CN113791619B - Airport automatic driving tractor dispatching navigation system and method - Google Patents
Airport automatic driving tractor dispatching navigation system and method Download PDFInfo
- Publication number
- CN113791619B CN113791619B CN202111072114.3A CN202111072114A CN113791619B CN 113791619 B CN113791619 B CN 113791619B CN 202111072114 A CN202111072114 A CN 202111072114A CN 113791619 B CN113791619 B CN 113791619B
- Authority
- CN
- China
- Prior art keywords
- tractor
- information
- node
- aircraft
- road side
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 22
- 238000004891 communication Methods 0.000 claims description 40
- 230000008569 process Effects 0.000 claims description 8
- 230000010365 information processing Effects 0.000 claims description 6
- 230000003068 static effect Effects 0.000 claims description 5
- 230000004927 fusion Effects 0.000 claims description 4
- 230000005540 biological transmission Effects 0.000 claims description 3
- 230000006870 function Effects 0.000 claims description 3
- 238000010295 mobile communication Methods 0.000 claims description 3
- 230000007613 environmental effect Effects 0.000 description 5
- 238000012545 processing Methods 0.000 description 5
- 238000003032 molecular docking Methods 0.000 description 2
- 230000008447 perception Effects 0.000 description 2
- 230000007704 transition Effects 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000004888 barrier function Effects 0.000 description 1
- 239000012141 concentrate Substances 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 239000003550 marker Substances 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0219—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory ensuring the processing of the whole working surface
Landscapes
- Engineering & Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention relates to the technical field of aircraft traction operation, in particular to an airport autopilot tractor dispatching navigation system and method.
Description
Technical Field
The invention relates to the technical field of aircraft traction operation, in particular to a dispatching navigation system and a dispatching navigation method for an airport automatic steering tractor.
Background
The rapidly growing volume of airborne traffic greatly increases the operating pressure of airport scenes. In order to improve the safety and efficiency of tractor operation, a series of related technical researches are also carried out in the universities and institutions at home and abroad, and mainly concentrate on one direction, namely, the traffic situation information around the tractor is obtained by additionally installing a sensor on the tractor, and the information affecting the operation safety is provided for a tractor driver through a visual interface. However, this approach still fails to address the problems of stringent tractor driver requirements and operator personal hazards, the need for multiple personnel cooperation, and inefficiency.
Disclosure of Invention
The technical problem that this patent to be solved is to provide a airport autopilot tractor dispatch navigation method and system in order to guarantee tractor operation personnel's safety and improve the operating efficiency based on the above-mentioned demand of prior art.
In order to solve the above-mentioned problem, the technical scheme that this patent provided includes:
there is provided an airport autopilot tractor dispatch navigation system comprising: the cloud control system comprises a tractor matching system and a real-time navigation map generation system; the tractor matching system retrieves an autopilot tractor corresponding to the type of the aircraft to be towed and sends an assignment command and information of the aircraft to be towed to a tractor which is idle and closest to the aircraft to be towed; the real-time navigation map generation system abstracts an airport scene map into a scene network map, and generates a real-time navigation map through data information transmitted by the intelligent road side system; the intelligent road side system comprises a road side communication unit and a multi-element road side sensing unit; the multi-road side sensing unit comprises a laser radar and a camera, point cloud data are acquired through the laser radar, and video data are acquired through the camera; the automatic driving platform comprises multiple sensors, a sensing unit, a decision planning unit and a control unit; the multi-sensor respectively collects the position, the gesture, the video and the point cloud data of the aircraft to be towed; the sensing unit is used for acquiring data information transmitted by the multiple sensors in a distributed fusion manner; the decision-making planning unit is divided into three layers of path planning, behavior decision-making and motion planning, the path planning layer generates a global path, the behavior decision-making layer receives the path, combines the received information of the sensing unit to make a behavior decision, the motion planning layer plans a characteristic track according to the behavior decision-making, the track is a final driving path of the automatic driving traction plan, and the control unit controls the tractor to travel according to the obtained track; the cloud control center, the intelligent road side system and the autopilot platform are used for information transmission through a 5G aviation airport mobile communication system.
Preferably, the path dividing layer generates a global path by adopting an a-algorithm, a real-time navigation map at a certain moment can be regarded as a static road network, the road network is simplified into small squares, the small squares are combined to form a final found path, an initial node and a target node are respectively a starting point and an end point of the path, the priority of each node is calculated through a heuristic function f (n) =g (n) +h (n), f (n) is the comprehensive priority of the node n, when the next node to be traversed is selected, the node with the highest comprehensive priority is always selected, g (n) is the cost of the node n from the starting point, and h (n) is the expected cost of the node n from the end point.
Preferably, in the operation process of the a-algorithm, each time, a node with the highest priority is selected from the priority queue as a next node to be traversed, and a shortest path is finally determined.
Preferably, the motion planning layer plans the motion trail expected by the vehicle in local space and time according to the environmental information, the upper layer decision and the real-time pose information of the vehicle body, and the motion trail comprises trail, speed, direction and state.
Preferably, the cloud control center further comprises an information processing system, the information processing system acquires all flight information of the same day from the airport operation control center, the flight information is arranged in the system according to the estimated departure time of each aircraft and the time sequence from the early to the late, and the departure aircraft waiting queue is formed.
Preferably, the cloud control center comprises a communication system, the intelligent road side system comprises a road side communication unit, the automatic driving platform comprises a communication unit, the road side communication unit transmits the point cloud data and the video data obtained by the multi-road side sensing unit to the communication system, and the cloud control center processes the received data to obtain an implementation navigation map and transmits the implementation navigation map to the communication unit through the communication system.
The invention also provides a dispatching navigation method of the airport automatic driving tractor, which comprises the following steps: the method comprises the steps that S1, a cloud control center obtains flight information, all the flight information is arranged in time sequence to form an aircraft waiting queue, an aircraft to be towed sends out a push-out application before the aircraft is expected to leave a port, and meanwhile, the information of the aircraft waiting queue is sent to the cloud control center, wherein the information comprises the model, the position, the gesture and the target position of the aircraft; s2, a tractor matching system of the cloud control center searches an autopilot tractor corresponding to the type of the aircraft to be towed according to the aircraft to be towed from which the application is sent, searches the tractor closest to the aircraft to be towed in the same kind of idle autopilot tractors, and sends an assignment command and information of the aircraft to be towed to the tractor matching system; s3, a communication unit on the automatic driving tractor in an idle state receives an assignment command and sends a wake-up signal to an automatic driving platform on the automatic driving tractor, and the automatic driving platform starts each unit and enters a preparation state; s4, the cloud control center generates a real-time navigation map on the basis of an airport scene map by processing data acquired by a multi-road-side sensing unit in an intelligent road-side system and sends the real-time navigation map to a tractor, wherein the multi-road-side sensing unit comprises a laser radar and a camera, and the data comprises point cloud data acquired by the laser radar and video data acquired by the camera; s5, the automatic driving platform obtains a starting point position of traction operation according to information of the aircraft to be towed, and an optimal route to the starting point position is planned according to a real-time navigation map; the automatic driving tractor travels to the starting point position according to the optimal route.
Preferably, the communication unit of the autopilot platform is kept in a long-term on-line state, while the other units of the autopilot platform are in a standby state for a long time, and when the communication unit receives the assignment command sent by the cloud control center, the communication unit sends a wake-up signal to the other units in the standby state for a long time, so that the autopilot tractor enters a standby state.
Compared with the prior art, the full-automatic docking of the automatic driving tractor and the airplane to be towed can be realized, the safety of operators is ensured, the manual labor capacity is reduced, and the operation efficiency and the operation precision are improved.
Drawings
In order to more clearly illustrate the embodiments of the present description or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the embodiments of the present description, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
FIG. 1 is a flow chart of steps of a method for dispatch navigation of an airport autopilot tractor of the present invention;
fig. 2 is a diagram of an airport automatic driving tractor dispatching navigation system architecture of the present invention.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
For the purpose of facilitating an understanding of the embodiments of the present application, reference will now be made to the following description of specific embodiments, taken in conjunction with the accompanying drawings, in which the embodiments are not intended to limit the embodiments of the present application.
Example 1
The present embodiment provides an airport autopilot tractor dispatch navigation system, referring to fig. 2.
The airport automatic driving tractor dispatching navigation system comprises a cloud control center, an intelligent road side system and an automatic driving platform, wherein the cloud control center, the intelligent road side system and the automatic driving platform are used for carrying out information transmission through a 5G aeroMACS (Aeronautical Mobile Airport Communications System air-port mobile communication system).
The cloud control center comprises a communication system, an information processing system, a tractor matching system and a real-time navigation map generation system.
The communication system communicates with the autopilot platform and the intelligent roadside system through 5G aeroMACS.
The information processing system acquires all flight information of the same day from the airport operation control center, sorts the flight information from the early to the late according to the predicted departure time of each aircraft and arranges the flight information in the system to form a departure aircraft waiting queue.
The tractor matching system comprises tractors of various types and corresponds to various types of aircrafts, and the tractor matching system searches idle tractors corresponding to the types of the aircrafts from which the application is sent out and matches the idle tractors with the actual types of the aircrafts.
The real-time navigation map generation system firstly abstracts an airport scene map into a scene network map, wherein the scene network map comprises nodes and paths.
The communication system transmits the data received by the multi-road-side sensing unit in the intelligent road-side system to the real-time current map generation system, and the real-time navigation map is generated by processing the point cloud data and the video data acquired by the multi-road-side sensing unit and marking barrier and prompt information on the basis of a scene network map.
The intelligent road side system comprises a road side communication unit, a multi-element road side sensing unit and a sign line.
The road side communication unit is communicated with the cloud control center and the automatic driving platform through 5G aeroMACS.
The multi-component road side sensing unit comprises a laser radar and a camera, and the laser radar and the camera are installed on an airport scene.
The laser radar uses 32-line laser radars with a horizontal view angle and a vertical view angle of 360 degrees and 40 degrees respectively, the laser radars are used for collecting point cloud data of the position where the laser radars are located, and the point cloud data are used for assisting in generating a real-time navigation map.
The camera uses a CMOS camera, has the advantages of flexible image capturing, high sensitivity, wide dynamic range and high resolution, and is used for acquiring airport scene video images.
The sign line is used for providing the environmental information of the location and defining the lane, providing assistance for the automatic driving tractor, and assisting the tractor to move to the target position on the airport scene. The marker line is identified by a camera on the autopilot platform, which, illustratively, identifies a lane line that assists the tractor in lane keeping; a dynamic traffic sign like a traffic light is arranged at the crossing position.
The automatic driving platform comprises a plurality of sensors, a communication unit, a sensing unit, a decision planning unit and a control unit, and is an execution main body of the method in the embodiment of the invention.
The multi-sensor comprises a high-precision positioning module, a camera and a laser radar.
The high-precision positioning module consists of a satellite antenna, an inertial/satellite combined navigation host and upper computer software and is respectively used for receiving satellite signals, calculating and providing multi-parameter navigation information, assisting in positioning, analyzing data and the like.
The camera uses a GigE camera to transmit image data at high speed for capturing video images and identification lines in front of the tractor.
The laser radar uses a 32-line laser radar with a horizontal view angle and a vertical view angle of 360 degrees and 40 degrees respectively, and is used for detecting obstacles around the tractor and sending environmental information to the decision planning unit.
The communication unit is communicated with the cloud control center and the intelligent road side system through the 5G aeroMACS.
The sensing unit is used for receiving information acquired by the multiple sensors and carrying out distributed fusion on the information, namely, carrying out local processing on the original data acquired by each independent sensor, and then sending the result into the information fusion center for intelligent optimization and combination to acquire a final result.
The decision planning unit is divided into three layers: path planning, behavior decision-making, and motion planning. Firstly, a path planning layer generates a global path, a behavior decision layer combines information received from a sensing unit to make a specific behavior decision after receiving the global path, and finally, a motion planning layer plans and generates a track meeting specific constraint conditions according to the specific behavior decision, and the track is used as input of a control unit to determine the final running path of the vehicle.
The path planning layer performs planning, also called navigation planning, on the global path, and performs path planning by adopting an a-algorithm. The algorithm a is a direct search method for solving the shortest path in a static road network, and a real-time navigation map at a certain moment can be regarded as a static road network. Firstly, simplifying the area to be searched into small squares, and finally finding a path which is a combination of some small squares, wherein an initial node and a target node respectively represent the starting point and the end point of the path. The a algorithm calculates the priority of each node by the heuristic function f (n) =g (n) +h (n). f (n) is the comprehensive priority of node n, and when the node to be traversed next is selected, the node with the highest comprehensive priority (the smallest value) is always selected. g (n) is the cost of node n from the start point. h (n) is the expected cost of node n from the endpoint. In the operation process of the algorithm, a node with the smallest f (n) value (highest priority) is selected from the priority queue each time to serve as a next node to be traversed, and a shortest path is finally determined.
Behavior decision, also called behavior planning, is to plan reasonable driving behavior under the constraint of traffic rules according to global planning route information, current traffic scene and environment perception information and current driving state. Here, a hierarchical finite state machine is used, in which a finite number of states are constructed, and external inputs can only cause the state machine to switch between these. The hierarchical finite state machine comprises the following parts: 1. input set: also called stimulus set, containing all inputs that the state machine may receive; 2. output set: i.e., the set of responses that the state machine can make; 3. using directed graphs to describe state and transition logic inside the state machine; 4. the state machine has a fixed initial state; 5. ending the state set; 6. transfer logic: i.e. the condition that the state machine transitions from one state to another.
According to the motion planning, according to local environment information, an upper layer decision task and real-time pose information of a vehicle body, under the condition that certain kinematic constraint is met, the planning decides a motion track expected by the vehicle in local space and time, including a running track, speed, direction, state and the like, and the information such as an expected vehicle speed, a running track and the like which are planned and output is fed into a control unit, so that a series of specific control signals for the vehicle can be finally generated, and the vehicle can run according to a planning target.
The control unit adopts PID control, and generates control commands for the bottom accelerator, the brake, the steering wheel and the gear shift lever of the tractor according to the planned running track and speed and the current position, posture and speed, so that the tractor runs along the target track at the target speed and acceleration.
And the cloud control center, the intelligent road side system and the automatic driving platform establish a communication network through a 5G aeroMACS by communication units contained in the respective systems to exchange data.
The method comprises the steps that laser radar and cameras installed in an airport field collect point cloud data and video data, the collected data are transmitted to a communication system of a cloud control center through a road side communication unit, the cloud control center processes received data information, a real-time navigation map generation system generates a real-time current map on the basis of an airport scene map and sends an updated map to a communication unit on an automatic driving platform through the communication system, and a decision planning unit plans and generates a track meeting specific constraint conditions by combining the received updated map information with information obtained by a perception unit, wherein the track is used as input of the control unit to determine a final driving path of a vehicle.
Example 2
The embodiment provides a dispatching navigation method of an airport automatic driving tractor, and the method is described with reference to fig. 1.
S1, a cloud control center acquires flight information, all the flight information is arranged in time sequence to form an aircraft waiting queue, an aircraft to be towed sends out a push-out application before the aircraft is expected to leave a port, and meanwhile, the information of the aircraft waiting queue is sent to the cloud control center, and the information comprises the model, the position, the gesture and the target position of the aircraft.
The cloud control center acquires all flight information of the same day from the airport operation control center, and according to the estimated departure time of each aircraft, the flight information is arranged in the system to form a departure aircraft waiting queue according to the sequence of time from the morning to the evening.
The aircraft to be towed issues a push-out application T minutes before the departure time, T being a value according to the distance of the aircraft from the towing vehicle parking, preferably in this embodiment 30, 40. Meanwhile, the aircraft sends the information to the cloud control center, the model of the aircraft in the information is used for matching with the waiting queue of the aircraft, and the position, the gesture and the target position are used for providing the tractor for navigation.
And S2, the tractor matching system of the cloud control center searches the automatic steering tractor corresponding to the type of the aircraft to be towed according to the aircraft to be towed from which the application is sent, searches the automatic steering tractor closest to the aircraft to be towed in the similar idle automatic steering tractors, and sends an assignment command and information of the aircraft to be towed to the automatic steering tractor.
In a tractor matching system of a cloud control center, an aircraft and a tractor are matched in real time.
The tractor matching system is provided with a plurality of types of tractors, the tractors respectively correspond to different types of aircrafts, and according to the types of the to-be-towed aircrafts which send out applications, the corresponding automatic steering tractors are matched in real time, and the automatic steering tractors have two states of working and idle.
The tractor matching system searches for the closest one of the idle tractors to the aircraft to be towed, and sends an assignment command and the aircraft to be towed information to the closest one of the idle tractors through the communication system.
And S3, receiving an assignment command by a communication unit on the automatic driving tractor in an idle state, sending a wake-up signal to an automatic driving platform on the automatic driving tractor, starting each unit by the automatic driving platform, and entering a preparation state.
The autopilot platform comprises a communication unit that remains on-line for a long period of time, with other units of the autopilot platform being in standby state for a long period of time.
After receiving an assignment command sent by a cloud control center, the communication unit sends a wake-up signal to an automatic driving platform, the automatic driving platform starts a sensing unit, a decision planning unit and a control unit, and the automatic driving tractor enters a preparation state.
S4, the cloud control center generates a real-time navigation map on the basis of an airport scene map by processing data acquired by a multi-road-side sensing unit in the intelligent road-side system and sends the real-time navigation map to the tractor, the multi-road-side sensing unit comprises a laser radar and a camera, and the data comprises point cloud data acquired by the laser radar and video data acquired by the camera.
The lidar and the camera are mounted on an airport scene.
The real-time navigation map generation system of the cloud control center can provide navigation assistance for the tractor. Firstly, abstracting an airport scene map into a scene network map, wherein the scene network map comprises nodes and paths; and then, the obstacle and the prompt information are marked on the basis of the scene network map by processing the point cloud data and the video data acquired by the multi-road side sensing unit, so that a real-time navigation map is generated and sent to the tractor.
S5, the automatic driving platform obtains a starting point position of traction operation according to information of the aircraft to be towed, and an optimal route to the starting point position is planned according to the real-time navigation map; the automatic driving tractor travels to the starting point position according to the optimal route.
The starting point of the towing operation is arranged at L m right in front of the aircraft to be towed, and L is 10-15m. From this position, the tractor performs subsequent docking operations with the aircraft and towing operations.
And according to the real-time navigation map provided by the cloud control center, the decision planning unit of the automatic driving platform plans an optimal route to the traction operation starting point.
The automatic driving platform comprises a decision planning unit, wherein the decision planning unit adopts an A-type algorithm to plan a path, constructs a feasible path from a starting point to an end point by means of a known environment map and obstacle information in the map, and selects one of the feasible paths with the shortest driving time as the optimal route to the starting point of traction operation.
It should be noted that, the optimal route may have an obstacle, the static obstacle requires the tractor to avoid the obstacle autonomously, and the dynamic obstacle may generate a waiting time, and the optimal route may reach the start point of the traction operation in any case.
In the process that the tractor runs to the traction operation starting point, the multi-sensor of the automatic driving platform continuously collects surrounding environment information and feeds back the surrounding environment information to the sensing unit of the automatic driving platform, and the surrounding environment information is processed and then sent to the decision planning unit.
The high-precision positioning module realizes high-precision positioning, the camera acquires a front video image and a recognition mark line of the tractor, and the laser radar acquires the cloud data of the surrounding points of the tractor.
Meanwhile, in the process that the tractor runs to the traction operation starting point, the road side communication unit of the intelligent road side system continuously sends the environmental information of the position to the automatic driving platform, wherein the environmental information comprises whether an obstacle exists at the position, the obstacle information, the passable time and the like.
The decision planning unit of the automatic driving platform analyzes and processes the information received from the sensing unit and the road side communication unit to make decisions, and the control unit controls the motion state of the tractor.
By implementing the method provided by the embodiment of the invention, the dispatching navigation of the airport automatic driving tractor can be realized, the burden of workers is reduced, and the safety and efficiency of traction operation are improved.
The foregoing embodiments have been provided for the purpose of illustrating the general principles of the present application, and are not meant to limit the scope of the invention, but to limit the scope of the invention.
Claims (3)
1. An airport autopilot tractor dispatch navigation system comprising:
the cloud control system comprises a tractor matching system, a real-time navigation map generating system and an information processing system;
the tractor matching system retrieves an autopilot tractor corresponding to the type of the aircraft to be towed and sends an assignment command and information of the aircraft to be towed to a tractor which is idle and closest to the aircraft to be towed; the real-time navigation map generation system abstracts an airport scene map into a scene network map, and generates a real-time navigation map through data information transmitted by the intelligent road side system; the information processing system acquires all flight information of the same day from an airport operation control center, sorts the flight information from the early to the late according to the predicted departure time of each aircraft and arranges the flight information in the system to form a departure aircraft waiting queue;
the intelligent road side system comprises a road side communication unit and a multi-element road side sensing unit;
the multi-road side sensing unit comprises a laser radar and a camera, point cloud data are acquired through the laser radar, and video data are acquired through the camera;
and an autopilot platform on the autopilot tractor, the autopilot platform comprising a multi-sensor, a sensing unit, a decision planning unit and a control unit; the multi-sensor respectively collects the position, the gesture, the video and the point cloud data of the aircraft to be towed; the sensing unit is used for acquiring data information transmitted by the multiple sensors in a distributed fusion manner; the decision planning unit is divided into three layers of path planning, behavior decision and motion planning, the path planning layer generates a global path, the path planning layer adopts an A-algorithm to generate the global path, a real-time navigation map at a certain moment can be regarded as a static road network, the road network is simplified into small squares, the combination of the small squares is a finally found path, an initial node and a target node are respectively a starting point and an end point of the path, the priority of each node is calculated through heuristic functions f (n) =g (n) +h (n), wherein f (n) is the comprehensive priority of the node n, when the node to be traversed next is selected, the node with the highest comprehensive priority is always selected, g (n) is the cost of the node n from the starting point, and h (n) is the predicted cost of the node n from the end point; after the behavior decision layer receives the path, the behavior decision is made by combining the received information of the sensing unit, a characteristic track is planned by the motion planning layer according to the behavior decision, the track is a final driving path planned by automatic driving traction, and the motion planning layer plans the expected motion track of the vehicle in the local space and time according to the environment information, the upper layer decision and the real-time pose information of the vehicle body, wherein the motion track comprises a track, a speed, a direction and a state; the control unit controls the tractor to travel according to the obtained track;
the cloud control center, the intelligent road side system and the autopilot platform are used for information transmission through a 5G aviation airport mobile communication system.
2. An airport automatic driving tractor dispatching navigation system according to claim 1, wherein during the operation of the a-algorithm, each time a node with highest priority is selected from the priority queue as a next node to be traversed, a shortest path is finally determined.
3. The airport automatic driving tractor dispatching navigation system of claim 2, wherein the cloud control center comprises a communication system, the intelligent road side system comprises a road side communication unit, the automatic driving platform comprises a communication unit, the road side communication unit transmits the point cloud data and the video data obtained by the multi-road side sensing unit to the communication system, and the cloud control center processes the received data to obtain an implementation navigation map and transmits the implementation navigation map to the communication unit through the communication system.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111072114.3A CN113791619B (en) | 2021-09-14 | 2021-09-14 | Airport automatic driving tractor dispatching navigation system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111072114.3A CN113791619B (en) | 2021-09-14 | 2021-09-14 | Airport automatic driving tractor dispatching navigation system and method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113791619A CN113791619A (en) | 2021-12-14 |
| CN113791619B true CN113791619B (en) | 2024-04-12 |
Family
ID=79183239
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111072114.3A Active CN113791619B (en) | 2021-09-14 | 2021-09-14 | Airport automatic driving tractor dispatching navigation system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113791619B (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114512014A (en) * | 2021-12-31 | 2022-05-17 | 中智行(上海)交通科技有限公司 | Lane-level auxiliary driving method based on vehicle-road system |
| CN114739381B (en) * | 2022-03-01 | 2024-08-23 | 仓擎智能科技(上海)有限公司 | Airport vehicle navigation system and method |
| CN115230707B (en) * | 2022-09-23 | 2023-06-27 | 江苏天一航空工业股份有限公司 | Unmanned control method, device and system for bulk cargo vehicle |
| CN116339347B (en) * | 2023-04-24 | 2023-10-31 | 广东工业大学 | Unmanned vehicle running path planning method, device and equipment |
| CN117218908B (en) * | 2023-09-14 | 2024-04-26 | 中国民航大学 | Guiding system and method for optimizing driving behavior of mopless aircraft tractor |
| CN117850436B (en) * | 2024-03-08 | 2024-06-14 | 民航机场成都电子工程设计有限责任公司 | Unmanned trailer running route avoiding method, device, equipment and medium |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104299455A (en) * | 2014-10-27 | 2015-01-21 | 重庆布伦坦茨航空技术进出口有限公司 | Automatic driving system for airport special vehicle |
| CN104750107A (en) * | 2015-03-23 | 2015-07-01 | 中国民航大学 | Intelligent management system for airport luggage tractor |
| CN206231442U (en) * | 2016-10-18 | 2017-06-09 | 上汽通用汽车有限公司 | Electric traction trailer |
| CN107063258A (en) * | 2017-03-07 | 2017-08-18 | 重庆邮电大学 | A kind of mobile robot indoor navigation method based on semantic information |
| CN107703944A (en) * | 2017-10-23 | 2018-02-16 | 中国民用航空飞行学院 | A kind of airport ground aircraft automated intelligent trailer system and method |
| CN110268353A (en) * | 2017-02-17 | 2019-09-20 | 科尔摩根自动化有限公司 | Method for controlling the driving path of automatically guided vehicle |
| CN110262508A (en) * | 2019-07-06 | 2019-09-20 | 深圳数翔科技有限公司 | Applied to the automated induction systems and method on the closing unmanned goods stock in place |
| CN111310383A (en) * | 2020-01-17 | 2020-06-19 | 中国民航大学 | HTCPN-based airport tractor dynamic optimization scheduling method |
| CN111623793A (en) * | 2020-07-01 | 2020-09-04 | 北京博能科技股份有限公司 | Navigation method and device for vehicles in flight area and electronic equipment |
| CN112799409A (en) * | 2021-01-29 | 2021-05-14 | 中科大路(青岛)科技有限公司 | Ground traffic management and control integrated system for airport based on vehicle-road cloud collaborative architecture |
| CN113127590A (en) * | 2021-04-09 | 2021-07-16 | 中移智行网络科技有限公司 | Map updating method and device |
| CN113359752A (en) * | 2021-06-24 | 2021-09-07 | 中煤科工开采研究院有限公司 | Automatic driving method for underground coal mine skip car |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6600992B2 (en) * | 2001-05-17 | 2003-07-29 | Airborne Holding, Llc | Airport ground navigation system |
| US11591757B2 (en) * | 2019-04-17 | 2023-02-28 | Caterpillar Paving Products Inc. | System and method for machine control |
-
2021
- 2021-09-14 CN CN202111072114.3A patent/CN113791619B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104299455A (en) * | 2014-10-27 | 2015-01-21 | 重庆布伦坦茨航空技术进出口有限公司 | Automatic driving system for airport special vehicle |
| CN104750107A (en) * | 2015-03-23 | 2015-07-01 | 中国民航大学 | Intelligent management system for airport luggage tractor |
| CN206231442U (en) * | 2016-10-18 | 2017-06-09 | 上汽通用汽车有限公司 | Electric traction trailer |
| CN110268353A (en) * | 2017-02-17 | 2019-09-20 | 科尔摩根自动化有限公司 | Method for controlling the driving path of automatically guided vehicle |
| CN107063258A (en) * | 2017-03-07 | 2017-08-18 | 重庆邮电大学 | A kind of mobile robot indoor navigation method based on semantic information |
| CN107703944A (en) * | 2017-10-23 | 2018-02-16 | 中国民用航空飞行学院 | A kind of airport ground aircraft automated intelligent trailer system and method |
| CN110262508A (en) * | 2019-07-06 | 2019-09-20 | 深圳数翔科技有限公司 | Applied to the automated induction systems and method on the closing unmanned goods stock in place |
| CN111310383A (en) * | 2020-01-17 | 2020-06-19 | 中国民航大学 | HTCPN-based airport tractor dynamic optimization scheduling method |
| CN111623793A (en) * | 2020-07-01 | 2020-09-04 | 北京博能科技股份有限公司 | Navigation method and device for vehicles in flight area and electronic equipment |
| CN112799409A (en) * | 2021-01-29 | 2021-05-14 | 中科大路(青岛)科技有限公司 | Ground traffic management and control integrated system for airport based on vehicle-road cloud collaborative architecture |
| CN113127590A (en) * | 2021-04-09 | 2021-07-16 | 中移智行网络科技有限公司 | Map updating method and device |
| CN113359752A (en) * | 2021-06-24 | 2021-09-07 | 中煤科工开采研究院有限公司 | Automatic driving method for underground coal mine skip car |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113791619A (en) | 2021-12-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113791619B (en) | Airport automatic driving tractor dispatching navigation system and method | |
| CN111104849B (en) | Automatic annotation of environmental features in a map during navigation of a vehicle | |
| US11011064B2 (en) | System and method for vehicle platooning | |
| CN112558608B (en) | Vehicle-mounted machine cooperative control and path optimization method based on unmanned aerial vehicle assistance | |
| GB2613509A (en) | Redundancy in autonomous vehicles | |
| CN113165668A (en) | Operating a vehicle using motion planning with machine learning | |
| CN113196011A (en) | Motion map construction and lane level route planning | |
| CN113359752A (en) | Automatic driving method for underground coal mine skip car | |
| CN107031600A (en) | Automated driving system based on highway | |
| CN109765909B (en) | Method for applying V2X system in port | |
| EP4163595A1 (en) | Automatic annotation of environmental features in a map during navigation of a vehicle | |
| WO2022072412A1 (en) | Methods and systems for performing outlet inference by an autonomous vehicle to determine feasible paths through an intersection | |
| US12049238B2 (en) | Systems and methods for autonomous vehicle motion control and motion path adjustments | |
| US20220219720A1 (en) | Methods and system for constructing data representation for use in assisting autonomous vehicles navigate intersections | |
| US12030485B2 (en) | Vehicle operation using maneuver generation | |
| CN115079687A (en) | System, method, and storage medium for autonomous vehicles | |
| EP4285083A1 (en) | Methods and system for generating a lane-level map for an area of interest for navigation of an autonomous vehicle | |
| KR20230033551A (en) | Navigation with drivable area detection | |
| CN114264306A (en) | Indoor and outdoor combined distribution robot and distribution method | |
| US20230280180A1 (en) | Systems and methods for performing data collection missions | |
| US12043282B1 (en) | Autonomous vehicle steerable sensor management | |
| US20240351610A1 (en) | Autonomous vehicle steerable sensor management | |
| US20240103522A1 (en) | Perception system for an autonomous vehicle | |
| US20230286529A1 (en) | Systems and methods for dynamic data mining during data collection missions | |
| US11975726B1 (en) | Systems and methods for interaction-based trajectory prediction |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |