CN114137960A - Unmanned vehicle cooperation method of intelligent transportation system of closed area - Google Patents
Unmanned vehicle cooperation method of intelligent transportation system of closed area Download PDFInfo
- Publication number
- CN114137960A CN114137960A CN202111282365.4A CN202111282365A CN114137960A CN 114137960 A CN114137960 A CN 114137960A CN 202111282365 A CN202111282365 A CN 202111282365A CN 114137960 A CN114137960 A CN 114137960A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- cooperation
- unmanned
- task
- unmanned vehicle
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 56
- 230000008569 process Effects 0.000 claims description 23
- 238000004891 communication Methods 0.000 claims description 6
- 230000002457 bidirectional effect Effects 0.000 claims description 3
- 230000007246 mechanism Effects 0.000 claims description 3
- 230000009471 action Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 230000029305 taxis Effects 0.000 description 2
- 230000008447 perception Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000007789 sealing Methods 0.000 description 1
Images
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)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention relates to an unmanned vehicle cooperation method of an intelligent transportation system of a closed area, which comprises a plurality of unmanned vehicles, a plurality of stations and cooperation platforms. The method of the invention can also solve the problems of vehicle scheduling and task allocation in the industries of urban multipoint garbage collection and transportation, medical first-aid transportation and the like.
Description
Technical Field
The invention belongs to the technical field of unmanned technology and intelligent transportation in special areas, and particularly relates to the technical field of behavior cooperation of multiple unmanned vehicles.
Background
The areas of airports, industrial parks, university campuses, wharfs, mines and the like have the characteristics of large area, relative sealing, relatively fixed traffic lines, dynamic change of people flow and logistics requirements and the like. Traditional taxis and logistics vehicles are generally difficult to enter the areas, and the operation mode of the traditional taxis and logistics vehicles is difficult to achieve profit in the special areas. In order to improve the transportation conditions in the area and improve the safety and the transportation efficiency, a special unmanned vehicle is necessary, and an intelligent cooperation technology is adopted to realize an intelligent transportation system capable of dynamically responding to the transportation requirements in the special area.
Disclosure of Invention
The invention provides a cooperation method, a work flow and related objects of a plurality of special unmanned vehicles in an enclosed area, which are used for solving the problems of vehicle scheduling, task allocation, vehicle control and the like in the operation process of an intelligent transportation system.
The invention realizes the purpose through the following technical scheme: the unmanned vehicle cooperation method is characterized in that after transportation tasks of people or articles are issued on the cooperation platform, the unmanned vehicles compete according to the task cooperation method, the unmanned vehicles which acquire the tasks plan transportation routes according to the route cooperation method and dynamically update, in the vehicle operation process, the switching of operation modes such as constant-speed cruising, following and overtaking related to vehicle body control can be determined by the vehicle body control cooperation method, in the vehicle operation process, the states of the unmanned vehicles are updated in real time and are sent to the cooperation platform, and the states of the corresponding unmanned vehicles are also updated synchronously.
Furthermore, the unmanned vehicle is a special low-speed unmanned vehicle, the unmanned vehicle is controlled by a vehicle-mounted computer and can run autonomously, the unmanned vehicle is provided with an RGB-D depth camera, a laser radar, a GPS and other devices for sensing the surrounding environment and determining the position and the posture of the vehicle, the head and the side of the vehicle are provided with a millimeter wave radar and an ultrasonic sensor for sensing obstacles in front and around and providing alarm signals for the vehicle, and the unmanned vehicle is provided with a wireless communication device, so that the communication between the vehicles, between the vehicles and a platform and between the vehicles and a cooperation platform can be realized.
Furthermore, the plurality of stations and the lanes between the stations form a net-shaped line, and the transportation line in the closed area is set to be a bidirectional single lane, namely, two stations can drive in two directions, but only one vehicle can drive at the same time.
Further, the cooperation platform is a software platform for collecting and updating the unmanned vehicle and task information in the system in real time, and the collected information of the unmanned vehicle mainly includes: the position, speed and available carrying capacity of the vehicle, and the collected mission information includes: the method comprises the steps of carrying task amount, a starting station of a task, a target station of the task, and running a software platform on a cooperation platform to realize dynamic task allocation and vehicle scheduling.
Further, the cooperation is divided into three levels, namely task-level cooperation, line-level cooperation and vehicle body control-level cooperation, the task-level cooperation is mainly used for how each unmanned vehicle acquires a transportation task through a bidding and bidding mechanism after the transportation task is published, the line-level cooperation means how the unmanned vehicle determines a transportation line and how the line is dynamically adjusted or updated in the transportation process, and the vehicle body control-level cooperation is used for adjusting the operation mode in the vehicle operation process.
Further, the task cooperation is used as a transportation task issued on the cooperation platform, and the matching degree of the N unmanned vehicles in the cooperation platform to the task is calculated according to the self state and the formula (1);
Pi＝a1X1+a2X2+a3X3+a4X4(1)
wherein, Pi(1 ≦ i ≦ N) represents the degree of matching of the ith unmanned vehicle for a certain transportation task,
X1transport capacity/available carrying capacity of the unmanned vehicle i. If the transport capacity exceeds the available carrying capacity of the vehicle, X is set1Set to 0;
X21-distance of current location of drone vehicle i to mission start station/total length of mesh line;
for X3If the transport mission start station is in the current transport route of the unmanned vehicle i, i.e. the vehicle will pass this station, X3Set to 1, otherwise X3Set to 0;
a1,a2,a3respectively correspond to X1,X2And X3And 0 is not less than a1,a2,a3≤1。
Each unmanned vehicle calculates the matching degree according to the formula (1), the matching degree is used as a bid value aiming at the task and is issued on a cooperation platform, and the unmanned vehicle with the highest matching degree obtains the transportation task; if the bid values issued by two or more unmanned vehicles are equal, the unmanned vehicle with the smallest serial number has the highest priority and is authorized to obtain the task.
Further, the line cooperation is used as a starting station and a destination station of an obtained certain transport task, and the shortest path between the starting station and the destination station and a station sequence thereof are determined by adopting a Dijkstra method according to the starting station and the destination station of the task;
if the special unmanned vehicle only has one transportation task, the vehicle runs along the route determined by the corresponding unmanned vehicle, when the vehicle passes through a station in the route, the serial number of the station is removed from the station sequence, and when the station sequence is empty, the vehicle completes all the transportation tasks;
in the running process of the vehicle, the personnel or the articles corresponding to the new task are loaded, but the transportation route of the vehicle needs to be consistent with the current route, and under the condition, the vehicle route needs to be extended and updated according to the route corresponding to the new task.
Further, the vehicle body control cooperatively processes mutual influence and interference in the driving process of the unmanned vehicle. The method comprises the steps that multiple operation modes are set for special unmanned vehicles in an enclosed area to adapt to changes of traffic conditions, the unmanned vehicles in multiple unmanned vehicle models determine the operation modes of the vehicles according to information such as vehicle positions and speeds corresponding to the unmanned vehicles and other unmanned vehicles, wherein the operation modes comprise constant-speed cruising modes, preceding vehicle following modes, deceleration parking modes and overtaking modes, and the unmanned vehicles receive operation mode instructions sent by the corresponding unmanned vehicles in real time and switch to the corresponding models to operate.
Compared with the prior art, the unmanned vehicle cooperation method of the intelligent transportation system of the closed area has the advantages that: aiming at the problem of personnel and goods transportation in a special area, the unmanned intelligent transportation system is adopted as a solution, a plurality of cooperation platforms of special unmanned vehicles are designed based on a plurality of unmanned vehicle models, the platforms can dynamically respond to transportation requirements, and transportation capacity as large as possible is provided by vehicles as few as possible, so that transportation efficiency is improved.
Drawings
Fig. 1 is a schematic diagram of a station and a network line.
FIG. 2 is a block diagram of the components and structure of an unmanned vehicle.
Fig. 3 is a vehicle running mode switching flow.
Fig. 4 is an operation flow of the intelligent transportation system.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments.
Closed area intelligence transportation system includes a plurality of unmanned vehicles, a plurality of stations, cooperation platform.
The unmanned vehicle is a special low-speed unmanned vehicle. The single unmanned vehicle is controlled by a vehicle-mounted computer and can run autonomously. The unmanned vehicle is provided with an RGB-D depth camera, a laser radar, a GPS and other equipment which are used for sensing the surrounding environment and determining the position and the posture of the vehicle. The head and the side of the vehicle are provided with a millimeter wave radar and an ultrasonic sensor which are used for sensing obstacles in front and around and providing alarm signals for the vehicle. The wireless communication equipment is arranged on the unmanned vehicle, and communication between vehicles, between vehicles and a platform and between vehicles and a cooperation platform can be realized.
A plurality of vehicles are generally set in a closed areaThe station, people with traffic demands or goods with transport demands in the area need to wait for unmanned vehicles at the station. After the person or the article arrives at the station, the vehicle can be called by pressing a button on the station, or the transportation task can be reserved before the person or the article arrives on a special webpage. The information of the key call or the online reservation is sent to the cooperation platform in real time and is issued to all vehicles in the intelligent transportation system in the form of transportation tasks. A plurality of stations and lanes between the stations form a net-shaped route, as shown in fig. 1. In order to ensure safety, the transportation line in the closed area is set to be a bidirectional single lane, namely, two stations can drive in two directions, but only one vehicle can drive at the same time. As shown in fig. 1, if the starting station of the transportation task is the ith station and the destination station is the jth station, there are multiple optional routes from the ith station to the jth station, such as the route marked as the sequence "i-g-k-j" or "i-h-f-j". If a and b are adjacent and interconnected stations, the distance between the two stations is recorded as a constant value sab(ii) a Otherwise, it is recorded as infinity.
The collaboration platform is a software platform that collects and updates the unmanned vehicles and task information in the system in real time. The collected information of the unmanned vehicle mainly includes: the location, speed, and available carrying capacity of the vehicle. The task information collected includes: carrying task amount, a starting station of the task and a target station of the task. The software platform developed by the cooperation technology is operated on the cooperation platform, and dynamic task allocation and vehicle scheduling are realized.
The invention adopts a Multi-unmanned vehicle (Multi-AgentSysteme) model to design an intelligent transportation system cooperation platform in a closed area. The unmanned vehicle has the capabilities of perception, decision making and control, can perceive the peripheral environment, generates the action intention by combining the self state, and takes action according to the action intention, and the composition and the structure of the unmanned vehicle are shown in figure 2. If N special unmanned vehicles are arranged in the closed area, N unmanned vehicles are correspondingly arranged in the cooperation platform, and the set of the N unmanned vehicles is { Agent }1,Agent2,…,AgentN}. Each unmanned vehicle is mapped into an unmanned vehicle in the cooperation platform, and after the state of the vehicle is sent to the cooperation platform, each unmanned vehicle is driven according to the vehicleAnd updating the self state in real time. And determining respective tasks and lines through interaction among the unmanned vehicles, and sending the task and line information to each unmanned vehicle in real time. Meanwhile, the interaction between the unmanned vehicles can also process potential conflicts in the process of driving the vehicles at a close distance.
The cooperation of the invention is divided into three levels, namely task-level cooperation, line-level cooperation and vehicle body control-level cooperation. The task-level cooperation is mainly used for how each unmanned vehicle acquires a transportation task through a bidding and bidding mechanism after the transportation task is published. Line-level collaboration refers to how the unmanned vehicle determines the route of transportation, how the route is dynamically adjusted or updated during transportation. The cooperation of the vehicle body control level is used for adjusting the running mode of the vehicle in the running process, so that the driving safety is ensured, and the running efficiency is improved.
Task collaboration: for a transportation task issued on a cooperation platform, N unmanned vehicles in the cooperation platform calculate the matching degree of the task according to a formula (1) according to the self state.
Pi＝a1X1+a2X2+a3X3+a4X4(1)
Wherein, Pi(1 ≦ i ≦ N) represents the degree of matching of the ith unmanned vehicle for a certain transportation task,
X1transport capacity/available carrying capacity of the unmanned vehicle i. If the transport capacity exceeds the available carrying capacity of the vehicle, X is set1Is set to 0.
X21-distance of current position of unmanned vehicle i to station at which mission is initiated/total length of mesh line
For X3If the transport mission start station is in the current transport route of the unmanned vehicle i, i.e. the vehicle will pass this station, X3Set to 1, otherwise X3Is set to 0.
a1,a2,a3Respectively correspond to X1,X2And X3And 0 is not less than a1,a2,a3≤1。
And (3) calculating the matching degree of each unmanned vehicle according to the formula (1), taking the matching degree as a bid value aiming at the task, issuing the bid value on a cooperation platform, and obtaining the transportation task by the unmanned vehicle with the highest matching degree. If the bid values issued by two or more unmanned vehicles are equal, the unmanned vehicle with the smallest serial number has the highest priority and is authorized to obtain the task.
Line cooperation: for an unmanned vehicle which has acquired a certain transportation task, according to an initial station and a destination station of the task, a Dijkstra (Dijkstra) method can be adopted to determine a shortest path between the initial station and the destination station and a station sequence thereof, corresponding to the mesh model shown in fig. 1.
If the special unmanned vehicle has only one transportation task, the vehicle runs along the route determined by the corresponding unmanned vehicle. When the vehicle passes through a station in the line, the serial number of the station is removed from the station sequence. When the station sequence is emptied, the vehicle completes all transportation tasks.
During the operation of the vehicle, people or articles corresponding to the new task can be loaded, but the transportation route of the vehicle is required to be consistent with the current route. In this case, the route of the vehicle needs to be extended and updated according to the route corresponding to the new task. For example, the current driving route sequence of the vehicle is 'a-b-c-d', the vehicle has the residual carrying capacity, a new task with the consistent direction of the transportation route is obtained, the route corresponding to the new task is'd-e-f', and the route of the vehicle is updated to be 'a-b-c-d-e-f'.
And (3) vehicle body control cooperation: unmanned vehicles running on the same line can influence each other in the running process due to factors such as vehicle speed difference and stop time difference. The vehicle body control cooperatively processes mutual influence and interference in the driving process of the unmanned vehicle. Various operation modes are set for special unmanned vehicles in the closed area to adapt to the change of traffic conditions. The unmanned vehicles in the multi-unmanned vehicle model determine the running modes of the vehicles according to the information of the positions, the speeds and the like of the vehicles corresponding to the unmanned vehicles and other unmanned vehicles, wherein the running modes comprise constant-speed cruising modes, preceding vehicle following modes, deceleration parking modes and overtaking modes. And the unmanned vehicle receives the operation mode command sent by the corresponding unmanned vehicle in real time and switches to the corresponding model to operate. The flow of dynamically switching the operation mode of the unmanned vehicle is shown in fig. 3.
Based on the collaboration platform of the present invention, the operation flow of the intelligent transportation system in the enclosed area is shown in fig. 4. After the transportation task of the personnel or the goods is issued on the cooperation platform, the unmanned vehicles compete according to a task cooperation method, the unmanned vehicles which acquire the task plan the transportation route according to the route cooperation method and dynamically update, and the switching of the vehicle body control related operation modes such as constant-speed cruising, following, overtaking and the like can be determined by the vehicle body control cooperation method in the vehicle operation process. And in the running process of the unmanned vehicle, the state of the unmanned vehicle is updated in real time and is sent to the cooperation platform, and the state of the corresponding unmanned vehicle is also updated synchronously.
Aiming at the problem of personnel and goods transportation in a special area, the unmanned intelligent transportation system is adopted as a solution, a plurality of cooperation platforms of special unmanned vehicles are designed based on a plurality of unmanned vehicle models, the platforms can dynamically respond to transportation requirements, and transportation capacity as large as possible is provided by vehicles as few as possible, so that transportation efficiency is improved. The method of the invention can also solve the problems of vehicle scheduling and task allocation in the industries of urban multipoint garbage collection and transportation, medical first-aid transportation and the like.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (8)
1. An unmanned vehicle cooperation method of an intelligent transportation system of an enclosed area is characterized in that: the closed area intelligent transportation system comprises a plurality of unmanned vehicles, a plurality of stations and a cooperation platform, wherein the unmanned vehicle cooperation method is that after transportation tasks of people or articles are issued on the cooperation platform, the unmanned vehicles compete for bids according to the task cooperation method, the unmanned vehicles which acquire the tasks plan transportation routes and dynamically update according to the route cooperation method, in the vehicle operation process, the switching of operation modes such as constant-speed cruising, following and overtaking related to vehicle body control can be determined by the vehicle body control cooperation method, in the unmanned vehicle operation process, the states of the unmanned vehicles are updated in real time and are sent to the cooperation platform, and the corresponding states of the unmanned vehicles are also updated synchronously.
2. The unmanned vehicle cooperation method of an enclosed area intelligent transportation system of claim 1, characterized in that: the unmanned vehicle is a special low-speed unmanned vehicle, the unmanned vehicle is controlled by a vehicle-mounted computer and can run autonomously, the unmanned vehicle is provided with an RGB-D depth camera, a laser radar, a GPS and other devices for sensing the surrounding environment and determining the position and the posture of the vehicle, the head and the side of the vehicle are provided with a millimeter wave radar and an ultrasonic sensor for sensing obstacles in front and around and providing alarm signals for the vehicle, and the unmanned vehicle is provided with a wireless communication device, so that the communication between the vehicles, between the vehicles and a platform and between the vehicles and a cooperation platform can be realized.
3. The unmanned vehicle cooperation method of an enclosed area intelligent transportation system of claim 1, characterized in that: the stations and the lanes between the stations form a net-shaped line, and the transportation line in the closed area is set to be a bidirectional single lane, namely, two-way driving can be performed between two adjacent stations, but only one vehicle can be performed at the same time.
4. The unmanned vehicle cooperation method of an enclosed area intelligent transportation system of claim 1, characterized in that: the cooperation platform is a software platform for collecting and updating the unmanned vehicle and task information in the system in real time, and the collected information of the unmanned vehicle mainly comprises the following steps: the position, speed and available carrying capacity of the vehicle, and the collected mission information includes: the method comprises the steps of carrying task amount, a starting station of a task, a target station of the task, and running a software platform on a cooperation platform to realize dynamic task allocation and vehicle scheduling.
5. The unmanned vehicle cooperation method of an enclosed area intelligent transportation system of claim 1, characterized in that: the cooperation is divided into three levels, namely task-level cooperation, line-level cooperation and vehicle body control-level cooperation, wherein the task-level cooperation is mainly used for how each unmanned vehicle acquires a transportation task through a bidding and bidding mechanism after the transportation task is published, the line-level cooperation refers to how the unmanned vehicle determines a transportation line and how the line is dynamically adjusted or updated in the transportation process, and the vehicle body control-level cooperation is used for adjusting the operation mode in the vehicle operation process.
6. The unmanned vehicle cooperation method of an enclosed area intelligent transportation system of claim 5, wherein: the task cooperation is used as a transportation task issued on the cooperation platform, and the matching degree of N unmanned vehicles in the cooperation platform to the task is calculated according to the self state and the formula (1);
Pi＝a1X1+a2X2+a3X3+a4X4 (1)
wherein, Pi(1 ≦ i ≦ N) represents the degree of matching of the ith unmanned vehicle for a certain transportation task, X1Transport capacity/available carrying capacity of the unmanned vehicle i. If the transport capacity exceeds the available carrying capacity of the vehicle, X is set1Set to 0;
X21-distance of current location of drone vehicle i to mission start station/total length of mesh line;
for X3If the transport mission start station is in the current transport route of the unmanned vehicle i, i.e. the vehicle will pass this station, X3Set to 1, otherwise X3Set to 0;
a1,a2,a3respectively correspond to X1,X2And X3And 0 is not less than a1,a2,a3≤1。
Each unmanned vehicle calculates the matching degree according to the formula (1), the matching degree is used as a bid value aiming at the task and is issued on a cooperation platform, and the unmanned vehicle with the highest matching degree obtains the transportation task; if the bid values issued by two or more unmanned vehicles are equal, the unmanned vehicle with the smallest serial number has the highest priority and is authorized to obtain the task.
7. The unmanned vehicle cooperation method of an enclosed area intelligent transportation system of claim 5, wherein: the line cooperation is used as a starting station and a destination station of an acquired transportation task, and the shortest path between the starting station and the destination station and a station sequence thereof are determined by adopting a Dijkstra method according to the starting station and the destination station of the task;
if the special unmanned vehicle only has one transportation task, the vehicle runs along the route determined by the corresponding unmanned vehicle, when the vehicle passes through a station in the route, the serial number of the station is removed from the station sequence, and when the station sequence is empty, the vehicle completes all the transportation tasks;
in the running process of the vehicle, the personnel or the articles corresponding to the new task are loaded, but the transportation route of the vehicle needs to be consistent with the current route, and under the condition, the vehicle route needs to be extended and updated according to the route corresponding to the new task.
8. The unmanned vehicle cooperation method of an enclosed area intelligent transportation system of claim 5, wherein: and the vehicle body controls and collaboratively processes mutual influence and interference in the running process of the unmanned vehicle. The method comprises the steps that multiple operation modes are set for special unmanned vehicles in an enclosed area to adapt to changes of traffic conditions, the unmanned vehicles in multiple unmanned vehicle models determine the operation modes of the vehicles according to information such as vehicle positions and speeds corresponding to the unmanned vehicles and other unmanned vehicles, wherein the operation modes comprise constant-speed cruising modes, preceding vehicle following modes, deceleration parking modes and overtaking modes, and the unmanned vehicles receive operation mode instructions sent by the corresponding unmanned vehicles in real time and switch to the corresponding models to operate.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111282365.4A CN114137960A (en) | 2021-11-01 | 2021-11-01 | Unmanned vehicle cooperation method of intelligent transportation system of closed area |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111282365.4A CN114137960A (en) | 2021-11-01 | 2021-11-01 | Unmanned vehicle cooperation method of intelligent transportation system of closed area |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114137960A true CN114137960A (en) | 2022-03-04 |
Family
ID=80391930
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111282365.4A Pending CN114137960A (en) | 2021-11-01 | 2021-11-01 | Unmanned vehicle cooperation method of intelligent transportation system of closed area |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114137960A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117130375A (en) * | 2023-10-26 | 2023-11-28 | 北京首钢气体有限公司 | In-field intelligent transportation control method and equipment for various types of semitrailers |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108519773A (en) * | 2018-03-07 | 2018-09-11 | 西安交通大学 | The paths planning method of automatic driving vehicle under a kind of structured environment |
| CN108648440A (en) * | 2018-05-31 | 2018-10-12 | 广州亿程交通信息集团有限公司 | Vehicle scheduling platform based on car networking monitoring |
| CN108734429A (en) * | 2017-04-19 | 2018-11-02 | 通用汽车环球科技运作有限责任公司 | Multimodal transportation management |
| CN108958257A (en) * | 2018-07-25 | 2018-12-07 | 深圳市集大自动化有限公司 | The collaboration paths planning method of more AGV integrated navigations |
| CN109471444A (en) * | 2018-12-12 | 2019-03-15 | 南京理工大学 | Based on the parking AGV paths planning method for improving dijkstra's algorithm |
| CN109814550A (en) * | 2018-12-29 | 2019-05-28 | 北京中云智车科技有限公司 | It is a kind of for closing the unmanned transport vehicle of garden |
| CN109829367A (en) * | 2018-12-24 | 2019-05-31 | 杨昌兵 | A kind of unmanned obstacle identification management system and method |
| CN110428161A (en) * | 2019-07-25 | 2019-11-08 | 北京航空航天大学 | A kind of unmanned mine car cloud intelligent dispatching method based on end edge cloud framework |
| CN110530369A (en) * | 2019-08-22 | 2019-12-03 | 北京交通大学 | AGV method for scheduling task based on time window |
| CN111199359A (en) * | 2020-01-08 | 2020-05-26 | 中国电子科技集团公司第五十四研究所 | Multi-agent task allocation method under network resource constraint |
| CN111307169A (en) * | 2018-11-22 | 2020-06-19 | 北京图森智途科技有限公司 | Path planning method, path planning device and unmanned vehicle |
| US20200294394A1 (en) * | 2019-03-13 | 2020-09-17 | Mitsubishi Electric Research Laboratories, Inc. | Joint Control of Vehicles Traveling on Different Intersecting Roads |
| AU2020101761A4 (en) * | 2020-08-11 | 2020-09-17 | Nanjing University Of Science & Technology | Method for planning path of parking agv based on improved dijkstra algorithm |
| CN111798041A (en) * | 2020-06-18 | 2020-10-20 | 北京卫星制造厂有限公司 | AGV intelligent scheduling method based on time window |
| JP2021117038A (en) * | 2020-01-23 | 2021-08-10 | アイシン・エィ・ダブリュ株式会社 | Driving support device and computer program |
-
2021
- 2021-11-01 CN CN202111282365.4A patent/CN114137960A/en active Pending
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108734429A (en) * | 2017-04-19 | 2018-11-02 | 通用汽车环球科技运作有限责任公司 | Multimodal transportation management |
| CN108519773A (en) * | 2018-03-07 | 2018-09-11 | 西安交通大学 | The paths planning method of automatic driving vehicle under a kind of structured environment |
| CN108648440A (en) * | 2018-05-31 | 2018-10-12 | 广州亿程交通信息集团有限公司 | Vehicle scheduling platform based on car networking monitoring |
| CN108958257A (en) * | 2018-07-25 | 2018-12-07 | 深圳市集大自动化有限公司 | The collaboration paths planning method of more AGV integrated navigations |
| CN111307169A (en) * | 2018-11-22 | 2020-06-19 | 北京图森智途科技有限公司 | Path planning method, path planning device and unmanned vehicle |
| CN109471444A (en) * | 2018-12-12 | 2019-03-15 | 南京理工大学 | Based on the parking AGV paths planning method for improving dijkstra's algorithm |
| CN109829367A (en) * | 2018-12-24 | 2019-05-31 | 杨昌兵 | A kind of unmanned obstacle identification management system and method |
| CN109814550A (en) * | 2018-12-29 | 2019-05-28 | 北京中云智车科技有限公司 | It is a kind of for closing the unmanned transport vehicle of garden |
| US20200294394A1 (en) * | 2019-03-13 | 2020-09-17 | Mitsubishi Electric Research Laboratories, Inc. | Joint Control of Vehicles Traveling on Different Intersecting Roads |
| CN110428161A (en) * | 2019-07-25 | 2019-11-08 | 北京航空航天大学 | A kind of unmanned mine car cloud intelligent dispatching method based on end edge cloud framework |
| CN110530369A (en) * | 2019-08-22 | 2019-12-03 | 北京交通大学 | AGV method for scheduling task based on time window |
| CN111199359A (en) * | 2020-01-08 | 2020-05-26 | 中国电子科技集团公司第五十四研究所 | Multi-agent task allocation method under network resource constraint |
| JP2021117038A (en) * | 2020-01-23 | 2021-08-10 | アイシン・エィ・ダブリュ株式会社 | Driving support device and computer program |
| CN111798041A (en) * | 2020-06-18 | 2020-10-20 | 北京卫星制造厂有限公司 | AGV intelligent scheduling method based on time window |
| AU2020101761A4 (en) * | 2020-08-11 | 2020-09-17 | Nanjing University Of Science & Technology | Method for planning path of parking agv based on improved dijkstra algorithm |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117130375A (en) * | 2023-10-26 | 2023-11-28 | 北京首钢气体有限公司 | In-field intelligent transportation control method and equipment for various types of semitrailers |
| CN117130375B (en) * | 2023-10-26 | 2024-01-26 | 北京首钢气体有限公司 | In-field intelligent transportation control method and equipment for various types of semitrailers |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112106001B (en) | Intelligent allocation system and method for driving tasks of vehicle and road | |
| US11754408B2 (en) | Methods and systems for topological planning in autonomous driving | |
| JP7335350B2 (en) | Remote control for exception handling | |
| US11120688B2 (en) | Orientation-adjust actions for autonomous vehicle operational management | |
| US11027751B2 (en) | Reinforcement and model learning for vehicle operation | |
| CN101859494B (en) | Autonomous vehicle management | |
| JP6992182B2 (en) | Autonomous vehicle operation management plan | |
| EP3759563B1 (en) | Centralized shared autonomous vehicle operational management | |
| JP6726363B2 (en) | Autonomous vehicle monitoring using the generated interface | |
| JP2022163098A (en) | Operation management of autonomous traveling vehicle | |
| CN113811929A (en) | Combined control of vehicles travelling on different intersecting roads | |
| US11740624B2 (en) | Advanced control system with multiple control paradigms | |
| CN109521764A (en) | Vehicle remote auxiliary mode | |
| JP7241642B2 (en) | platooning system | |
| CN110895877A (en) | Intelligent distribution system and method for vehicle road driving tasks | |
| US11874120B2 (en) | Shared autonomous vehicle operational management | |
| WO2022016901A1 (en) | Method for planning driving route of vehicle, and intelligent vehicle | |
| US12071127B2 (en) | Proactive risk mitigation | |
| US20200283014A1 (en) | Continual Planning and Metareasoning for Controlling an Autonomous Vehicle | |
| CN112540606A (en) | Obstacle avoidance method and device, scheduling server and storage medium | |
| CN107221178A (en) | A kind of traffic command control system based on unmanned plane | |
| CN114137960A (en) | Unmanned vehicle cooperation method of intelligent transportation system of closed area | |
| CN116135654A (en) | Vehicle running speed generation method and related equipment | |
| CN114264306A (en) | Indoor and outdoor combined distribution robot and distribution method | |
| CN113971883A (en) | Vehicle-road cooperative automatic driving method and efficient transportation system |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20220304 |
|
| WD01 | Invention patent application deemed withdrawn after publication |