Autonomous navigation and collaborative working system of unmanned equipment in signal-free area and method thereof
By constructing a local collaborative control architecture using self-organizing network devices and an edge computing platform, the problem of collaborative operation of unmanned equipment in environments without public network signals is solved, achieving low-latency, high-reliability collaborative operation of equipment and robust positioning, adapting to the dynamic environment of construction scenarios.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-02-13
- Publication Date
- 2026-06-23
AI Technical Summary
Existing unmanned equipment cannot achieve collaborative operation between devices in environments without public network signals. Its positioning accuracy is insufficient and its robustness is poor. Reliance on cloud scheduling leads to high communication latency, which cannot meet the multi-task collaborative needs of construction scenarios.
A local collaborative control architecture is built using self-organizing network devices and an edge computing platform. Low-latency data interaction between devices is achieved through the wireless network of the self-organizing network devices. Local and global dynamic maps are constructed by combining visual SLAM algorithm and time difference positioning method. Edge algorithms are used for local task scheduling and path planning.
It enables highly reliable data interaction and low-latency collaborative operation between devices in signal-free environments, improves positioning robustness, avoids path conflicts, adapts to the dynamic environment of construction scenarios, and reduces operating costs and safety risks.
Smart Images
Figure CN122261137A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of building construction technology, specifically to an unmanned equipment autonomous navigation and collaborative operation system and method for areas without signal coverage. Background Technology
[0002] In the current intelligent upgrade of the construction engineering field, the application of unmanned equipment such as inspection robot dogs and drones, as well as Bluetooth positioning of safety helmets, is gradually becoming widespread, becoming a core means to improve construction efficiency and reduce human safety risks. The operation of existing unmanned equipment relies on point-to-point private networks and public network communication to achieve cloud scheduling, positioning data transmission, and inter-device collaboration. The positioning method mostly adopts a single GPS, and the collaborative operation architecture is mainly based on centralized cloud scheduling.
[0003] The existing unmanned equipment operation has the following drawbacks: 1. Communication interruption in no-signal environment: In construction scenarios without public network signals, unmanned equipment cannot rely on the public network to issue commands, transmit data back, and exchange information between devices. It can only operate independently as a single device and cannot meet the needs of multi-task collaboration.
[0004] 2. Insufficient positioning accuracy and robustness: Single positioning technology is easily affected by factors such as obstruction, changes in light, and complex terrain in construction scenarios, resulting in large positioning deviations; moreover, the positioning data between devices are independent of each other and there is no sharing mechanism, which makes it impossible to form a global positioning perspective, leading to positioning conflicts when multiple devices are operating.
[0005] 3. Collaborative operations rely on the cloud and lack local scheduling capabilities: Existing collaborative operation architectures all rely on cloud servers for task allocation and path planning. Without a public network environment, the cloud cannot intervene, and the devices lack local decentralized collaborative control capabilities, which can easily lead to problems such as path conflicts and untimely obstacle avoidance, resulting in low operation efficiency.
[0006] 4. High communication and scheduling latency: Even in construction scenarios with public networks, long-distance data transmission for cloud scheduling will result in high latency, which cannot meet the low-latency requirements of dynamic equipment operation in construction scenarios. Summary of the Invention
[0007] To overcome the shortcomings of existing technologies, this paper provides an autonomous navigation and collaborative operation system and method for unmanned equipment in areas without signal coverage, in order to solve the problem of high dependence on public networks in existing unmanned equipment operations.
[0008] To achieve the above objectives, an autonomous navigation and collaborative operation system for unmanned equipment in signal-free areas is provided, comprising: Self-organizing network devices; Multiple types of unmanned operation equipment, the unmanned operation equipment being communicatively connected to the self-organizing network, the unmanned operation equipment being equipped with a first positioning module for collecting first positioning data of the unmanned operation equipment, a sensing module for collecting environmental data, an attitude sensor for collecting attitude data of the unmanned operation equipment, and a mapping module for constructing a local dynamic map of the construction area based on the environmental data and the attitude data; The second positioning module includes at least three wireless micro-stations. The safety helmet of the construction worker is equipped with a positioning terminal. The positioning terminal is communicatively connected to the self-organizing network of the self-organizing network device. The positioning module locates the safety helmet by time difference positioning method. The edge computing platform includes a control module, a storage module, a fusion module for generating a global dynamic map based on the local dynamic map of multiple types of unmanned operating equipment and the first positioning data, and a path planning module for generating an initial operation path based on the construction task and the global dynamic map. The control module is communicatively connected to the ad hoc network, and the storage module, the fusion module, and the path planning module are respectively connected to the control module. The unmanned operation equipment receives the initial operation path and executes the construction task according to the initial operation path. During the operation, the unmanned operation equipment broadcasts its first positioning data and the initial operation path in real time through the self-organizing network. If a potential conflict is detected, it immediately performs a preliminary obstacle avoidance operation and uploads the conflict information to the edge computing platform. The fusion module updates the global dynamic map, and the path planning module re-plans and generates an updated operation path.
[0009] Furthermore, the mapping module constructs the local dynamic map based on the environmental data and the attitude data using a visual SLAM algorithm.
[0010] Furthermore, the fusion module uses a map fusion algorithm to generate the global dynamic map based on the local dynamic map and the first positioning data.
[0011] Furthermore, the perception module includes a visual perception unit and a lidar.
[0012] Furthermore, the unmanned operation equipment can be an inspection robot dog, a drone, a robotic arm, or an unmanned excavator.
[0013] This invention provides a method for autonomous navigation and collaborative operation of unmanned equipment in signal-free areas using an unmanned equipment autonomous navigation and collaborative operation system, comprising the following steps: Install self-organizing network equipment in areas with no signal to form a self-organizing network; The positioning modules on various types of unmanned operating equipment and the safety helmets of construction workers are communicatively connected to the self-organizing network; The control module of the edge computing platform issues a start command, and the unmanned operation equipment receives and responds to the start command; The positioning module collects the first positioning data of the unmanned operation equipment, the sensing module collects environmental data, and the attitude sensor collects the attitude data of the unmanned operation equipment. The mapping module constructs a local dynamic map of the construction area based on the environmental data and the attitude data; The control module acquires the local dynamic map and the first positioning data; The fusion module of the edge computing platform generates a global dynamic map by fusing the local dynamic maps of multiple types of unmanned operating equipment and the first positioning data; The control module acquires the construction task; The path planning module generates an initial work path based on the construction task and the global dynamic map; The control module issues the initial job path; The unmanned operation equipment receives the initial operation path and executes the construction task according to the initial operation path. During the operation, the unmanned operation equipment broadcasts its first positioning data and the initial operation path in real time through the self-organizing network. If a potential conflict is detected, it immediately performs a preliminary obstacle avoidance operation and uploads the conflict information to the edge computing platform. The fusion module updates the global dynamic map; Based on the updated global dynamic map, the path planning module re-plans and generates updated job paths; The control module issues the updated work path to instruct multiple types of unmanned work equipment to execute the updated work path.
[0014] The beneficial effects of this invention are that the unmanned equipment autonomous navigation and collaborative operation system in the signal-free area of this invention constructs a local collaborative control architecture without public network dependence through self-organizing wireless network and edge computing platform, realizing low-latency and high-reliability data interaction between devices in the signal-free construction area, and solving the core problem of communication interruption in the signal-free environment in traditional technology.
[0015] The autonomous navigation and collaborative operation system for unmanned equipment in signal-free areas of the present invention adopts a joint positioning method and combines a decentralized positioning data sharing mechanism to adapt to the obstruction and dynamic environment of the construction scene, significantly improving the positioning robustness.
[0016] The autonomous navigation and collaborative operation system for unmanned equipment in signal-free areas of the present invention is based on local task scheduling and collaborative path planning using edge algorithms, which enables distributed collaborative operation of multiple types of unmanned equipment in signal-free environments and avoids path conflicts. Attached Figure Description
[0017] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of an unmanned equipment autonomous navigation and collaborative operation system in a signal-free area according to an embodiment of the present invention.
[0018] Figure label: Self-organizing network device 1; Unmanned operation equipment 2, first positioning module 21, sensing module 22, attitude sensor 23, mapping module 24; Second positioning module 3; Edge computing platform 4, control module 41, storage module 42, fusion module 43, path planning module 44. Detailed Implementation
[0019] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.
[0020] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.
[0021] Reference Figure 1 As shown, the present invention provides an autonomous navigation and collaborative operation system for unmanned equipment in areas without signal coverage, comprising: a self-organizing network device 1, an unmanned operation device 2, a positioning module, and an edge computing platform 4.
[0022] In this embodiment, the self-organizing network device 1 is used to form a self-organizing network in the construction area where there is no signal, so as to provide wireless communication for the unmanned operation equipment 2, the positioning module, and the edge computing platform 4 to access.
[0023] Self-organizing network device 1 includes self-organizing network wireless nodes, lightweight wireless communication terminals, and network repeaters.
[0024] The self-organizing network device 1 deploys self-organizing network wireless nodes according to the area and terrain features of the construction zone, with a node spacing of ≤100 meters, achieving full wireless coverage of the construction area. The self-organizing network wireless nodes support self-organizing networks and dynamic expansion, and newly added nodes can automatically connect to the network. The edge computing platform connects to the network through the main self-organizing network wireless node, and each unmanned operation equipment and personnel safety helmet automatically connects to the network through the lightweight wireless communication terminal it is equipped with, with the communication link being bidirectional point-to-point transmission.
[0025] Core functions of self-organizing network device 1: (1) Achieve low-latency transmission of scheduling instructions, sensing data, positioning data and operation status data between the edge computing platform and each unmanned operation equipment, with a transmission delay of ≤100ms.
[0026] (2) Realize the real-time decentralized broadcasting and interaction of positioning data, obstacle avoidance information, and local map data of various unmanned operating equipment and personnel safety helmets.
[0027] (3) Supports dynamic network expansion to adapt to the communication needs of construction areas of different scales.
[0028] Unmanned operation equipment 2 comes in several types. Specifically, unmanned operation equipment 2 includes construction-specific inspection robot dogs and drones, which can be expanded to include construction robotic arms, unmanned excavators, and other unmanned construction equipment. All unmanned operation equipment features a modular design, equipped with a unified core functional unit, and is adapted to meet the specific operational needs of each task.
[0029] The unmanned operation equipment 2 is connected to an ad hoc network. The unmanned operation equipment 2 is equipped with a first positioning module, a sensing module 22, an attitude sensor 23, and a mapping module 24.
[0030] The first positioning module is used to collect the first positioning data of the unmanned operation equipment 2.
[0031] The sensing module 22 is used to collect environmental data of the environment in which the unmanned operating equipment 2 is located.
[0032] In a preferred embodiment, the perception module 22 includes a visual perception unit and a lidar. The visual perception unit can be a visual sensor, a camera, or an industrial camera. The visual perception unit acquires images of the external environment of the unmanned operating equipment. The lidar acquires point cloud data of the external environment of the unmanned operating equipment.
[0033] The attitude sensor 23 is used to collect attitude data of the unmanned operation equipment 2.
[0034] The mapping module 24 is used to construct a local dynamic map of the construction area based on environmental data and attitude data.
[0035] As a preferred implementation, the mapping module 24 constructs a local dynamic map based on environmental data and attitude data using a visual SLAM algorithm.
[0036] The core functions of unmanned operating equipment include: It automatically connects to the self-organizing wireless network in the construction area, enabling bidirectional data interaction with edge algorithms and other devices; By combining with the first positioning module to achieve its own high-precision positioning, a local dynamic map is generated, and the first positioning data and local map data are transmitted back to the edge computing platform in real time through the self-organizing network, while also being broadcast to other unmanned operating equipment; It receives sub-task instructions and path planning information from the edge computing platform and achieves autonomous navigation based on its own positioning data and a global dynamic map; It receives positioning and obstacle avoidance information from other unmanned operating equipment, achieves initial obstacle avoidance locally, and completes global dynamic obstacle avoidance in conjunction with edge algorithms.
[0037] The second positioning module includes at least three wireless micro-stations. Positioning terminals are installed on the safety helmets of construction workers. These positioning terminals are communicatively connected to the self-organizing network of self-organizing network device 1. The second positioning module locates the safety helmets using a time-difference positioning method.
[0038] The second positioning module is deployed as a wireless micro-station in the construction area. The second positioning module is placed at key locations within the construction area. The number of wireless micro-stations is at least 3, achieving triangulation positioning. The micro-stations complete unified coordinate calibration.
[0039] The core functions of the second positioning module include: Absolute positioning. The safety helmet's positioning terminal receives signals from multiple micro-stations and achieves its own absolute positioning through time difference positioning.
[0040] Decentralized location data sharing. Each device broadcasts its real-time location data and local dynamic map data to all other unmanned operating devices in real time via a self-organizing wireless network. Each unmanned operating device can independently integrate its own location data with the shared location data of other devices to form a local and global positioning perspective.
[0041] The edge computing platform 4 includes a control module 41, a storage module 42, a fusion module 43, and a path planning module 44.
[0042] The control module 41 is communicatively connected to the ad hoc network. The storage module 42, fusion module 43, and path planning module 44 are all connected to the control module 41.
[0043] The fusion module 43 is used to generate a global dynamic map by fusing local dynamic maps and first positioning data based on multiple types of unmanned operating equipment 2.
[0044] The path planning module 44 is used to generate an initial work path based on the construction task and the global dynamic map.
[0045] Edge computing platforms support local real-time data processing and path planning algorithm execution without relying on cloud computing power.
[0046] The core functions of an edge computing platform include: Communication and interaction functions. The edge computing platform connects to the self-organizing network through the self-organizing network communication master terminal, establishing stable two-way communication with all unmanned operating equipment to realize command issuance and data feedback.
[0047] Map fusion and update function. Receive local dynamic map data transmitted from various unmanned operating devices, fuse them to generate a global dynamic map of the construction area, and update the global map in real time based on environmental change information transmitted from the devices (such as new obstacles, material stacking, and terrain changes).
[0048] Task allocation function. Receives the total construction task input by staff through the local operation terminal of the edge computing platform, breaks down the total task into several sub-tasks according to task type and work area, and formulates the optimal task allocation plan based on the equipment type, working capacity, current location, remaining power, and working status of each unmanned operation equipment.
[0049] Collaborative path planning function. Based on a global dynamic map and real-time location data of each device, it plans conflict-free operation paths for each device. When device path conflicts are detected or sudden changes in the environment are detected, the path is replanned in real time to achieve dynamic obstacle avoidance.
[0050] The unmanned operation equipment 2 receives the initial operation path and executes the construction task according to the initial operation path. During the operation, the unmanned operation equipment 2 broadcasts its first positioning data and initial operation path in real time through an ad hoc network. If a potential conflict is detected, it immediately performs preliminary obstacle avoidance operations and uploads the conflict information to the edge computing platform 4. The fusion module 43 updates the global dynamic map. The path planning module 44 replans and generates an updated operation path.
[0051] As a preferred implementation, the fusion module 43 uses a map fusion algorithm to generate a global dynamic map based on the local dynamic map and the first positioning data.
[0052] This invention provides a method for autonomous navigation and collaborative operation of unmanned equipment in signal-free areas using an unmanned equipment autonomous navigation and collaborative operation system, comprising the following steps: S1. Install self-organizing network device 1 in the area without signal to form a self-organizing network.
[0053] S2. Connect the positioning modules on the safety helmets of construction workers and other unmanned operating equipment of various types to the self-organizing network.
[0054] In construction areas without public network signals, deploy self-organizing network wireless nodes according to deployment requirements to build a self-organizing network wireless network with full coverage. Connect the self-organizing network communication master terminal of the edge computing platform to the network core node, complete network debugging, and ensure communication without dead zones.
[0055] Connect all unmanned operating equipment and personnel safety helmets to the self-organizing wireless network, complete the communication pairing between each unmanned operating device and the edge computing platform, and test the stability and latency of the communication link.
[0056] Initialize and calibrate the first positioning module of each unmanned operation equipment, start the equipment to complete the joint debugging of positioning and SLAM mapping, ensure that the positioning accuracy meets the requirements, and that local map data can be generated and transmitted normally.
[0057] Enter the basic geographic information of the construction area and complete the system initialization parameter settings.
[0058] S3, the control module 41 of the edge computing platform 4 issues a start command, and the unmanned operation equipment 2 receives and responds to the start command.
[0059] S4, the positioning module collects the first positioning data of the unmanned operation equipment 2, the sensing module 22 collects environmental data, and the attitude sensor 23 collects the attitude data of the unmanned operation equipment 2.
[0060] The edge computing platform issues a device start command. After each unmanned operation device starts, it obtains its own absolute positioning coordinates. The visual perception unit of the perception module identifies visual beacons to calibrate the absolute positioning coordinates and obtain high-precision positioning data.
[0061] The mapping modules of each device combine environmental data collected by the visual perception unit and LiDAR. S5, Mapping Module 24 constructs a local dynamic map of the construction area based on environmental and attitude data.
[0062] The mapping module 24 constructs a local dynamic map of the construction area using a visual SLAM algorithm. Attitude sensors collect equipment attitude data in real time to help optimize map construction accuracy.
[0063] S6, Control module 41 acquires local dynamic map and first positioning data.
[0064] The control module 41 of the edge computing platform receives local dynamic map data and first positioning data transmitted back from all devices.
[0065] S7, the fusion module 43 of the edge computing platform 4 generates a global dynamic map by fusing local dynamic maps and first positioning data from multiple types of unmanned operating equipment 2.
[0066] The fusion module 43 generates a global dynamic map of the construction area through a map fusion algorithm, and updates the global dynamic map in real time based on the environmental change information returned by the unmanned operation equipment, while also sending the global dynamic map data to each device.
[0067] S8, control module 41 acquires construction tasks.
[0068] Construction workers input the overall construction task requirements into the edge computing platform, including key information such as task type (inspection / transportation / aerial photography, etc.), work area, task completion deadline, and work requirements.
[0069] Based on the overall construction task requirements, the control module breaks it down into several independently executable sub-tasks, and matches the equipment type requirements and operational capability requirements for each sub-task.
[0070] The control module combines the current location, remaining power, operating status, and equipment type of each unmanned operating device to formulate the optimal sub-task allocation scheme through a greedy algorithm, ensuring that task execution efficiency is maximized.
[0071] S9, Path Planning Module 44 generates an initial work path based on the construction task and the global dynamic map.
[0072] Based on the global dynamic map, the path planning module plans an initial conflict-free operation path for each unmanned operating device assigned to a sub-task.
[0073] S10, Control module 41 issues the initial operation path.
[0074] The control module 41 sends subtask instructions, initial operation path information, and the latest global dynamic map data to the corresponding unmanned operation equipment through the self-organizing wireless network.
[0075] S11. The unmanned operation equipment 2 receives the initial operation path and performs construction tasks according to the initial operation path. During the operation, the unmanned operation equipment 2 broadcasts its first positioning data and initial operation path in real time through the self-organizing network. If a potential conflict is detected, it immediately performs a preliminary obstacle avoidance operation and uploads the conflict information to the edge computing platform 4.
[0076] Each unmanned operation device combines its own real-time high-precision positioning data with a global dynamic map, and uses an autonomous navigation algorithm to control the device to move towards the target operation point and begin executing sub-tasks.
[0077] During the operation, each unmanned operating device broadcasts its own positioning data and operation path information in real time through an ad hoc wireless network, while receiving positioning and path information from other unmanned operating devices. The main control unit detects path conflicts in real time. If a potential conflict is detected, it immediately performs a local preliminary obstacle avoidance operation and sends back the conflict information and its own position information.
[0078] S12, Fusion Module 43 updates the global dynamic map.
[0079] S13. Based on the updated global dynamic map, the path planning module 44 replans and generates the updated job path.
[0080] After receiving path conflict information, the edge computing platform uses a collaborative path planning algorithm to replan conflict-free operation paths for conflicting devices based on a global dynamic map and real-time positioning data of all devices. The platform then sends path adjustment instructions to the corresponding devices, which complete the path adjustment upon receiving the instructions, thus achieving global dynamic obstacle avoidance.
[0081] S14, the control module 41 issues an updated work path to enable multiple types of unmanned work equipment 2 to execute the updated work path.
[0082] If the sensing module of the unmanned operation equipment detects a sudden change in the construction environment (such as the addition of obstacles, material stacking, or terrain collapse), it immediately transmits the environmental change information and location information back to the edge computing platform and broadcasts it to other unmanned operation equipment. Upon receiving the information, the edge computing platform immediately updates the global dynamic map. If the environmental change affects the equipment's operating path, it replans the path for the affected equipment to ensure continuous operation.
[0083] During the execution of sub-tasks, each unmanned operation equipment completes operations such as data collection, material transportation, and aerial photography according to the operation requirements, and transmits back real-time operation data (such as inspection hazard data, material transportation weight, and aerial images).
[0084] If an unmanned operating device detects an abnormal state such as low remaining power or equipment failure, it immediately sends the abnormal information back to the edge computing platform. Based on the actual situation, the unfinished sub-tasks of the unmanned operating device are reassigned to other idle unmanned operating devices to ensure the overall completion of the task.
[0085] After completing a subtask, an unmanned operation device transmits the subtask completion status and complete operation data back to the device. Upon receiving this data, the edge computing platform marks the subtask as completed and, based on the overall task completion status, determines whether there are any subsequent subtasks to be assigned. If so, it assigns a new subtask to the device; otherwise, it issues a return-to-home command.
[0086] The edge computing platform monitors the completion status of all sub-tasks in real time. When all sub-tasks are completed, the overall construction task is deemed complete. A unified return command is then issued to all devices, and the return point information is sent to each device.
[0087] After receiving the return command, each unmanned operating device autonomously navigates back to the designated return point based on its own positioning data and the global dynamic map, thus completing the operation.
[0088] The unmanned equipment autonomous navigation and collaborative operation system in signal-free areas of the present invention constructs a local collaborative control architecture that does not rely on the public network through a self-organizing wireless network and an edge computing platform, realizing low-latency and high-reliability data interaction between devices in signal-free construction areas, and solving the core problem of communication interruption in signal-free environments in traditional technologies.
[0089] The autonomous navigation and collaborative operation system for unmanned equipment in signal-free areas of the present invention adopts a joint positioning method and combines a decentralized positioning data sharing mechanism to adapt to the obstruction and dynamic environment of the construction scene, significantly improving the positioning robustness.
[0090] The autonomous navigation and collaborative operation system for unmanned equipment in signal-free areas of the present invention is based on local task scheduling and collaborative path planning using edge algorithms, which enables distributed collaborative operation of multiple types of unmanned equipment in signal-free environments and avoids path conflicts.
[0091] The autonomous navigation and collaborative operation system for unmanned equipment in signal-free areas of the present invention supports dynamic expansion of self-organizing network nodes, can adapt to construction areas of different sizes, and can flexibly expand the types of unmanned equipment and personnel safety helmets, possessing good compatibility and scalability.
[0092] The autonomous navigation and collaborative operation system for unmanned equipment in signal-free areas of this invention breaks through the limitations of public network signals on the operation of unmanned construction equipment, realizing low-latency communication, high-precision positioning and efficient collaborative operation in signal-free environments, filling the technical gap of multi-equipment collaborative operation in signal-free construction areas; the joint positioning method and data sharing mechanism significantly improve the positioning accuracy and operational robustness of the equipment, adapting to the complex dynamic environment of construction scenarios.
[0093] The autonomous navigation and collaborative operation system for unmanned equipment in signal-free areas of this invention can be widely applied to all construction scenarios without public network signals, such as construction, road and bridge, mining, and tunnels. It is suitable for various construction tasks such as inspection, transportation, aerial photography, and excavation. The types of unmanned equipment can be flexibly expanded. The system is simple to deploy and easy to debug, and has good practicality and compatibility.
[0094] The autonomous navigation and collaborative operation system for unmanned equipment in signal-free areas of this invention enables unmanned construction operations, significantly reducing manual input and lowering construction labor costs; it improves construction efficiency and task completion quality, and reduces construction losses caused by human error; the system operates locally without cloud computing costs, thus reducing operating costs.
[0095] The unmanned autonomous navigation and collaborative operation system for signal-free areas of this invention replaces manual labor in high-risk construction areas (such as mine tunnels, tunnel faces, and deep foundation pits), significantly reducing the safety risks for construction workers and minimizing the occurrence of safety accidents. Real-time environmental monitoring and hazard data collection provide precise data support for construction safety management and improve the overall safety of construction.
[0096] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.
Claims
1. An autonomous navigation and collaborative operation system for unmanned equipment in signal-free areas, characterized in that, include: Self-organizing network devices; Multiple types of unmanned operation equipment, the unmanned operation equipment being communicatively connected to the self-organizing network, the unmanned operation equipment being equipped with a first positioning module for collecting first positioning data of the unmanned operation equipment, a sensing module for collecting environmental data, an attitude sensor for collecting attitude data of the unmanned operation equipment, and a mapping module for constructing a local dynamic map of the construction area based on the environmental data and the attitude data; The second positioning module includes at least three wireless micro-stations. The safety helmet of the construction worker is equipped with a positioning terminal. The positioning terminal is communicatively connected to the self-organizing network of the self-organizing network device. The positioning module locates the safety helmet by time difference positioning method. The edge computing platform includes a control module, a storage module, a fusion module for generating a global dynamic map based on the local dynamic map of multiple types of unmanned operating equipment and the first positioning data, and a path planning module for generating an initial operation path based on the construction task and the global dynamic map. The control module is communicatively connected to the ad hoc network, and the storage module, the fusion module, and the path planning module are respectively connected to the control module. The unmanned operation equipment receives the initial operation path and executes the construction task according to the initial operation path. During the operation, the unmanned operation equipment broadcasts its first positioning data and the initial operation path in real time through the self-organizing network. If a potential conflict is detected, it immediately performs a preliminary obstacle avoidance operation and uploads the conflict information to the edge computing platform. The fusion module updates the global dynamic map, and the path planning module re-plans and generates an updated operation path.
2. The unmanned equipment autonomous navigation and collaborative operation system in signal-free areas according to claim 1, characterized in that, The mapping module constructs the local dynamic map based on the environmental data and the attitude data using a visual SLAM algorithm.
3. The autonomous navigation and collaborative operation system for unmanned equipment in signal-free areas according to claim 1, characterized in that, The fusion module uses a map fusion algorithm to generate the global dynamic map based on the local dynamic map and the first positioning data.
4. The autonomous navigation and collaborative operation system for unmanned equipment in signal-free areas according to claim 1, characterized in that, The perception module includes a visual perception unit and a lidar.
5. The autonomous navigation and collaborative operation system for unmanned equipment in signal-free areas according to claim 1, characterized in that, The unmanned operation equipment includes inspection robot dogs, drones, robotic arms, or unmanned excavators.
6. A method for autonomous navigation and collaborative operation of unmanned equipment in signal-free areas using the autonomous navigation and collaborative operation system for unmanned equipment in signal-free areas as described in any one of claims 1 to 5, characterized in that, Includes the following steps: Install self-organizing network equipment in areas with no signal to form a self-organizing network; The positioning modules on various types of unmanned operating equipment and the safety helmets of construction workers are communicatively connected to the self-organizing network; The control module of the edge computing platform issues a start command, and the unmanned operation equipment receives and responds to the start command; The positioning module collects the first positioning data of the unmanned operation equipment, the sensing module collects environmental data, and the attitude sensor collects the attitude data of the unmanned operation equipment. The mapping module constructs a local dynamic map of the construction area based on the environmental data and the attitude data; The control module acquires the local dynamic map and the first positioning data; The fusion module of the edge computing platform generates a global dynamic map by fusing the local dynamic maps of multiple types of unmanned operating equipment and the first positioning data; The control module acquires the construction task; The path planning module generates an initial work path based on the construction task and the global dynamic map; The control module issues the initial job path; The unmanned operation equipment receives the initial operation path and executes the construction task according to the initial operation path. During the operation, the unmanned operation equipment broadcasts its first positioning data and the initial operation path in real time through the self-organizing network. If a potential conflict is detected, it immediately performs a preliminary obstacle avoidance operation and uploads the conflict information to the edge computing platform. The fusion module updates the global dynamic map; Based on the updated global dynamic map, the path planning module re-plans and generates updated job paths; The control module issues the updated work path to instruct multiple types of unmanned work equipment to execute the updated work path.