading alignment device and method, which uses high-precision GNSS positioning sensors to collect position information of the bucket wheel excavator and the truck, determining whether the excavator is unloading based on their positions and whether the truck is full based on the height of the material on the truck. Chinese Invention CN202110857651.2 discloses a method and main control device for docking an excavator and a mining truck, where an infrared transmitter is installed at the front of the excavator and an infrared receiver is installed at the rear of the mining truck. The infrared signal between the excavator and the mining truck indicates docking between the mining truck and the excavator, achieving automatic loading. Summary of the Invention

[0004] The purpose of this invention is to provide an airborne unmanned aerial vehicle (UAV) system and collaborative operation method for unmanned excavators. By stitching together multiple UAVs to form a dense map displaying information within the work area, the system guides multiple unmanned excavators to enter the site for operation. Through optimal scheduling of multi-UAV collaborative charging and vertical landing methods, the system enables real-time monitoring of the unmanned excavator's work area and operation status by multiple UAVs, thereby improving the operational efficiency and intelligence level of multiple unmanned excavators and UAVs in mines and construction sites, while also increasing the construction safety of unmanned excavators.

[0005] To achieve the above objectives, a collaborative communication and positioning system for mining excavators and mining trucks is provided, characterized by comprising a communication and navigation integrated module, a sensing module, an edge computing module, an energy supply module, a safety and protection module, and a remote and terminal user interface.

[0006] The integrated communication and navigation module includes an on-board communication and navigation unit and a roadside communication and navigation unit.

[0007] The vehicle-mounted communication unit, installed on the excavating robot and mining truck, is used to enable direct communication between vehicles (V2V) and communication with infrastructure (V2I). It also combines a multi-band GNSS receiver and RTK (Real-Time Kinematic) technology to obtain the vehicle's precise geographical location using the GNSS receiver and, in conjunction with an inertial navigation system (INS), to provide continuous location updates in the event of poor signal.

[0008] The roadside communication unit is deployed in key locations in the mine, such as intersections and loading / unloading areas, to communicate with vehicles and provide roadside wireless positioning information, communication relay, traffic information, warning messages, etc.

[0009] The sensing module includes an on-board sensing unit and a roadside sensing unit.

[0010] The vehicle-mounted sensing unit includes sensors such as vehicle-mounted LiDAR, cameras, and millimeter-wave radar, which are used for vehicle-mounted environmental perception and identification positioning. This helps the excavator identify and locate mining trucks, obstacles, other vehicles, pedestrians, etc., and provides necessary data support for the collaborative positioning of unmanned excavators and mining trucks.

[0011] The roadside perception unit includes roadside LiDAR, cameras, millimeter-wave radar and other sensors, which are used for roadside environmental perception and identification and positioning. At the same time, it can identify and locate unmanned excavators, mining trucks, obstacles, other vehicles, pedestrians and other objects, providing necessary data support for the autonomous driving of unmanned excavators.

[0012] The edge computing module is installed in unmanned excavators, mining trucks, and roadside facilities to process data from sensors, execute decision-making algorithms, control vehicle operation, and manage communication with other vehicles and infrastructure.

[0013] The energy supply module is installed on fixed facilities such as roadside units, with solar panels installed to supply power to roadside equipment.

[0014] The security and protection module includes a physical protection structure that provides dustproof, waterproof, and shockproof functions for key electronic components; and network security measures that implement technologies such as encrypted communication and authentication to prevent unauthorized access and data leakage.

[0015] The remote and terminal user interface includes a remote monitoring center, through a dedicated software platform, which allows managers to monitor the status of all vehicles such as unmanned excavators and mining trucks in real time, schedule tasks, and respond to emergencies; the vehicle-mounted display screen provides location and communication information for the unmanned excavators.

[0016] A collaborative communication and positioning method for mining excavation robots and mining trucks includes the following steps:

[0017] S1. Large-scale, high-capacity, integrated, scalable communication network and deployment for large-scale mining scenarios;

[0018] S2. Relative positioning of unmanned excavator and mining truck based on communication and navigation network model;

[0019] S3, a high-precision collaborative positioning method for unmanned excavators and mining trucks that integrates communication and navigation and combines digital and analog models;

[0020] S4, Vehicle-Road Cooperative Unmanned Excavator - Mining Truck Cooperative High-Precision Positioning.

[0021] In step S1, the specific process is as follows:

[0022] S11. Establish a transmission model for communication and positioning signals: Based on the integrated communication and navigation module, using sparse signal representation, construct an overcomplete sub-dictionary of communication signals and interference through the K-SVD (K-Singular Value Decomposition) algorithm, and use the OMP (Orthogonal Matching Pursuit) algorithm to achieve signal separation and reconstruction, so as to quickly switch the positioning data channel to the optimal channel; use the optimal redundant data packet allocation strategy to allocate data packets among multiple relay nodes to ensure that data packets are transmitted in the optimal way and reduce the packet loss rate; through highly reliable multi-relay node redundant data packet forwarding, the cooperation of relay nodes is used to achieve redundant forwarding of data packets and improve the reliability of data transmission.

[0023] S12. Construct a PNT (Positioning, Navigation, and Timing) network protocol suite: Design a scalable, large-space positioning, navigation, and timing (PNT) network protocol suite under positioning performance constraints to transmit spatiotemporal references between multiple devices and ensure spatiotemporal consistency among them.

[0024] S13. Design an integrated communication and navigation network protocol: Utilize a rapid switching and optimal redundant data packet allocation strategy for positioning data channels to quickly switch positioning data channels and allocate data packets to achieve optimal transmission performance; employ a highly reliable multi-relay node data packet redundancy forwarding mechanism to achieve redundant forwarding of data packets through relay node cooperation, thereby improving the reliability of data transmission; and utilize an integrated communication and navigation network congestion control method to establish a network congestion control strategy to manage network traffic, prevent network congestion, and ensure communication quality.

[0025] S14. Construct an integrated communication and navigation network: Utilize vehicle-mounted wireless network equipment, based on the vehicle-mounted wireless network equipment of unmanned excavators and mining trucks, to construct a large-space, high-capacity, scalable, and dynamically networked integrated communication and navigation network; using dynamic networking technology, the constructed network needs to be able to dynamically adjust to adapt to constantly changing network conditions and user needs.

[0026] In step S2, the specific process is as follows:

[0027] S21. Complex Scene Communication and Navigation Signal Quality Detection and Compensation: Integrating multi-mode signals such as BDS (BeiDouNavigationSystem), 5G, and UWB, data fusion between BDS and UWB is performed. Leveraging the high-precision ranging characteristics of UWB and the wide coverage capability of BDS, more accurate positioning is achieved. A multipath fading detection and non-line-of-sight error compensation mechanism for wireless signals in all-weather complex environments such as urban buildings and mines is designed. Simulation analysis using the UWB channel model IEEE802.15.4a is used to estimate the mean and variance of the additional delay in non-line-of-sight environments. Measurement values ​​are corrected using probability theory, and collaborative positioning is achieved by combining the Chan algorithm and particle swarm optimization algorithm to improve positioning accuracy.

[0028] S22. Model-Based Vehicle Cooperative Localization: Based on the initial positioning results of BDS, and the short-range wireless communication and high-precision ranging characteristics provided by UWB carried by the unmanned excavator and the mining truck, the observation information between the unmanned excavator and the mining truck is fused and calculated. Through tight or deep coupling, the data from GNSS and INS (Inertial Navigation System) are further fused to improve the accuracy and reliability of positioning.

[0029] In step S3, the specific process is as follows:

[0030] S31. A scene-adaptive positioning method jointly driven by data and models: Based on model-based collaborative positioning, the onboard communication and navigation network modules of unmanned excavators and mining trucks are automatically networked and collect data from different scenes. Based on a data-driven wireless sensor network collaborative positioning method, a scene model and data-driven communication and navigation network networking positioning strategy is constructed to achieve scene-adaptive positioning through data-model collaboration. The data-driven method uses machine learning algorithms to optimize the positioning process, improving positioning accuracy and robustness.

[0031] S32. Cooperative positioning of unmanned excavators and mining trucks: Constructing three-dimensional scene information as the information source of comprehensive PNT, providing comprehensive PNT support at different levels, including low-level scene spatial structure information, texture features, topological road network, etc. to improve positioning capabilities, and high-level electromagnetic wave three-dimensional spatiotemporal calculation, scene knowledge graph, etc. to eliminate multipath effects, and realize cooperative positioning of unmanned excavators and mining trucks.

[0032] In step S4, the specific process is as follows:

[0033] S41. Time Synchronization: Construct software and hardware time synchronization triggering mechanisms for data signals from multiple sensors such as LiDAR, vision camera, millimeter-wave radar, and inertial sensors, and design a multi-source information synchronous acquisition module to support subsequent data fusion processing.

[0034] S42, Roadside Perception and Positioning: Based on the roadside perception module, combined with multiple sensors such as lidar, millimeter-wave radar, cameras, and radar, it can realize all-weather object recognition and positioning. By using multi-sensor fusion, through instance segmentation and six-degree-of-freedom pose estimation deep learning algorithms, it can detect objects in the working environment and perform accurate three-dimensional pose estimation.

[0035] S43. Vehicle-mounted perception and positioning: Based on the vehicle-mounted perception module, combined with multiple sensors such as lidar, millimeter-wave radar, cameras, and radar, and using multi-sensor fusion SLAM (Simultaneous Localization and Mapping), autonomous perception and positioning of unmanned excavators and mining trucks are achieved.

[0036] S44. High-precision absolute positioning of unmanned excavators and mining trucks in vehicle-road cooperative manner: Combining the methods in steps S3 and S42, geographic coordinate transmission constraint factors are constructed using GPS and roadside end identification positioning and attitude determination results, and relative pose constraint factors are constructed using the communication and navigation integration and digital-analog combination unmanned excavator-mining truck high-precision cooperative positioning method. The factor graph fusion method is used to realize the fusion positioning of communication and navigation, model and perception methods.

[0037] Therefore, the present invention employs the above-described airborne unmanned aerial vehicle system and collaborative operation method for unmanned excavators, which has the following advantages:

[0038] (1) Currently, most engineering machinery does not adopt an integrated communication and positioning solution. Communication relies solely on 5G networks, and positioning relies solely on GPS signals. Under conditions such as complex electromagnetic environments and severe weather, the quality and reliability of communication and positioning cannot be guaranteed. In this invention, an autonomous PNT networking technology with integrated communication and navigation and scalable topology is proposed. A high-precision ranging and angle measurement network based on multi-carrier communication signals such as BDS, 5G, and UWB is constructed. A scene-adaptive positioning method driven by data and models and a non-line-of-sight error compensation mechanism are designed to achieve high-precision V2V fusion and collaborative positioning of unmanned excavators and mining trucks in complex environments, ensuring the reliability and security of communication and positioning in all aspects.

[0039] (2) Currently, most engineering machinery relies solely on its own sensors for positioning, which makes it difficult to guarantee reliability in complex environments and accuracy in harsh working conditions such as dust. In this invention, a vehicle-road cooperative unmanned excavator-mining truck cooperative high-precision absolute positioning is proposed. Two types of geographic coordinate transmission constraint factors are constructed using GPS and roadside end recognition positioning and attitude determination results to achieve communication, model and perception method fusion positioning and absolute positioning, ensuring accuracy and reliability.

[0040] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0041] Figure 1 A schematic diagram of a collaborative communication and positioning system for a mining excavator robot and a mining truck provided in an embodiment of the present invention;

[0042] Figure 2 This is a schematic diagram of a collaborative communication and positioning method between a mining excavator robot and a mining truck, provided in an embodiment of the present invention.

[0043] Figure Labels

[0044] 1. Communication and navigation integrated module; 2. Sensing module; 3. Edge computing module; 4. Energy supply module; 5. Security and protection module; 6. Remote and terminal user interface; S1. Large-scale, high-capacity, scalable communication network and deployment for large-scale mining scenarios; S2. Relative positioning of unmanned excavators and mining trucks based on communication and navigation network models; S3. High-precision collaborative positioning method for unmanned excavators and mining trucks using integrated communication and navigation and combined digital-analog approaches; S4. High-precision collaborative positioning of unmanned excavators and mining trucks using vehicle-road cooperation. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Specific model specifications need to be selected and determined according to the actual specifications of the device, etc. The specific selection calculation method adopts existing technology in the art, and therefore will not be described in detail.

[0046] Example

[0047] A collaborative communication and positioning system for mining excavation robots and mining trucks, characterized in that it includes a communication and navigation integrated module 1, a sensing module 2, an edge computing module 3, an energy supply module 4, a safety and protection module 5, and a remote and terminal user interface 6.

[0048] The integrated communication and navigation module 1 includes a vehicle-mounted communication and navigation unit and a roadside communication and navigation unit.

[0049] The vehicle-mounted communication unit, installed on the excavating robot and mining truck, is used to enable direct communication between vehicles (V2V) and communication with infrastructure (V2I). It also combines a multi-band GNSS receiver and RTK (Real-Time Kinematic) technology to obtain the vehicle's precise geographical location using the GNSS receiver and, in conjunction with an inertial navigation system (INS), to provide continuous location updates in the event of poor signal.

[0050] The roadside communication unit is deployed in key locations in the mine, such as intersections and loading / unloading areas, to communicate with vehicles and provide roadside wireless positioning information, communication relay, traffic information, warning messages, etc.

[0051] The sensing module 2 includes an on-board sensing unit and a roadside sensing unit.

[0052] The vehicle-mounted sensing unit 3 includes sensors such as vehicle-mounted LiDAR, camera, and millimeter-wave radar, which are used for vehicle-mounted environmental perception and identification positioning. This helps the excavator identify and locate mining trucks, obstacles, other vehicles, pedestrians, etc., and provides necessary data support for the collaborative positioning of unmanned excavators and mining trucks.

[0053] The roadside perception unit 4 includes roadside LiDAR, cameras, millimeter-wave radar and other sensors, which are used for roadside environmental perception and identification and positioning. At the same time, it can realize the identification and positioning of unmanned excavators, mining trucks, obstacles, other vehicles, pedestrians, etc., and provide necessary data support for the autonomous driving of unmanned excavators.

[0054] The edge computing module 5 is installed in unmanned excavators, mining trucks, and roadside facilities to process data from sensors, execute decision-making algorithms, control vehicle operation, and manage communication with other vehicles and infrastructure.

[0055] The energy supply module 6 is installed on fixed facilities such as roadside units, with solar panels installed to supply power to roadside equipment.

[0056] The security and protection module 7 includes a physical protection structure that provides dustproof, waterproof, and shockproof functions for key electronic components; and network security measures that implement encrypted communication, authentication, and other technologies to prevent unauthorized access and data leakage.

[0057] The remote and terminal user interface 8 includes a remote monitoring center, through a dedicated software platform, which allows managers to monitor the status of all vehicles such as unmanned excavators and mining trucks in real time, schedule tasks, and respond to emergencies; and an on-board display screen that provides location and communication information for the unmanned excavators.

[0058] A collaborative communication and positioning method for mining excavation robots and mining trucks includes the following steps:

[0059] S1. Large-scale, high-capacity, integrated, scalable communication network and deployment for large-scale mining scenarios;

[0060] S2. Relative positioning of unmanned excavator and mining truck based on communication and navigation network model;

[0061] S3, a high-precision collaborative positioning method for unmanned excavators and mining trucks that integrates communication and navigation and combines digital and analog models;

[0062] S4, Vehicle-Road Cooperative Unmanned Excavator - Mining Truck Cooperative High-Precision Positioning.

[0063] In step S1, the specific process is as follows:

[0064] S11. Establish a transmission model for communication and positioning signals: Based on the integrated communication and navigation module, using sparse signal representation, construct an overcomplete sub-dictionary of communication signals and interference through the K-SVD (K-Singular Value Decomposition) algorithm, and use the OMP (Orthogonal Matching Pursuit) algorithm to achieve signal separation and reconstruction, so as to quickly switch the positioning data channel to the optimal channel; use the optimal redundant data packet allocation strategy to allocate data packets among multiple relay nodes to ensure that data packets are transmitted in the optimal way and reduce the packet loss rate; through highly reliable multi-relay node redundant data packet forwarding, the cooperation of relay nodes is used to achieve redundant forwarding of data packets and improve the reliability of data transmission.

[0065] S12. Construct a PNT (Positioning, Navigation, and Timing) network protocol suite: Design a scalable, large-space positioning, navigation, and timing (PNT) network protocol suite under positioning performance constraints to transmit spatiotemporal references between multiple devices and ensure spatiotemporal consistency among them.

[0066] S13. Design an integrated communication and navigation network protocol: Utilize a rapid switching and optimal redundant data packet allocation strategy for positioning data channels to quickly switch positioning data channels and allocate data packets to achieve optimal transmission performance; employ a highly reliable multi-relay node data packet redundancy forwarding mechanism to achieve redundant forwarding of data packets through relay node cooperation, thereby improving the reliability of data transmission; and utilize an integrated communication and navigation network congestion control method to establish a network congestion control strategy to manage network traffic, prevent network congestion, and ensure communication quality.

[0067] S14. Construct an integrated communication and navigation network: Utilize vehicle-mounted wireless network equipment, based on the vehicle-mounted wireless network equipment of unmanned excavators and mining trucks, to construct a large-space, high-capacity, scalable, and dynamically networked integrated communication and navigation network; using dynamic networking technology, the constructed network needs to be able to dynamically adjust to adapt to constantly changing network conditions and user needs.

[0068] In step S2, the specific process is as follows:

[0069] S21. Complex Scene Communication and Navigation Signal Quality Detection and Compensation: Integrating multi-mode signals such as BDS (BeiDouNavigationSystem), 5G, and UWB, data fusion between BDS and UWB is performed. Leveraging the high-precision ranging characteristics of UWB and the wide coverage capability of BDS, more accurate positioning is achieved. A multipath fading detection and non-line-of-sight error compensation mechanism for wireless signals in all-weather complex environments such as urban buildings and mines is designed. Simulation analysis using the UWB channel model IEEE802.15.4a is used to estimate the mean and variance of the additional delay in non-line-of-sight environments. Measurement values ​​are corrected using probability theory, and collaborative positioning is achieved by combining the Chan algorithm and particle swarm optimization algorithm to improve positioning accuracy.

[0070] S22. Model-Based Vehicle Cooperative Localization: Based on the initial positioning results of BDS, and the short-range wireless communication and high-precision ranging characteristics provided by UWB carried by the unmanned excavator and the mining truck, the observation information between the unmanned excavator and the mining truck is fused and calculated. Through tight or deep coupling, the data from GNSS and INS (Inertial Navigation System) are further fused to improve the accuracy and reliability of positioning.

[0071] In step S3, the specific process is as follows:

[0072] S31. A scene-adaptive positioning method jointly driven by data and models: Based on model-based collaborative positioning, the onboard communication and navigation network modules of unmanned excavators and mining trucks are automatically networked and collect data from different scenes. Based on a data-driven wireless sensor network collaborative positioning method, a scene model and data-driven communication and navigation network networking positioning strategy is constructed to achieve scene-adaptive positioning through data-model collaboration. The data-driven method uses machine learning algorithms to optimize the positioning process, improving positioning accuracy and robustness.

[0073] S32. Cooperative positioning of unmanned excavators and mining trucks: Constructing three-dimensional scene information as the information source of comprehensive PNT, providing comprehensive PNT support at different levels, including low-level scene spatial structure information, texture features, topological road network, etc. to improve positioning capabilities, and high-level electromagnetic wave three-dimensional spatiotemporal calculation, scene knowledge graph, etc. to eliminate multipath effects, and realize cooperative positioning of unmanned excavators and mining trucks.

[0074] In step S4, the specific process is as follows:

[0075] S41. Time Synchronization: Construct software and hardware time synchronization triggering mechanisms for data signals from multiple sensors such as LiDAR, vision camera, millimeter-wave radar, and inertial sensors, and design a multi-source information synchronous acquisition module to support subsequent data fusion processing.

[0076] S42, Roadside Perception and Positioning: Based on the roadside perception module, combined with multiple sensors such as lidar, millimeter-wave radar, cameras, and radar, it can realize all-weather object recognition and positioning. By using multi-sensor fusion, through instance segmentation and six-degree-of-freedom pose estimation deep learning algorithms, it can detect objects in the working environment and perform accurate three-dimensional pose estimation.

[0077] S43. Vehicle-mounted perception and positioning: Based on the vehicle-mounted perception module, combined with multiple sensors such as lidar, millimeter-wave radar, cameras, and radar, and using multi-sensor fusion SLAM (Simultaneous Localization and Mapping), autonomous perception and positioning of unmanned excavators and mining trucks are achieved.

[0078] S44. High-precision absolute positioning of unmanned excavators and mining trucks in vehicle-road cooperative manner: Combining the methods in steps S3 and S42, geographic coordinate transmission constraint factors are constructed using GPS and roadside end identification positioning and attitude determination results, and relative pose constraint factors are constructed using the communication and navigation integration and digital-analog combination unmanned excavator-mining truck high-precision cooperative positioning method. The factor graph fusion method is used to realize the fusion positioning of communication and navigation, model and perception methods.

[0079] Therefore, this invention proposes an autonomous PNT networking technology that integrates communication and navigation with a scalable topology. It constructs a communication and navigation integrated positioning network based on high-precision ranging and angle measurement using multi-carrier communication signals such as BDS, 5G, and UWB. It designs a scene-adaptive positioning method driven by data and models, as well as a non-line-of-sight error compensation mechanism, achieving high-precision V2V fusion collaborative positioning of unmanned excavators and mining trucks in complex environments through multi-mode signal fusion, comprehensively ensuring the reliability and security of communication and positioning. Furthermore, it proposes a vehicle-road cooperative high-precision absolute positioning system for unmanned excavators and mining trucks, utilizing GPS and roadside identification positioning and attitude determination results to construct two types of geographic coordinate transmission constraint factors. This enables fusion positioning of communication and navigation, model, and perception methods, achieving absolute positioning and ensuring accuracy and reliability.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A collaborative communication and positioning system for a mining unmanned excavator and mining truck, characterized in that: It includes a communication and navigation module, a sensing module, an edge computing module, an energy supply module, a security and protection module, and a remote and terminal user interface; The integrated communication and navigation module includes an on-board communication and navigation unit and a roadside communication and navigation unit; The vehicle-mounted communication and navigation unit is installed on unmanned excavators and mining trucks to realize V2V and V2I communication. It also uses a multi-band GNSS receiver and RTK technology to obtain the vehicle's precise geographical location using the GNSS receiver and, combined with INS, provides continuous location updates in the event of poor signal. The roadside communication and navigation unit is deployed at key locations in the mine to communicate with vehicles and provide roadside wireless positioning information, communication relay, traffic information, and warning messages for scene targets. The sensing module includes an on-board sensing unit and a roadside sensing unit; The vehicle-mounted sensing unit includes a vehicle-mounted LiDAR, a camera, and a millimeter-wave radar, which are used for vehicle-mounted environmental perception and identification positioning. This helps the unmanned excavator identify and locate mining trucks, obstacles, other vehicles, and pedestrians, and provides necessary data support for the collaborative positioning of the unmanned excavator and the mining truck. The roadside perception unit includes a roadside lidar, a camera, and a millimeter-wave radar, which are used for roadside environmental perception, identification, and positioning. At the same time, it enables the identification and positioning of unmanned excavators, mining trucks, obstacles, other vehicles, and pedestrians, providing necessary data support for the autonomous driving of unmanned excavators. The edge computing module is installed in unmanned excavators, mining trucks, and roadside facilities to process data from sensors, execute decision-making algorithms, control vehicle operation, and manage communication with other vehicles and infrastructure. The energy supply module is installed on a fixed facility and supplies power to the roadside equipment by installing solar panels. The security and protection module includes a physical protection structure that provides dustproof, waterproof, and shockproof functions for key electronic components; and network security measures that implement encrypted communication and authentication technologies to prevent unauthorized access and data leakage. The remote and terminal user interface includes a remote monitoring center, through a dedicated software platform, which allows managers to monitor the status of all vehicles in real time, schedule tasks, and respond to emergencies; and an on-board display screen, which provides location and communication information for the unmanned excavator.

2. A method for collaborative communication and positioning between a mining unmanned excavator and a mining truck, used in the mining unmanned excavator-mining truck collaborative communication and positioning system as described in claim 1, characterized in that, Includes the following steps: S1. Conduct large-scale, high-capacity, integrated, scalable communication networking and deployment in large-scale mining scenarios; S2. Perform relative positioning of unmanned excavator and mining truck based on communication and navigation network model; S3. High-precision collaborative positioning of unmanned excavators and mining trucks that integrate communication and navigation and combine digital and analog models; S4. High-precision positioning of unmanned excavators and mining trucks in vehicle-road cooperative operation.

3. The method according to claim 2, characterized in that: In S1, the specific process is as follows: S11. Establish a transmission model for communication and positioning signals: Based on the integrated communication and navigation module, using sparse signal representation, construct an overcomplete sub-dictionary of communication signals and interference through the K-SVD algorithm, and use the OMP algorithm to achieve signal separation and reconstruction, so as to quickly switch the positioning data channel to the optimal channel; use the optimal redundant data packet allocation strategy to allocate data packets among multiple relay nodes to ensure that data packets are transmitted in the optimal way and reduce the packet loss rate; through highly reliable multi-relay node redundant data packet forwarding, the cooperation of relay nodes is used to achieve redundant forwarding of data packets and improve the reliability of data transmission. S12. Construct a PNT network protocol suite: Design a scalable, large-space PNT network protocol suite under positioning performance constraints to transmit spatiotemporal references between multiple devices and ensure spatiotemporal consistency between devices. S13. Design a communication and navigation integrated network protocol: Utilize a rapid switching strategy for positioning data channels and an optimal redundant data packet allocation strategy to rapidly switch positioning data channels and allocate data packets to achieve optimal transmission performance; Utilize a communication and navigation integrated network congestion control method to establish a network congestion control strategy to manage network traffic, prevent network congestion, and ensure communication quality. S14. Construct an integrated communication and navigation network: Utilize vehicle-mounted wireless network equipment, based on unmanned excavators and mining trucks, to construct a large-scale, high-capacity, scalable, and dynamically networked integrated communication and navigation network; utilize dynamic networking technology to construct a network that can be dynamically adjusted to adapt to constantly changing network conditions and user needs.

4. The method according to claim 2, characterized in that: In S2, the specific process is as follows: S21. Conduct signal quality detection and compensation in complex scenarios: Integrate BDS, 5G, and UWB signals, and achieve accurate positioning by fusing data between BDS and UWB, utilizing the high-precision ranging characteristics of UWB and the wide coverage capability of BDS; Design a multipath fading detection and non-line-of-sight error compensation mechanism for wireless signals in complex environments such as urban buildings and mines around the clock, and use simulation analysis of the UWB channel model IEEE802.15.4a to estimate the mean and variance of the additional delay in non-line-of-sight environments, correct the measurement values ​​through probability theory, and combine the Chan algorithm and particle swarm optimization algorithm for collaborative positioning to improve positioning accuracy; S22. Perform model-based vehicle cooperative positioning: Based on the initial positioning results of BDS, the short-range wireless communication and high-precision ranging characteristics provided by UWB carried by the unmanned excavator and mining truck, perform data fusion and calculation on the observation information between the unmanned excavator and the mining truck; further fuse GNSS and INS data through tight coupling or deep coupling to improve the positioning accuracy and reliability.

5. The method according to claim 2, characterized in that: In S3, the specific process is as follows: S31. Scene adaptive positioning driven by data and model: Based on model-based collaborative positioning, the onboard communication and navigation network modules of unmanned excavators and mining trucks are automatically networked and collect data from different scenes. Based on data-driven wireless sensor network collaborative positioning, a scene model and data-driven communication and navigation network networking positioning strategy is constructed to achieve scene adaptive positioning by combining data and model. The data-driven wireless sensor network collaborative positioning technology uses machine learning algorithms to optimize the positioning process and improve the accuracy and robustness of positioning. S32. Conduct collaborative positioning between unmanned excavators and mining trucks: Construct three-dimensional scene information as the information source for integrated PNT, and provide integrated PNT support at different levels, including improving positioning capabilities through low-level scene spatial structure information, texture features, and topological road networks, and eliminating multipath effects through high-level electromagnetic wave three-dimensional spatiotemporal calculations and scene knowledge graphs, to achieve collaborative positioning between unmanned excavators and mining trucks.

6. The method according to claim 5, characterized in that: In S4, the specific process is as follows: S41. Time synchronization: Construct software and hardware time synchronization triggering mechanisms for multi-sensor data signals, and design a multi-source information synchronization acquisition module to support subsequent data fusion processing; S42. Perform roadside perception and localization: Based on the roadside perception module, combined with LiDAR, millimeter-wave radar and camera, realize all-weather object recognition and localization. Utilize multi-sensor fusion, through instance segmentation and six-degree-of-freedom pose estimation deep learning algorithms, detect objects in the working environment and perform accurate three-dimensional pose estimation. S43. Perform vehicle-mounted perception and positioning: Based on the vehicle-mounted perception module, combined with lidar, millimeter-wave radar and camera, and using multi-sensor fusion SLAM, realize the autonomous perception and positioning of the unmanned excavator and mining truck. S44. High-precision absolute positioning of unmanned excavators-mining trucks in vehicle-road cooperative system: Combining steps S3 and S42, construct geographic coordinate transmission constraint factors based on GPS and roadside end identification positioning and attitude determination results, construct relative pose constraint factors based on step S3, and use factor graph fusion method to achieve integrated positioning of communication, navigation, model and perception methods.

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