阅读说明：本技术 一种机场自动驾驶牵引车调度导航系统及方法 (Dispatching navigation system and method for airport automatic driving tractor ) 是由 于海洋 余航 任毅龙 王吉祥 兰征兴 付翔 于 2021-09-14 设计创作，主要内容包括：本发明涉及航空器牵引作业技术领域,具体而言涉及一种机场自动驾驶牵引车调度导航系统及方法,所述系统包括,云端控制中心、自动驾驶平台以及智慧路测系统,三者通过5G航空空港移动通讯系统进行信息传输,所述云端控制系统将飞机信息进行处理,并将相应的牵引车和飞机匹配,并通过智慧路侧系统传输的道路信息形成实时导航地图,将所述地图通过通信系统传输至自动驾驶平台,所述自动驾驶平台根据相应的信息控制所述牵引车工作,最终实现与飞起的对接作业。(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 driving tractor, wherein the dispatching navigation system comprises a cloud control center, an automatic driving platform and an intelligent drive test system, the cloud control center, the automatic driving platform and the intelligent drive test system are used for carrying out information transmission through a 5G aviation airport mobile communication system, the cloud control system is used for processing airplane information, matching a corresponding tractor with an airplane, forming a real-time navigation map through road information transmitted by an intelligent road side system, transmitting the map to the automatic driving platform through a communication system, and the automatic driving platform is used for controlling the tractor to work according to corresponding information so as to finally realize docking operation with flying.)

1. An airport autopilot tractor dispatch navigation system, comprising:

the system comprises a cloud control center, a real-time navigation map generation system and a real-time navigation map matching system, wherein the cloud control system comprises a tractor matching system and a real-time navigation map generation system;

the tractor matching system retrieves an automatic driving tractor corresponding to the model 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 the 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-element roadside sensing unit comprises a laser radar and a camera, point cloud data are collected through the laser radar, and video data are collected through the camera;

the automatic driving platform comprises a plurality of sensors, a sensing unit, a decision planning unit and a control unit;

the multi-sensor respectively collects the position, the attitude, the video and the point cloud data of the aircraft to be towed; the sensing units are fused in a distributed manner to acquire data information transmitted by the multiple sensors; 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 behavior decision layer makes a behavior decision by combining the received information of the sensing unit after receiving the path, the motion planning layer plans a characteristic track according to the behavior decision, the track is a final driving path planned by automatic driving traction, and the control unit controls the tractor to advance according to the obtained track;

and the cloud control center, the intelligent roadside system and the automatic driving platform transmit information through a 5G aviation airport mobile communication system.

2. The dispatching and navigation system of the airport automatic driving tractor according to claim 1, wherein the path division layer generates a global path by adopting an a-x 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 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 by a heuristic function f (n) ═ g (n) + h (n), wherein f (n) is the comprehensive priority of a node n, when a next node to be traversed is selected, a 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.

3. The dispatching navigation system of the airport automatic driving tractors according to claim 2, wherein in the operation process of the a-x algorithm, the node with the highest priority is selected from the priority queue as the next node to be traversed, and finally a shortest path is determined.

4. The dispatching navigation system of the airport automatic driving tractor as claimed in claim 2, wherein the motion planning layer plans the expected motion track of the vehicle in the local space and time of the decision according to environment information, upper layer decision and real-time pose information of the vehicle body, and the motion track comprises track, speed, direction and state.

5. The dispatching navigation system of an airport autopilot tractor according to claim 1, wherein the cloud control center further comprises an information processing system, the information processing system obtains all flight information of the day from the airport operation and control center, sorts the flight information from the morning to the evening according to the time sequence according to the predicted departure time of each aircraft, and arranges the flight information in the system to form a departure aircraft waiting queue.

6. The dispatching navigation system of the airport autopilot tractor according to claim 3, characterized in that the cloud control center comprises a communication system, the intelligent roadside system comprises a roadside communication unit, the autopilot platform comprises a communication unit, the roadside communication unit transmits the point cloud data and the video data obtained by the multiple roadside sensing units to the communication system, and the cloud control center processes the received data to obtain a navigation map and transmits the navigation map to the communication unit through the communication system.

7. The dispatching navigation method for the airport automatic driving tractor is characterized by comprising the following steps:

s1, the cloud control center acquires flight information, arranges all the flight information according to the time sequence to form an aircraft waiting queue, sends a push-out application before the aircraft to be towed is expected to leave the port, and simultaneously sends the information to the cloud control center, wherein the information comprises the model, the position, the posture and the target position of the aircraft;

s2, the tractor matching system of the cloud control center retrieves the automatic driving tractor corresponding to the model of the aircraft to be towed according to the aircraft to be towed which sends the application, searches for the tractor closest to the aircraft to be towed in the similar idle automatic driving tractors, and sends an assignment command and information of the aircraft to be towed to the tractor matching system;

s3, the communication unit on the automatic driving tractor in idle state receives the assignment command, and sends a wake-up signal to the automatic driving platform on the automatic driving tractor, and the automatic driving platform starts each unit to enter 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 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 pilot platform obtains the starting point position of the traction operation according to the information of the aircraft to be towed, and plans the optimal route leading to the starting point position according to the real-time navigation map; and the automatic driving tractor drives to the starting point position according to the optimal route.

8. The dispatching navigation method for the airport automatic driving tractors according to claim 7, wherein the communication unit of the automatic driving platform is kept in a long-term on-line state, and other units of the automatic driving 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 other units in the standby state for a long time, so that the automatic driving tractors enter a standby state.

Technical Field

The invention relates to the technical field of aircraft traction operation, in particular to a dispatching navigation system and method for an airport automatic driving tractor.

Background

The rapidly increasing air traffic greatly increases the operating pressure at airport surfaces. In a large hub airport, the delay caused by vehicle dispatching accounts for 15.45 percent of all the delay. At present, tractors of most airports in China finish scheduling manually, workers schedule vehicles meeting conditions in a visual mode, a voice talkback mode and the like, and the scheduling efficiency is low in the peak time period of a flight due to the laggard information acquisition mode and the decision process, so that the punctuality rate of the flight is influenced.

Meanwhile, the traction operation is carried out by adopting a manually driven tractor, so that a plurality of potential safety hazards exist. The existing aircraft traction operation mode in China is that a tractor driver drives a tractor on the basis of familiar various aircraft traction technical requirements, at least two traction guides are required to be equipped due to limited visual field of the driver, the tractor driver is responsible for observing surrounding conditions, and early warning and reminding are given to the tractor driver through voice communication. The traditional aircraft traction operation mainly depends on manual driving and artificial observation and judgment, so that the potential safety hazard and the low efficiency exist, and the requirements on a tractor driver and a traction guide person are extremely high.

In order to improve the safety and efficiency of the operation of the tractor, a series of related technical researches are also developed in local colleges and universities and institutions at home and abroad, and the researches mainly concentrate on one direction, namely, the information of the traffic situation around the tractor is acquired by additionally arranging a sensor on the tractor, and the information influencing the operation safety is provided for a tractor driver through a visual interface. However, the method still cannot solve the problems that the requirements of a tractor driver are strict, the personnel danger of operators is high, cooperation of multiple persons is required, and the efficiency is low.

Disclosure of Invention

The patent is provided based on the above requirements in the prior art, and the technical problem to be solved by the patent is to provide a dispatching navigation method and system for an airport automatic driving tractor so as to ensure the safety of tractor operators and improve the operation efficiency.

In order to solve the above problem, the technical scheme provided by the patent comprises:

the utility model provides an airport autopilot tractor dispatch navigation, includes: the system comprises a cloud control center, a real-time navigation map generation system and a real-time navigation map matching system, wherein the cloud control system comprises a tractor matching system and a real-time navigation map generation system; the tractor matching system retrieves an automatic driving tractor corresponding to the model 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 the 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-element roadside sensing unit comprises a laser radar and a camera, point cloud data are collected through the laser radar, and video data are collected through the camera; the automatic driving platform comprises a plurality of sensors, a sensing unit, a decision planning unit and a control unit; the multi-sensor respectively collects the position, the attitude, the video and the point cloud data of the aircraft to be towed; the sensing units are fused in a distributed manner to acquire data information transmitted by the multiple sensors; 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 behavior decision layer makes a behavior decision by combining the received information of the sensing unit after receiving the path, the motion planning layer plans a characteristic track according to the behavior decision, the track is a final driving path planned by automatic driving traction, and the control unit controls the tractor to advance according to the obtained track; and the cloud control center, the intelligent roadside system and the automatic driving platform transmit information through a 5G aviation airport mobile communication system.

Preferably, the path division layer generates a global path by using an a-algorithm, a real-time navigation map at a certain time 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 by a heuristic function f (n) ═ g (n) + h (n), wherein f (n) is the comprehensive priority of a node n, when a next node to be traversed is selected, a 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.

Preferably, in the operation process of the a-x algorithm, the node with the highest priority is selected from the priority queue each time as the next node to be traversed, and finally a shortest path is determined.

Preferably, the motion planning layer plans the motion trail expected by the vehicle in the local space and time of the decision point according to the environment information, the upper layer decision and the real-time pose information of the vehicle body, and the motion trail comprises a trail, a speed, a direction and a state.

Preferably, the cloud control center further comprises an information processing system, the information processing system acquires all flight information of the day from the airport operation and control center, sorts the flight information from morning to evening according to the predicted departure time of each aircraft and the time sequence, and arranges the flight information in the system to form a departure aircraft waiting queue.

Preferably, the cloud control center comprises a communication system, the intelligent roadside system comprises a roadside communication unit, the automatic driving platform comprises a communication unit, the roadside communication unit transmits the point cloud data and the video data obtained by the multiple roadside sensing units to the communication system, and the cloud control center processes the received data to obtain a navigation map and transmits the navigation map to the communication unit through the communication system.

The dispatching navigation method of the airport automatic driving tractor comprises the following steps: s1, the cloud control center acquires flight information, arranges all the flight information according to the time sequence to form an aircraft waiting queue, sends a push-out application before the aircraft to be towed is expected to leave the port, and simultaneously sends the information to the cloud control center, wherein the information comprises the model, the position, the posture and the target position of the aircraft; s2, the tractor matching system of the cloud control center retrieves the automatic driving tractor corresponding to the model of the aircraft to be towed according to the aircraft to be towed which sends the application, searches for the tractor closest to the aircraft to be towed in the similar idle automatic driving tractors, and sends an assignment command and information of the aircraft to be towed to the tractor matching system; s3, the communication unit on the automatic driving tractor in idle state receives the assignment command, and sends a wake-up signal to the automatic driving platform on the automatic driving tractor, and the automatic driving platform starts each unit to enter 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 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 pilot platform obtains the starting point position of the traction operation according to the information of the aircraft to be towed, and plans the optimal route leading to the starting point position according to the real-time navigation map; and the automatic driving tractor drives to the starting point position according to the optimal route.

Preferably, the communication unit of the automatic driving platform is kept in a long-term online state, and other units of the automatic driving platform are in a standby state for a long time, and when the communication unit receives an 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 automatic driving tractor enters a preparation state.

Compared with the prior art, the full-automatic butt joint device can realize full-automatic butt joint of the automatic driving tractor and the airplane to be towed, ensure the safety of operators, reduce the amount of manual labor and improve the operation efficiency and the operation precision.

Drawings

In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the embodiments of the present specification, and other drawings can be obtained by those skilled in the art according to the drawings.

FIG. 1 is a flow chart illustrating the steps of an airport automatic tractor dispatching navigation method of the present invention;

fig. 2 is an architecture diagram of an airport automatic driving tractor dispatching navigation system of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in 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 obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

For the purpose of facilitating understanding of the embodiments of the present application, the following description will be made in terms of specific embodiments with reference to the accompanying drawings, which are not intended to limit the embodiments of the present application.

Example 1

The embodiment provides an airport automatic driving tractor dispatching navigation system, and reference is made to fig. 2.

The dispatching navigation System of the Airport automatic driving tractor 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 carry out information transmission through a 5G aeroMACS (Aeronautical Mobile Airport Communications System).

And the cloud control center comprises a communication system, an information processing system, a tractor matching system and a real-time navigation map generating system.

The communication system is communicated with the automatic driving platform and the intelligent road side system through 5G Aero MACS.

The information processing system acquires all flight information of the day from the airport operation and control center, sorts the flight information from morning to evening according to the time sequence according to the expected 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 models and is corresponding to aircraft of various models, and the tractor matching system is used for retrieving idle tractors corresponding to the aircraft models which are released by sending applications and matching the idle tractors in real time.

The real-time navigation map generation system firstly abstracts an airport scene map into a scene network map, and the scene network map comprises nodes and paths.

The communication system transmits data received by the multi-element road side sensing units in the intelligent road side system to the real-time current map generation system, and obstacles and prompt information are marked on the basis of a field network map by processing point cloud data and video data acquired by the multi-element road side sensing units to generate a real-time navigation map.

The intelligent road side system comprises a road side communication unit, a multi-element road side sensing unit and a sign line.

The roadside communication unit is communicated with the cloud control center and the automatic driving platform through 5G Aero MACS.

The multi-element roadside 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 field angle and a vertical field angle of 360 degrees and 40 degrees respectively, the laser radar is used for collecting point cloud data of the position where the laser radar is located, and the point cloud data is used for assisting in generating a real-time navigation map.

The camera uses a CMOS camera, has the advantages of flexible image capture, high sensitivity, wide dynamic range and high resolution, and is used for acquiring airport scene video images.

The marking lines are used for providing the environmental information and defining lanes, providing assistance for the automatic driving tractor and assisting the tractor to move to the target position on the airport surface. The marker line is identified by a camera on the autopilot platform, illustratively, the camera identifies a lane line to assist a tractor in lane keeping; dynamic traffic signs similar to traffic lights are arranged at the crossing positions.

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 inertia/satellite combined navigation host and upper computer software and is respectively used for receiving satellite signals, calculating and providing multi-parameter navigation information, assisting positioning, analyzing data and the like.

The camera uses a GigE camera, transmits image data at a high speed and is used for acquiring a video image in front of the tractor and identifying the sign line.

The laser radar uses 32-line laser radars with a horizontal field angle and a vertical field angle of 360 degrees and 40 degrees respectively, and is used for detecting obstacles around the tractor and sending environment information to the decision planning unit.

And the communication unit is communicated with the cloud control center and the intelligent road side system through 5G AeroMACS.

The sensing unit is used for receiving the information acquired by the multiple sensors and performing distributed fusion on the information, namely, the original data acquired by each independent sensor is locally processed, and then the result is sent to the information fusion center to perform intelligent optimization combination to acquire a final result.

The decision planning unit is divided into three levels: path planning, behavior decision making, and motion planning. Firstly, a path planning layer generates a global path, after the global path is received, a behavior decision layer combines information received from a sensing unit to make a specific behavior decision, 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 the input of a control unit to decide the final driving path of the vehicle.

The path planning layer plans a global path, which is also called navigation planning, and an a-algorithm is used for path planning. The algorithm A is the most effective direct search method for solving the shortest path in the static road network, and the real-time navigation map at a certain moment can be regarded as the static road network. The method includes the steps that firstly, an area to be searched is simplified into small squares, the finally found path is the combination of the small squares, and the initial node and the target node respectively represent the starting point and the ending point of the path. The algorithm calculates the priority of each node by the heuristic function of f (n) ═ g (n) + h (n). And f (n) is the comprehensive priority of the node n, and when the next node to be traversed is selected, the node with the highest comprehensive priority (the value is the smallest) is always selected. g (n) is the cost of node n from the origin. h (n) is the expected cost of node n from the end point. In the operation process of the A-algorithm, the node with the minimum value of f (n) (with the highest priority) is selected from the priority queue each time to be used as the next node to be traversed, and finally a shortest path is determined.

And (4) behavior decision, also called behavior planning, planning reasonable driving behaviors under the constraint of traffic rules according to global planning route information, current traffic scene and environment perception information and the current driving state of the driver. Here, a hierarchical finite state machine is used, in which a limited number of states are constructed, and external inputs only allow the state machine to switch between these states. The layered finite state machine comprises the following parts: 1. inputting a set: also called stimulus sets, containing all inputs that the state machine may receive; 2. and (3) outputting a set: i.e., the set of responses that the state machine is capable of making; 3. state and transition logic inside the state machine is described using a directed graph; 4. the state machine has a fixed initial state; 5. ending the state set; 6. transfer logic: i.e. the condition under which the state machine transitions from one state to another.

The motion planning is to plan and decide the motion trail expected by the vehicle in local space and time under the condition of meeting certain kinematic constraints according to local environment information, upper layer decision task and real-time pose information of the vehicle body, wherein the motion trail comprises a running trail, speed, direction, state and the like, and the information of the expected speed, the possible running trail and the like output by planning is fed into the control unit, so that a series of specific control signals for the vehicle can be generated finally, and the vehicle can run according to a planned target.

And the control unit adopts PID control and generates control commands for the bottom layer accelerator, the brake, the steering wheel and the gear lever of the tractor according to the planned running track and speed and the current position, posture and speed so as to enable the tractor to run at the target speed and acceleration along the target track.

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 respective systems to exchange data.

The system comprises a laser radar and a camera which are installed in an airport field, wherein the laser radar and the camera collect point cloud data and video data, the collected data are transmitted to a communication system of a cloud control center through a roadside communication unit, the cloud control center processes received data information, a real-time current map is generated on the basis of an airport field map through a real-time navigation map generating system, the updated map is sent to a communication unit on an automatic driving platform through the communication system, a decision planning unit combines the received updated map information with information obtained by a sensing unit, a track meeting specific constraint conditions is planned and generated, and the track is used as input of a control unit to determine a final driving path of a vehicle.

Example 2

The embodiment provides a dispatching navigation method for an airport automatic driving tractor, and the method refers to fig. 1.

S1, the cloud control center acquires flight information, arranges all the flight information according to the time sequence to form an aircraft waiting queue, sends a push-out application before the aircraft to be towed is expected to leave, and sends the information to the cloud control center, wherein the information comprises the model, the position, the posture and the target position of the aircraft.

The cloud control center acquires all flight information of the day from the airport operation and control center, sorts the flight information from morning to evening according to the expected departure time of each aircraft and arranges the flight information in the system to form a departure aircraft waiting queue.

The aircraft to be towed sends a release application T minutes before the departure time, and T can be taken according to the distance between the aircraft and the parking place of the towing vehicle, preferably the value [30,40] is taken in the embodiment. Meanwhile, the aircraft sends self information to a cloud control center, the model of the aircraft in the information is used for matching with a waiting queue of the aircraft, and the position, the posture and the target position are used for being provided for a tractor to navigate.

And S2, the tractor matching system of the cloud control center retrieves the automatic driving tractor corresponding to the model of the aircraft to be towed according to the aircraft to be towed which sends the application, searches the automatic tractor closest to the aircraft to be towed in the same kind of idle automatic driving tractors, and sends an assignment command and information of the aircraft to be towed.

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 characterized in that tractors of various models are arranged in the tractor matching system, the tractors correspond to aircraft of different models respectively, and automatic driving tractors corresponding to the tractors are matched in real time according to the models of the to-be-towed aircraft, which are applied, and the automatic driving tractors have two states, namely working state and idle state.

The tractor matching system finds the closest one among the idle tractors to the aircraft to be towed and sends the assignment command and the aircraft to be towed information to it through the communication system.

And S3, the communication unit on the automatic driving tractor in the idle state receives the assignment command and sends a wake-up signal to the automatic driving platform on the automatic driving tractor, and the automatic driving platform starts each unit and enters a preparation state.

The autopilot platform includes a communication unit that remains online for long periods of time, with other units of the autopilot platform being in a standby state for long periods of time.

After receiving an assignment command sent by the cloud control center, the communication unit sends a wake-up signal to the automatic driving platform, the automatic driving platform starts the sensing unit, the decision planning unit and the 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 collected by a multi-road-side sensing unit in the intelligent road-side system, and sends the real-time navigation map to the tractor, wherein the multi-road-side sensing unit comprises a laser radar and a camera, and the data comprises point cloud data collected by the laser radar and video data collected by the camera.

The laser radar and the camera are mounted on an airport surface.

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, by processing the point cloud data and the video data acquired by the multi-road side sensing unit, marking obstacles and prompt information on the basis of the field network map, thereby generating a real-time navigation map and sending the real-time navigation map to the tractor.

S5, the automatic pilot platform obtains the starting point position of the traction operation according to the information of the aircraft to be towed, and plans the optimal route from the position to the starting point position according to the real-time navigation map; and the automatic driving tractor drives 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 10-15 m. And starting the tractor from the position to perform subsequent docking operation and towing operation with the airplane.

And according to a real-time navigation map provided by the cloud control center, a decision planning unit of the automatic driving platform plans an optimal route to the starting point of the traction operation.

The automatic driving platform comprises a decision planning unit, the decision planning unit adopts an A-x algorithm to plan a path, a feasible path from a starting point to a terminal point is constructed by means of a known environment map and obstacle information in the map, and the shortest driving time is selected as an optimal path to the traction operation starting point.

It should be noted that there may be obstacles in the optimal route, static obstacles require the tractor to avoid the obstacles autonomously, and dynamic obstacles may cause a waiting time, and in any case, the optimal route may always reach the starting point of the traction operation.

And in the process that the tractor runs to the starting point of the traction operation, the multi-sensor of the automatic driving platform continuously acquires surrounding environment information and feeds the surrounding environment information back 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 collects video images and identification mark lines in front of the tractor, and the laser radar collects point cloud data around the tractor.

Meanwhile, when the tractor runs to the traction operation starting point, the roadside communication unit of the intelligent roadside system continuously sends the environment information of the place to the automatic driving platform, wherein the place has or not has obstacles, obstacle information, passable time and the like.

And a decision planning unit of the automatic driving platform analyzes and processes the information received from the sensing unit and the roadside communication unit, makes a decision, and controls the motion state of the tractor by the control unit.

By implementing the method provided by the embodiment of the invention, the dispatching navigation of the automatic driving tractor in the airport can be realized, the burden of workers is reduced, and the safety and efficiency of the traction operation are improved.

The above-mentioned embodiments, objects, technical solutions and advantages of the present application are described in further detail, it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present application, and are not intended to limit the scope of the present application, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present application should be included in the scope of the present application.