Mining mining robot-mine card cooperative communication positioning system and method

Abstract

The invention discloses a mining robot-mine card cooperative communication positioning system and method, and belongs to the technical field of cooperative operation of multiple unmanned systems, and the mining robot-mine card cooperative communication positioning system comprises a conduction integrated module, a sensing module, an edge calculation module, an energy supply module, a safety and protection module, and a remote and terminal user interface. Through mine large-scene large-capacity communication and conduction integrated telescopic communication networking and deployment, unmanned excavator-mine card relative positioning based on a communication and conduction network model, a communication and conduction integrated and digital-analog combined unmanned excavator-mine card high-precision cooperative positioning method, and vehicle-road cooperative unmanned excavator-mine card cooperative high-precision positioning, a communication and conduction integrated and digital-analog combined unmanned excavator-mine card high-precision cooperative positioning method and a communication and conduction integrated and digital-analog combined unmanned excavator-mine card high-precision cooperative positioning method are realized. Cooperative communication and high-precision positioning of the unmanned excavator and the mine card in a complex working scene of a mine are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of collaborative positioning of unmanned mining systems, in particular to a collaborative communication positioning system and method between a mining excavation robot and a mining truck. Background Art

[0002] In the modern mining process, in order to improve efficiency and safety, more and more unmanned operating equipment, such as unmanned excavation robots and mining trucks, are used. Through high-precision collaborative positioning, efficient cooperation between unmanned excavation robots and mining trucks can be achieved, waiting time and idling rate can be reduced, and material transfer speed can be accelerated, thereby greatly improving the production efficiency of mines. However, in a complex electromagnetic environment, how to achieve efficient and accurate communication and positioning between equipment and equipment has become a major challenge. Existing technical solutions are difficult to meet the requirements of large scenes, large capacity, and high precision, especially in urban buildings and mining environments where signal multipath effects are serious and non-line-of-sight (NLOS) errors are significant. When mining robots work in complex scenes such as urban canyons and mines, there are unstable GPS and 5G signals. Using solutions such as laser radar and cameras to extract features from the surrounding environment to determine the location of collaborative operation objects will also face problems such as large areas, unclear features, and no stable features in dynamic mining in environments such as mining areas. With the advancement of vehicle network V2V technology, it is expected to be applied to excavators and trucks to achieve collaborative positioning.

[0003] In terms of excavator-mine truck collaborative positioning, the current solution is mainly to install RTK positioning on the excavator and the mine truck for collaborative operation. Chinese utility model CN202121962283.X, a bucket wheel excavator-truck collaborative loading alignment device, uses high-precision GNSS positioning sensors to collect the position information of the bucket wheel excavator and the mining truck, and directly completes the loading process of the bucket wheel excavator and the truck; Chinese invention CN202110958558.0, a bucket wheel excavator-truck collaborative loading alignment device and method, uses high-precision GNSS positioning sensors to collect the position information of the bucket wheel excavator and the truck, and judges whether the bucket wheel excavator is unloading by the positions of the two, and judges whether the truck is full by the height of the truck material; Chinese invention CN202110857651.2, a method and main control device for docking an excavator and a mine truck, the front end of the excavator is provided with an infrared transmitting device, and the tail of the mine truck is provided with an infrared receiving device, based on the infrared signal between the excavator and the mine truck, the mine truck is instructed to dock with the excavator, and the excavator and the mine truck are automatically loaded. Summary of the invention

[0004] The purpose of the present invention is to provide an airborne drone system and a collaborative operation method for unmanned excavators, which forms a dense map by mapping and stitching multiple drones to display information in the working area, guides multiple unmanned excavators to enter the site for operation, and realizes real-time monitoring of the working area and operation status of the unmanned excavators by multiple drones through the optimal scheduling and vertical landing method of collaborative charging of multiple drones, thereby improving the operating efficiency and intelligence level of multiple unmanned excavators and multiple drones in mines and construction sites, and at the same time increasing the construction safety of unmanned excavators.

[0005] In order to achieve the above-mentioned purpose, a mining excavation robot-mining truck collaborative communication and positioning system is provided, which is characterized by: including an integrated communication and navigation module, a perception module, an edge computing module, an energy supply module, a safety and protection module, and a remote and terminal user interface.

[0006] The communication and navigation integrated module includes a vehicle-mounted communication and navigation unit and a roadside communication and navigation unit.

[0007] The vehicle-mounted communication unit is installed on the mining robot and mining truck to realize direct communication between vehicles (V2V) and communication with infrastructure (V2I). At the same time, it combines the use of multi-band GNSS receivers and RTK (RealTime Kinematic) technology to obtain the precise geographic location of the vehicle using the GNSS receiver, and combines with the inertial navigation system (INS) to provide continuous position updates under poor signal conditions.

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

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

[0010] The vehicle-mounted sensing unit includes vehicle-mounted laser radar, camera, millimeter-wave radar and other sensors, which are used for vehicle-mounted environmental perception and identification and positioning, helping the excavator to identify and locate mining trucks, obstacles, other vehicles, pedestrians, etc., and providing necessary data support for the coordinated 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 environment perception and identification and positioning, and at the same time realize the identification and positioning of unmanned excavators and mining trucks, obstacles, other vehicles, pedestrians, etc., providing necessary data support for the automatic 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 operations, and manage communications with other vehicles and infrastructure.

[0013] The energy supply module is installed on a roadside unit or other fixed facilities to install solar panels to supply power to the roadside equipment.

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

[0015] The remote and terminal user interfaces include a remote monitoring center. Through a dedicated software platform, managers can monitor the status of all vehicles such as unmanned excavators and mining trucks in real time, dispatch tasks, and respond to emergencies. The on-board display screen provides positioning and communication information display for the unmanned excavator.

[0016] A mining robot-mining truck collaborative communication positioning method, comprising the following steps: S1. Scalable communication networking and deployment of large-scale, high-capacity, integrated communication and navigation in large mine scenarios; S2. Relative positioning of unmanned excavator and mining truck based on communication and navigation network model; S3, a high-precision collaborative positioning method for unmanned excavator and mining truck with integrated communication and navigation and digital-analog combination; S4, vehicle-road collaborative unmanned excavator and mining truck collaborative high-precision positioning.

[0017] In step 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, use signal sparse representation, and construct an overcomplete sub-dictionary of communication signals and interference through the K-SVD (K-Singular Value Decomposition) algorithm. 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 best way and reduce the packet loss rate; through high-reliability multi-relay node data packet redundant forwarding, use the collaboration of relay nodes to achieve redundant forwarding of data packets and improve the reliability of data transmission.

[0018] S12. Build a PNT (Positioning, Navigation, and Timing) network protocol cluster: Design a scalable large-space positioning, navigation, and timing (PNT) network protocol cluster under positioning performance constraints to transmit time and space references between multiple devices to ensure their time and space consistency.

[0019] S13. Design a communication and navigation integrated network protocol: utilize the fast switching of positioning data channels and the optimal redundant data packet allocation strategy to quickly switch the positioning data channel and allocate data packets to achieve the best transmission effect; utilize the highly reliable multi-relay node data packet redundant forwarding mechanism to achieve redundant forwarding of data packets through the collaboration of relay nodes to improve the reliability of data transmission; utilize the 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.

[0020] S14. Build 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 build a large-space, large-capacity, scalable, and dynamically networked integrated communication and navigation network; utilize dynamic networking technology to build a network that can be dynamically adjusted to adapt to changing network conditions and user needs.

[0021] In step S2, the specific process is as follows: S21. Detection and compensation of communication signal quality in complex scenarios: Integrate multi-mode signals such as BDS (BeiDou Navigation System), 5G, and UWB, perform data fusion between BDS and UWB, and use the high-precision ranging characteristics of UWB and the wide coverage capability of BDS to achieve more accurate positioning; design wireless signal multipath fading detection and non-line-of-sight error compensation mechanism for all-weather complex environments such as urban buildings and mines, use simulation analysis of the UWB channel model IEEE 802.15.4a to estimate the mean and variance of the additional delay in the non-line-of-sight environment, correct the measured value through probability theory knowledge, and combine the Chan algorithm with the particle swarm algorithm for collaborative positioning to improve positioning accuracy.

[0022] S22, Model-based vehicle collaborative positioning: Based on the primary positioning results of BDS, the short-range wireless communication and high-precision ranging characteristics provided by the UWB carried by the unmanned excavator and the transport truck, the observation information between the unmanned excavator and the transport truck is fused and solved. Through tight coupling or deep coupling, the data of GNSS and INS (inertial navigation system) are further fused to improve the accuracy and reliability of positioning.

[0023] In step S3, the specific process is as follows: S31. Scene-adaptive positioning method driven by data and model: Based on model-based collaborative positioning, the airborne communication and navigation network modules of unmanned excavators and mining trucks are used to automatically network and collect data from different scenes. Based on the 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 of digital-analog combination. The data-driven method optimizes the positioning process through machine learning algorithms to improve the accuracy and robustness of positioning.

[0024] S32. Cooperative positioning of unmanned excavators and mining trucks: Construct three-dimensional scene information as the information source of comprehensive PNT, and provide comprehensive PNT support at different levels, including low-level scene spatial structure information, texture features, topological road networks, etc. to improve positioning capabilities, and high-level electromagnetic wave three-dimensional space-time calculations, scene knowledge graphs, etc. to eliminate multipath effects, and realize cooperative positioning of unmanned excavators and mining trucks.

[0025] In step S4, the specific process is as follows: S41, Time Synchronization: Construct software and hardware time synchronization trigger mechanisms for multi-sensor data signals such as lidar, visual camera, millimeter-wave radar, and inertial, and design multi-source information synchronization acquisition modules to support subsequent data fusion processing; S42, roadside perception and positioning: Based on the roadside perception module, combined with multiple sensors such as lidar, millimeter-wave radar, camera, radar, etc., it realizes all-weather object recognition and positioning. It uses multi-sensor fusion, instance segmentation, and six-degree-of-freedom pose estimation deep learning algorithms to detect objects in the working environment and perform accurate three-dimensional pose estimation; S43, vehicle-mounted perception and positioning: Based on the vehicle-mounted perception module, combined with multiple sensors such as laser radar, millimeter-wave radar, camera, radar, etc., using multi-sensor fusion SLAM (Simultaneous Localization And Mapping), the autonomous perception and positioning of the unmanned excavator and the mining truck is realized; S44. High-precision absolute positioning of unmanned excavator and mining truck in vehicle-road collaboration: Combine the methods of steps S3 and S42, and construct geographic coordinate transmission constraint factors with the results of GPS and roadside identification, positioning and attitude determination. Construct relative posture constraint factors with the high-precision collaborative positioning method of unmanned excavator and mining truck in combination with communication and navigation and digital-analog combination. Use the factor graph fusion method to realize the integrated positioning of communication and navigation, model and perception methods.

[0026] Therefore, the present invention adopts the above-mentioned airborne drone system and collaborative operation method for unmanned excavators, which has the following advantages: (1) Currently, common construction machinery has not adopted an integrated communication and positioning solution. Communication relies solely on 5G networks, and positioning relies solely on GPS signals. The quality and reliability of communication and positioning cannot be guaranteed under complex electromagnetic environments, severe weather and other conditions. In this invention, an autonomous PNT networking technology with integrated communication and navigation and scalable topology is proposed to build an integrated communication and navigation positioning network based on high-precision ranging and angle measurement of multi-carrier communication signals such as BDS, 5G, and UWB. A scenario-adaptive positioning method and non-line-of-sight error compensation mechanism driven by data and models are designed to achieve high-precision fusion collaborative positioning of unmanned excavators and mining trucks with multi-mode signal fusion in complex environments, and to ensure the reliability and safety of communication and positioning in all aspects.

[0027] (2) Currently, common construction machinery relies solely on its own sensors for positioning, and it is difficult to guarantee reliability in complex environments, and it is difficult to guarantee accuracy in harsh working conditions such as dust. In this invention, a vehicle-road collaborative unmanned excavator-mining truck collaborative high-precision absolute positioning is proposed, and two types of geographic coordinate transmission constraint factors are constructed using GPS and roadside identification positioning and attitude determination results to achieve communication, model and perception method fusion positioning, absolute positioning, and ensure accuracy and reliability.

[0028] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A schematic diagram of a mining robot-mining truck collaborative communication and positioning system provided by an embodiment of the present invention; Figure 2 A schematic diagram of a mining robot-mining truck collaborative communication positioning method provided by an embodiment of the present invention; Reference numerals 1. Communication and navigation integrated module; 2. Perception module; 3. Edge computing module; 4. Energy supply module; 5. Safety and protection module; 6. Remote and terminal user interface; S1. Large-scale, large-capacity communication and navigation integrated scalable communication networking and deployment in mines; S2. Relative positioning of unmanned excavator and mining truck based on communication and navigation network model; S3. High-precision collaborative positioning method of unmanned excavator and mining truck with communication and navigation integrated and digital-analog combination; S4. High-precision collaborative positioning of unmanned excavator and mining truck with vehicle-road collaboration. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations. The specific model specifications need to be selected and determined according to the actual specifications of the device, and the specific selection calculation method adopts the existing technology in the field, so it will not be described in detail.

[0031] Example A mining excavation robot-mining truck collaborative communication and positioning system, characterized by: comprising a communication and navigation integrated module 1, a perception 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.

[0032] The communication and navigation integrated module 1 includes a vehicle-mounted communication and navigation unit and a roadside communication and navigation unit.

[0033] The vehicle-mounted communication unit is installed on the mining robot and mining truck to realize direct communication between vehicles (V2V) and communication with infrastructure (V2I). At the same time, it combines the use of multi-band GNSS receivers and RTK (RealTime Kinematic) technology to obtain the precise geographic location of the vehicle using the GNSS receiver, and combines with the inertial navigation system (INS) to provide continuous position updates under poor signal conditions.

[0034] The roadside communication and navigation unit is deployed at key locations in the mine, such as intersections, loading and unloading areas, etc., and is used to communicate with vehicles and provide the roadside end with wireless positioning information, communication relay, traffic information, warning messages, etc. for scene targets.

[0035] The perception module 2 includes a vehicle-mounted perception unit and a roadside perception unit.

[0036] The vehicle-mounted sensing unit 3 includes sensors such as vehicle-mounted laser radar, camera, millimeter-wave radar, etc., which are used for vehicle-mounted environmental perception and identification and positioning, helping the excavator to identify and locate mining trucks, obstacles, other vehicles, pedestrians, etc., and providing necessary data support for the coordinated positioning of unmanned excavators and mining trucks.

[0037] The roadside sensing unit 4 includes roadside laser radar, camera, millimeter wave radar and other sensors, which are used for roadside environment perception and identification and positioning, and at the same time realize the identification and positioning of unmanned excavators and mining trucks, obstacles, other vehicles, pedestrians, etc., and provide necessary data support for the automatic driving of unmanned excavators.

[0038] The edge computing module 5 is installed in unmanned excavators, mining trucks, and roadside facilities to process data from sensors, execute decision algorithms, control vehicle operations, and manage communications with other vehicles and infrastructure.

[0039] The energy supply module 6 is installed on a roadside unit or other fixed facility to install a solar panel to supply power to the roadside equipment.

[0040] The safety and protection module 7 includes a physical protection structure to provide dustproof, waterproof, shockproof and other functions for key electronic components; as well as network security measures to implement encrypted communication, identity authentication and other technologies to prevent illegal access and data leakage.

[0041] The remote and terminal user interface 8 includes a remote monitoring center. Through a dedicated software platform, managers can monitor the status of all vehicles such as unmanned excavators and mining trucks in real time, schedule tasks, and respond to emergencies; the on-board display screen provides positioning and communication information display for the unmanned excavator.

[0042] A mining robot-mining truck collaborative communication positioning method, comprising the following steps: S1. Scalable communication networking and deployment of large-scale, high-capacity, integrated communication and navigation in large mine scenarios; S2. Relative positioning of unmanned excavator and mining truck based on communication and navigation network model; S3, a high-precision collaborative positioning method for unmanned excavator and mining truck with integrated communication and navigation and digital-analog combination; S4, vehicle-road collaborative unmanned excavator and mining truck collaborative high-precision positioning.

[0043] In step 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, use signal sparse representation, and construct an overcomplete sub-dictionary of communication signals and interference through the K-SVD (K-Singular Value Decomposition) algorithm. 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 best way and reduce the packet loss rate; through high-reliability multi-relay node data packet redundant forwarding, use the collaboration of relay nodes to achieve redundant forwarding of data packets and improve the reliability of data transmission.

[0044] S12. Build a PNT (Positioning, Navigation, and Timing) network protocol cluster: Design a scalable large-space positioning, navigation, and timing (PNT) network protocol cluster under positioning performance constraints to transmit time and space references between multiple devices to ensure their time and space consistency.

[0045] S13. Design a communication and navigation integrated network protocol: utilize the fast switching of positioning data channels and the optimal redundant data packet allocation strategy to quickly switch the positioning data channel and allocate data packets to achieve the best transmission effect; utilize the highly reliable multi-relay node data packet redundant forwarding mechanism to achieve redundant forwarding of data packets through the collaboration of relay nodes to improve the reliability of data transmission; utilize the 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.

[0046] S14. Build 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 build a large-space, large-capacity, scalable, and dynamically networked integrated communication and navigation network; utilize dynamic networking technology to build a network that can be dynamically adjusted to adapt to changing network conditions and user needs.

[0047] In step S2, the specific process is as follows: S21. Detection and compensation of communication signal quality in complex scenarios: Integrate multi-mode signals such as BDS (BeiDou Navigation System), 5G, and UWB, perform data fusion between BDS and UWB, and use the high-precision ranging characteristics of UWB and the wide coverage capability of BDS to achieve more accurate positioning; design wireless signal multipath fading detection and non-line-of-sight error compensation mechanism for all-weather complex environments such as urban buildings and mines, use simulation analysis of the UWB channel model IEEE 802.15.4a to estimate the mean and variance of the additional delay in the non-line-of-sight environment, correct the measured value through probability theory knowledge, and combine the Chan algorithm with the particle swarm algorithm for collaborative positioning to improve positioning accuracy.

[0048] S22, Model-based vehicle collaborative positioning: Based on the primary positioning results of BDS, the short-range wireless communication and high-precision ranging characteristics provided by the UWB carried by the unmanned excavator and the transport truck, the observation information between the unmanned excavator and the transport truck is fused and solved. Through tight coupling or deep coupling, the data of GNSS and INS (inertial navigation system) are further fused to improve the accuracy and reliability of positioning.

[0049] In step S3, the specific process is as follows: S31. Scene-adaptive positioning method driven by data and model: Based on model-based collaborative positioning, the airborne communication and navigation network modules of unmanned excavators and mining trucks are used to automatically network and collect data from different scenes. Based on the 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 of digital-analog combination. The data-driven method optimizes the positioning process through machine learning algorithms to improve the accuracy and robustness of positioning.

[0050] S32. Cooperative positioning of unmanned excavators and mining trucks: Construct three-dimensional scene information as the information source of comprehensive PNT, and provide comprehensive PNT support at different levels, including low-level scene spatial structure information, texture features, topological road networks, etc. to improve positioning capabilities, and high-level electromagnetic wave three-dimensional space-time calculations, scene knowledge graphs, etc. to eliminate multipath effects, and realize cooperative positioning of unmanned excavators and mining trucks.

[0051] In step S4, the specific process is as follows: S41, Time Synchronization: Construct software and hardware time synchronization trigger mechanisms for multi-sensor data signals such as lidar, visual camera, millimeter-wave radar, and inertial, and design multi-source information synchronization acquisition modules to support subsequent data fusion processing; S42, roadside perception and positioning: Based on the roadside perception module, combined with multiple sensors such as lidar, millimeter-wave radar, camera, radar, etc., it realizes all-weather object recognition and positioning. It uses multi-sensor fusion, instance segmentation, and six-degree-of-freedom pose estimation deep learning algorithms to detect objects in the working environment and perform accurate three-dimensional pose estimation; S43, vehicle-mounted perception and positioning: Based on the vehicle-mounted perception module, combined with multiple sensors such as laser radar, millimeter-wave radar, camera, radar, etc., using multi-sensor fusion SLAM (Simultaneous Localization And Mapping), the autonomous perception and positioning of the unmanned excavator and the mining truck is realized; S44. High-precision absolute positioning of unmanned excavator and mining truck in vehicle-road collaboration: Combine the methods of steps S3 and S42, and construct geographic coordinate transmission constraint factors with the results of GPS and roadside identification, positioning and attitude determination. Construct relative posture constraint factors with the high-precision collaborative positioning method of unmanned excavator and mining truck in combination with communication and navigation and digital-analog combination. Use the factor graph fusion method to realize the integrated positioning of communication and navigation, model and perception methods.

[0052] Therefore, the present invention proposes an autonomous PNT networking technology that integrates communication and navigation and has scalable topology, builds a communication and navigation integrated positioning network based on high-precision distance and angle measurement of multi-carrier communication signals such as BDS, 5G, and UWB, designs a scene-adaptive positioning method and a non-line-of-sight error compensation mechanism driven jointly by data and models, and realizes V2V high-precision fusion collaborative positioning of unmanned excavators and mining trucks with multi-mode signal fusion in complex environments, and comprehensively guarantees the reliability and safety of communication positioning. The vehicle-road collaborative vehicle-road collaborative unmanned excavator-mining truck collaborative high-precision absolute positioning is proposed, and two types of geographic coordinate transmission constraint factors are constructed using GPS and roadside identification positioning and attitude determination results to realize the fusion positioning and absolute positioning of communication and navigation, models and perception methods to ensure accuracy and reliability.

[0053] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A mining robot-mining truck collaborative communication positioning system, characterized in that: It includes communication and navigation integrated module, perception module, edge computing module, energy supply module, safety and protection module, and remote and terminal user interface. The communication and navigation integrated module includes a vehicle-mounted communication and navigation unit and a roadside communication and navigation unit. The vehicle-mounted communication unit is installed on the mining robot and mining truck to realize direct communication between vehicles (V2V) and communication with infrastructure (V2I). At the same time, it combines the use of multi-band GNSS receivers and RTK (Real Time Kinematic) technology to obtain the precise geographic location of the vehicle using the GNSS receiver, and combines with the inertial navigation system (INS) to provide continuous position updates under poor signal conditions. The roadside communication and navigation unit is deployed at key locations in the mine, such as intersections, loading and unloading areas, etc., and is used to communicate with vehicles and provide the roadside end with wireless positioning information, communication relay, traffic information, warning messages, etc. for scene targets. The perception module includes an on-board perception unit and a roadside perception unit. The vehicle-mounted sensing unit includes vehicle-mounted laser radar, camera, millimeter-wave radar and other sensors, which are used for vehicle-mounted environmental perception and identification and positioning, helping the excavator to identify and locate mining trucks, obstacles, other vehicles, pedestrians, etc., and providing necessary data support for the coordinated positioning of unmanned excavators and mining trucks. The roadside perception unit includes roadside lidar, cameras, millimeter-wave radar and other sensors, which are used for roadside environment perception and identification and positioning, and at the same time realize the identification and positioning of unmanned excavators and mining trucks, obstacles, other vehicles, pedestrians, etc., providing necessary data support for the automatic 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 operations, and manage communications with other vehicles and infrastructure. The energy supply module is installed on a roadside unit or other fixed facilities to install solar panels to supply power to the roadside equipment. The safety and protection module includes a physical protection structure that provides dustproof, waterproof, shockproof and other functions for key electronic components; as well as network security measures that implement encrypted communication, identity authentication and other technologies to prevent illegal access and data leakage. The remote and terminal user interfaces include a remote monitoring center. Through a dedicated software platform, managers can monitor the status of all vehicles such as unmanned excavators and mining trucks in real time, schedule tasks, and respond to emergencies. The on-board display screen provides positioning and communication information display for the unmanned excavator.

2. A mining robot-mining truck collaborative communication positioning method, comprising a mining robot-mining truck collaborative communication positioning system according to any one of claim 1, characterized in that: The following steps are involved: S1. Scalable communication networking and deployment of large-scale, high-capacity, integrated communication and navigation in large mine scenarios; S2. Relative positioning of unmanned excavator and mining truck based on communication network model; S3, a high-precision collaborative positioning method for unmanned excavator and mining truck with integrated communication and navigation and digital-analog combination; S4, vehicle-road collaborative unmanned excavator and mining truck collaborative high-precision positioning.

3. The method for cooperative operation of an airborne drone system for an unmanned excavator according to claim 1, characterized in that: in step 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, use signal sparse representation, and construct an overcomplete sub-dictionary of communication signals and interference through the K-SVD (K-Singular Value Decomposition) algorithm. 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 best way and reduce the packet loss rate; through high-reliability multi-relay node data packet redundant forwarding, use the collaboration of relay nodes to achieve redundant forwarding of data packets and improve the reliability of data transmission. S12. Build a PNT (Positioning, Navigation, and Timing) network protocol cluster: Design a scalable large-space positioning, navigation, and timing (PNT) network protocol cluster under positioning performance constraints to transmit time and space references between multiple devices to ensure their time and space consistency. S13. Design a communication and navigation integrated network protocol: utilize the fast switching of positioning data channels and the optimal redundant data packet allocation strategy to quickly switch the positioning data channel and allocate data packets to achieve the best transmission effect; utilize the highly reliable multi-relay node data packet redundant forwarding mechanism to achieve redundant forwarding of data packets through the collaboration of relay nodes to improve the reliability of data transmission; utilize the 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. Build 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 build a large-space, large-capacity, scalable, and dynamically networked integrated communication and navigation network; utilize dynamic networking technology to build a network that can be dynamically adjusted to adapt to changing network conditions and user needs.

4. The method for cooperative operation of an airborne drone system for an unmanned excavator according to claim 2 is characterized in that: in step S2, the specific process is as follows: S21. Detection and compensation of communication signal quality in complex scenarios: Integrate multi-mode signals such as BDS (BeiDou Navigation System), 5G, and UWB, perform data fusion between BDS and UWB, and use the high-precision ranging characteristics of UWB and the wide coverage capability of BDS to achieve more accurate positioning; design wireless signal multipath fading detection and non-line-of-sight error compensation mechanism for all-weather complex environments such as urban buildings and mines, use simulation analysis of the UWB channel model IEEE 802.15.4a to estimate the mean and variance of the additional delay in the non-line-of-sight environment, correct the measured value through probability theory knowledge, and combine the Chan algorithm with the particle swarm algorithm for collaborative positioning to improve positioning accuracy. S22, Model-based vehicle collaborative positioning: Based on the primary positioning results of BDS, the short-range wireless communication and high-precision ranging characteristics provided by the UWB carried by the unmanned excavator and the transport truck, the observation information between the unmanned excavator and the transport truck is fused and solved. Through tight coupling or deep coupling, the data of GNSS and INS (inertial navigation system) are further fused to improve the accuracy and reliability of positioning.

5. The method for cooperative operation of an airborne drone system for an unmanned excavator according to claim 2, characterized in that: in step S3, the specific process is as follows: S31. Scene-adaptive positioning method driven by data and model: Based on model-based collaborative positioning, the airborne communication and navigation network modules of unmanned excavators and mining trucks are used to automatically network and collect data from different scenes. Based on the 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 of digital-analog combination. The data-driven method optimizes the positioning process through machine learning algorithms to improve the accuracy and robustness of positioning. S32. Cooperative positioning of unmanned excavators and mining trucks: Construct three-dimensional scene information as the information source of comprehensive PNT, and provide comprehensive PNT support at different levels, including low-level scene spatial structure information, texture features, topological road networks, etc. to improve positioning capabilities, and high-level electromagnetic wave three-dimensional space-time calculations, scene knowledge graphs, etc. to eliminate multipath effects, and realize cooperative positioning of unmanned excavators and mining trucks.

6. The method for cooperative operation of an airborne drone system for an unmanned excavator according to claim 1, characterized in that: in step S4, the specific process is as follows: S41, Time Synchronization: Construct software and hardware time synchronization trigger mechanisms for multi-sensor data signals such as lidar, visual camera, millimeter-wave radar, and inertial, and design multi-source information synchronization acquisition modules to support subsequent data fusion processing; S42, roadside perception and positioning: Based on the roadside perception module, combined with multiple sensors such as lidar, millimeter-wave radar, camera, radar, etc., it realizes all-weather object recognition and positioning. It uses multi-sensor fusion, instance segmentation, and six-degree-of-freedom pose estimation deep learning algorithms to detect objects in the working environment and perform accurate three-dimensional pose estimation; S43, vehicle-mounted perception and positioning: Based on the vehicle-mounted perception module, combined with multiple sensors such as laser radar, millimeter-wave radar, camera, radar, etc., using multi-sensor fusion SLAM (Simultaneous Localization And Mapping), the autonomous perception and positioning of the unmanned excavator and the mining truck is realized; S44. High-precision absolute positioning of unmanned excavator and mining truck in vehicle-road collaboration: Combine the methods of steps S3 and S42, and construct geographic coordinate transmission constraint factors with the results of GPS and roadside identification, positioning and attitude determination. Construct relative posture constraint factors with the high-precision collaborative positioning method of unmanned excavator and mining truck in combination with communication and navigation and digital-analog combination. Use the factor graph fusion method to realize the integrated positioning of communication and navigation, model and perception methods.

Images