CN113672362A - Intelligent cooperative operation method and system for epidemic prevention machine group in complex and multi-environment - Google Patents
Intelligent cooperative operation method and system for epidemic prevention machine group in complex and multi-environment Download PDFInfo
- Publication number
- CN113672362A CN113672362A CN202110820172.3A CN202110820172A CN113672362A CN 113672362 A CN113672362 A CN 113672362A CN 202110820172 A CN202110820172 A CN 202110820172A CN 113672362 A CN113672362 A CN 113672362A
- Authority
- CN
- China
- Prior art keywords
- environment
- robot
- intelligent
- constructing
- complex
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 39
- 230000002265 prevention Effects 0.000 title claims abstract description 24
- 238000013473 artificial intelligence Methods 0.000 claims abstract description 6
- 230000003993 interaction Effects 0.000 claims description 15
- 230000002787 reinforcement Effects 0.000 claims description 13
- 230000005012 migration Effects 0.000 claims description 7
- 238000013508 migration Methods 0.000 claims description 7
- 238000011217 control strategy Methods 0.000 claims description 6
- 238000005457 optimization Methods 0.000 claims description 6
- 230000008520 organization Effects 0.000 claims description 5
- 238000005516 engineering process Methods 0.000 claims description 3
- 238000007689 inspection Methods 0.000 claims description 3
- 238000011160 research Methods 0.000 abstract description 2
- 230000006399 behavior Effects 0.000 description 4
- 238000002955 isolation Methods 0.000 description 3
- 230000003313 weakening effect Effects 0.000 description 3
- 238000012512 characterization method Methods 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/48—Program initiating; Program switching, e.g. by interrupt
- G06F9/4806—Task transfer initiation or dispatching
- G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The invention provides an intelligent cooperative operation method and system for an epidemic prevention machine group in a complex multi-environment, wherein the method comprises the following steps: step 1: dynamic configuration of multi-robot tasks and roles is carried out in a dynamic environment; step 2: constructing an intelligent cooperation strategy based on the LTL under the condition of weakened information according to the dynamic configuration result; and step 3: and constructing an intelligent cooperative strategy based on the LTL under the conflict resolution condition according to the dynamic configuration result. The LTL can express rich and complex task specifications and is close to the description of human natural language, so that the description of machine complex tasks is more direct and convenient, and the usability and usability of the system are enhanced. As a theoretical framework for multi-robot collaborative planning control, the method has good openness and expansibility, and is convenient to combine the existing control method with the latest scientific research achievement in the field of artificial intelligence.
Description
Technical Field
The invention relates to the technical field of machine cooperative control, in particular to an intelligent cooperative operation method and system under complex and multi-environment of epidemic prevention machine groups.
Background
The epidemic prevention machine moving in the working environment can be regarded as a system with a plurality of moving states, the expression of time sequence logic is strong, the epidemic prevention machine is suitable for controlling a large number of epidemic prevention robots, the LTL theory is combined with artificial intelligence, a control theory, model detection and other methods, and effective and feasible new ideas can be provided for robot intelligent cooperation problems under multiple constraint limits such as weakening information, conflict resolution and the like through real-time perception, detection and interaction with the environment of the robot.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide an intelligent cooperative operation method and system for an epidemic prevention machine group in a complex and multi-environment.
According to the intelligent cooperative operation method of the epidemic prevention machine group in the complex and multi-environment, the method comprises the following steps:
step 1: dynamic configuration of multi-robot tasks and roles is carried out in a dynamic environment;
step 2: constructing an intelligent cooperation strategy based on the LTL under the condition of weakened information according to the dynamic configuration result;
and step 3: and constructing an intelligent cooperative strategy based on the LTL under the conflict resolution condition according to the dynamic configuration result.
Preferably, the step 1 comprises:
step 1.1: learning an optimal strategy to maximize the return or achieve a specific objective problem through interaction of the robot and the environment by using reinforcement learning;
step 1.2: using game theory to realize dynamic balance among task selections;
step 1.3: the reinforcement learning and the game theory are combined, and intelligent task allocation under a dynamic unknown non-structural environment is achieved.
Preferably, said step 1 further comprises the step of dividing the robot role into leader and executor, building a hierarchical organization of leader-executor, the criterion of division being to actuate and control a large number of executor robots with a minimum number of leader robots.
Preferably, the step 2 includes constructing a robot information topology of the networked system, extracting topological features by using multi-dimensional interaction in the simple complex model characterization system based on the constructed information topology, and designing a multi-robot cooperative control strategy by fusing methods such as optimization control, nonlinear control, random control and the like.
Preferably, the step 3 comprises a task feasible network topology product automaton formed on the basis of the constructed migration system, the LTL task automaton and the Cartesian product; and searching a path meeting the system specification by applying methods such as a model inspection technology, artificial intelligence, decision and control and the like.
Preferably, the step 3 includes constructing a new method of intelligent coordination and motion planning of multiple robots based on a probability model, and constructing an automaton based on an uncertain automaton generated by a probability migration system and an LTL.
The invention also provides an intelligent cooperative operation system of the epidemic prevention machine group in a complex multi-environment, which comprises the following modules:
module M1: dynamic configuration of multi-robot tasks and roles is carried out in a dynamic environment;
module M2: constructing an intelligent cooperation strategy based on the LTL under the condition of weakened information according to the dynamic configuration result;
module M3: and constructing an intelligent cooperative strategy based on the LTL under the conflict resolution condition according to the dynamic configuration result.
Preferably, said module M1 comprises:
module M1.1: learning an optimal strategy to maximize the return or achieve a specific objective problem through interaction of the robot and the environment by using reinforcement learning;
module M1.2: using game theory to realize dynamic balance among task selections;
module M1.3: the reinforcement learning and the game theory are combined, and intelligent task allocation under a dynamic unknown non-structural environment is achieved.
Preferably, said module M1 further comprises the step of taking the robot role as a leader and an executor, building a hierarchical organization of leader-executor, the criterion of division being to actuate and control a large number of executor robots with a minimum number of leader robots.
Preferably, the module M2 includes a robot information topology for constructing a networked system, and based on the constructed information topology, the method of extracting topological features, fusing methods such as optimization control, nonlinear control, random control, and the like, and designing a multi-robot cooperative control strategy, by using multi-dimensional interaction in a simple complex model characterization system.
Compared with the prior art, the invention has the following beneficial effects:
1. the LTL can express rich and complex task specifications and is close to the description of a natural language of a human, so that the description of a complex task of a machine is more direct and convenient, and the usability of the system are enhanced;
2. as an automatic system verification method, a model-checking method has correct-by-design characteristics, and ensures that collaborative planning can meet various complex specifications such as environment, system and task (namely, the correctness of the collaborative planning is ensured);
3. as a theoretical framework for multi-robot collaborative planning control, the method has good openness and expansibility, and is convenient to combine the existing control method with the latest scientific research achievement in the field of artificial intelligence.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a general technical scheme diagram of multi-robot cooperative control;
FIG. 2 is a block diagram of an LTL-based intelligent planning control theory;
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
Referring to fig. 1 and 2, aiming at an epidemic prevention robot for an isolation area to undertake material distribution, a problem that an optimal strategy is learned to maximize return or achieve a specific target through interaction of the robot and a community environment by using reinforcement learning is adopted; using a game theory to realize dynamic balance among distribution task selections; the reinforcement learning and the game theory are combined, and intelligent task allocation under a dynamic unknown non-structural environment is achieved. The robot role is divided into leader and executor, and a leader-executor hierarchy is constructed, and the division is based on the principle that a large number of executor robots are driven and controlled by a minimum number of leader robots.
Considering that the behavior and the motion of the robot can be influenced by external factors, regarding the behavior and the motion of the robot as an uncertain event, extracting the influence factors in the environment, constructing a migration system based on a Markov decision process model, and depicting the behavior characteristics of the robot under the influence of the dynamic factors, the uncertain factors of robot hardware, information interaction and other factors in the environment through a probability model.
The LTL is made up of a set of atom topic sets, logical operators, and timing operators. The LTL is used as a formal language, is similar to the normal language habit of people, codes tasks needing to be completed by the robot into a linear time sequence formula, and can describe not only point-to-point simple tasks but also more complex tasks. The subject adopts the LTL to describe any specified and required behaviors of the robot in a complex and various way, and converts the actions into corresponding automata in a chart form according to the type of the LTL.
The distribution is carried out according to the geographic environment characteristics of the isolation area, and the isolation area is regarded as a weighted switching system with a finite state, and tasks which need to be executed by the intelligent epidemic prevention robot are described by a linear sequential logic language. And constructing a Product automaton fusing a system and a task. And obtaining the optimal path by using a Dijkstra algorithm and feeding the optimal path back to an actual system, thereby obtaining the path of the epidemic prevention robot in the actual environment.
In the multi-robot system, each independent robot is a node in the networked system, and the cooperative cooperation is realized through the real-time transmission and sharing of information. The topological structure characteristics of the communication network determine the information exchange and sharing mode among individuals and also determine whether the intelligent system can cooperatively complete complex tasks. Aiming at the situations of limited information exchange, time lag, packet loss and the like among robots by constructing a random network, a switching network, a time-varying network and the like under a dynamic non-structural environment and a complex task scene.
Based on a figure-of-graph theory and a control theory, the topological characteristics of the network are extracted by using continuous coherence, an internal rule is mined between the information topology of the multi-robot system and the information interaction and cooperative operation of the multi-stage machines, and theoretical support is provided for the cooperative operation under the condition of weakening information limitation.
Based on the topological characteristics extracted by a simple complex analysis method, optimization control, nonlinear control, random control and other methods are fused, and a multi-robot cooperative control strategy is designed, so that the robot can utilize local and limited information interaction to carry out effective intelligent cooperation, and intelligent cooperation and operation under the condition of weakening information limitation are realized.
Based on the constructed migration system, the LTL task automaton and a task feasible network topology product automaton formed by Cartesian products; searching an optimal path meeting a system specification by applying methods such as a model inspection technology, artificial intelligence, decision making, control and the like;
a novel method for establishing intelligent cooperation and motion planning of multiple robots based on a probability model is constructed, a loose product automaton is constructed on the basis of an uncertain Buchi automaton generated by a probability migration system and LTL, the robots are enabled not to strictly follow expected LTL constraint, theories such as reinforcement learning and game theory are combined, and the optimal motion planning for resolving conflicts is found in a limited time domain according to real-time sensed environment information.
The invention also provides an intelligent cooperative operation system of the epidemic prevention machine group in a complex multi-environment, which comprises: module M1: dynamic configuration of multi-robot tasks and roles is carried out in a dynamic environment; the module M1 includes: module M1.1: learning an optimal strategy to maximize the return or achieve a specific objective problem through interaction of the robot and the environment by using reinforcement learning; module M1.2: using game theory to realize dynamic balance among task selections; module M1.3: the reinforcement learning and the game theory are combined to realize intelligent task allocation under a dynamic unknown non-structural environment; module M1 also includes building a hierarchical organization of leader-executors by dividing the robot role into leader and executors, with the criterion of division being to actuate and control a large number of executors robots with a minimum number of leader robots.
Module M2: constructing an intelligent cooperation strategy based on the LTL under the condition of weakened information according to the dynamic configuration result; the module M2 comprises a robot information topology for constructing a networked system, and based on the constructed information topology, the method comprises the steps of utilizing a simple complex model to depict multi-dimensional interaction in the system, extracting topological characteristics, fusing methods such as optimization control, nonlinear control and random control, and designing a multi-robot cooperative control strategy.
Module M3: and constructing an intelligent cooperative strategy based on the LTL under the conflict resolution condition according to the dynamic configuration result.
Those skilled in the art will appreciate that, in addition to implementing the system and its various devices, modules, units provided by the present invention as pure computer readable program code, the system and its various devices, modules, units provided by the present invention can be fully implemented by logically programming method steps in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system and various devices, modules and units thereof provided by the invention can be regarded as a hardware component, and the devices, modules and units included in the system for realizing various functions can also be regarded as structures in the hardware component; means, modules, units for performing the various functions may also be regarded as structures within both software modules and hardware components for performing the method.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (10)
1. An intelligent cooperative operation method for an epidemic prevention machine group in a complex and multi-environment is characterized by comprising the following steps:
step 1: dynamic configuration of multi-robot tasks and roles is carried out in a dynamic environment;
step 2: constructing an intelligent cooperation strategy based on the LTL under the condition of weakened information according to the dynamic configuration result;
and step 3: and constructing an intelligent cooperative strategy based on the LTL under the conflict resolution condition according to the dynamic configuration result.
2. The intelligent cooperative operation method for the epidemic prevention machine group in the complex multi-environment according to claim 1, wherein the step 1 comprises:
step 1.1: learning an optimal strategy to maximize the return or achieve a specific objective problem through interaction of the robot and the environment by using reinforcement learning;
step 1.2: using game theory to realize dynamic balance among task selections;
step 1.3: the reinforcement learning and the game theory are combined, and intelligent task allocation under a dynamic unknown non-structural environment is achieved.
3. The intelligent cooperative work method for epidemic prevention robot group in complex multi-environment according to claim 1, wherein the step 1 further comprises dividing the robot role into leader and executor, and constructing a hierarchical organization of leader-executor, wherein the dividing is based on the minimum number of leader robots to drive and control a large number of executor robots.
4. The intelligent cooperative operation method under the complex multi-environment of the epidemic prevention machine group as claimed in claim 1, wherein the step 2 comprises constructing a robot information topology of a networked system, based on the constructed information topology, utilizing a simple complex model to depict multi-dimensional interaction in the system, extracting topological features, fusing methods such as optimization control, nonlinear control, random control and the like, and designing a multi-robot cooperative control strategy.
5. The intelligent cooperative operation method under the complex multi-environment of the epidemic prevention machine group as claimed in claim 1, wherein the step 3 comprises a task feasible network topology product automaton which is formed based on the constructed migration system, the LTL task automaton and the Cartesian product; and searching a path meeting the system specification by applying methods such as a model inspection technology, artificial intelligence, decision and control and the like.
6. The intelligent cooperative operation method for the epidemic prevention robot group in the complex multi-environment as claimed in claim 1, wherein the step 3 comprises constructing a new method for intelligent cooperation and motion planning of multiple robots based on a probability model, and constructing an automaton based on an uncertain automaton generated by a probability migration system and LTL.
7. The utility model provides an intelligent collaborative operation system under epidemic prevention machine crowd complicated multi-environment which characterized in that, the system includes following module:
module M1: dynamic configuration of multi-robot tasks and roles is carried out in a dynamic environment;
module M2: constructing an intelligent cooperation strategy based on the LTL under the condition of weakened information according to the dynamic configuration result;
module M3: and constructing an intelligent cooperative strategy based on the LTL under the conflict resolution condition according to the dynamic configuration result.
8. The intelligent cooperative operating system in an epidemic prevention machine group complex multi-environment as claimed in claim 7, wherein the module M1 comprises:
module M1.1: learning an optimal strategy to maximize the return or achieve a specific objective problem through interaction of the robot and the environment by using reinforcement learning;
module M1.2: using game theory to realize dynamic balance among task selections;
module M1.3: the reinforcement learning and the game theory are combined, and intelligent task allocation under a dynamic unknown non-structural environment is achieved.
9. The intelligent cooperative operation system in the complex multi-environment of epidemic prevention machine group as claimed in claim 7, wherein the module M1 further comprises dividing the robot role into leader and executor, and constructing a hierarchical organization of leader and executor, wherein the dividing criterion is to drive and control a large number of executor robots by a minimum number of leader robots.
10. The intelligent cooperative work method under the complex multi-environment of the epidemic prevention robot group as claimed in claim 7, wherein the module M2 comprises a robot information topology for constructing a networked system, extracting topology characteristics based on the constructed information topology by using multi-dimensional interaction in a simple complex model depicting system, and designing a multi-robot cooperative control strategy by fusing methods such as optimization control, nonlinear control, random control and the like.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110820172.3A CN113672362B (en) | 2021-07-20 | 2021-07-20 | Intelligent collaborative operation method and system under complex multi-environment of epidemic prevention machine group |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110820172.3A CN113672362B (en) | 2021-07-20 | 2021-07-20 | Intelligent collaborative operation method and system under complex multi-environment of epidemic prevention machine group |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113672362A true CN113672362A (en) | 2021-11-19 |
| CN113672362B CN113672362B (en) | 2023-11-07 |
Family
ID=78539645
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110820172.3A Active CN113672362B (en) | 2021-07-20 | 2021-07-20 | Intelligent collaborative operation method and system under complex multi-environment of epidemic prevention machine group |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113672362B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114722946A (en) * | 2022-04-12 | 2022-07-08 | 中国人民解放军国防科技大学 | Unmanned aerial vehicle asynchronous action and cooperation strategy synthesis method based on probability model detection |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020143598A1 (en) * | 2001-01-22 | 2002-10-03 | Scheer Robert H. | System for providing integrated supply chain management |
| US20060167917A1 (en) * | 2005-01-21 | 2006-07-27 | Solomon Research Llc | System, methods and apparatus for complex behaviors of collectives of intelligent mobile software agents |
| KR20160081048A (en) * | 2014-12-30 | 2016-07-08 | 국방과학연구소 | Device and method for remote robot cooperation control |
| CN109213200A (en) * | 2018-11-07 | 2019-01-15 | 长光卫星技术有限公司 | Multiple no-manned plane cooperates with formation flight management system and method |
| CN109405828A (en) * | 2018-07-30 | 2019-03-01 | 浙江工业大学 | Mobile robot global optimum path planning method based on LTL-A* algorithm |
| CN109409592A (en) * | 2018-10-15 | 2019-03-01 | 浙江工业大学 | The optimal policy solution of mobile robot under dynamic environment |
| CN109657868A (en) * | 2018-12-26 | 2019-04-19 | 北京理工大学 | A kind of probabilistic programming recognition methods of task sequential logic constraint |
| CN112861442A (en) * | 2021-03-10 | 2021-05-28 | 中国人民解放军国防科技大学 | Multi-machine collaborative air combat planning method and system based on deep reinforcement learning |
| CN113031593A (en) * | 2021-02-25 | 2021-06-25 | 上海交通大学 | Active sensing task path planning method and system, robot and controller |
| CN113128828A (en) * | 2021-03-05 | 2021-07-16 | 中国科学院国家空间科学中心 | Satellite observation distributed online planning method based on multi-agent reinforcement learning |
-
2021
- 2021-07-20 CN CN202110820172.3A patent/CN113672362B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020143598A1 (en) * | 2001-01-22 | 2002-10-03 | Scheer Robert H. | System for providing integrated supply chain management |
| US20060167917A1 (en) * | 2005-01-21 | 2006-07-27 | Solomon Research Llc | System, methods and apparatus for complex behaviors of collectives of intelligent mobile software agents |
| KR20160081048A (en) * | 2014-12-30 | 2016-07-08 | 국방과학연구소 | Device and method for remote robot cooperation control |
| CN109405828A (en) * | 2018-07-30 | 2019-03-01 | 浙江工业大学 | Mobile robot global optimum path planning method based on LTL-A* algorithm |
| CN109409592A (en) * | 2018-10-15 | 2019-03-01 | 浙江工业大学 | The optimal policy solution of mobile robot under dynamic environment |
| CN109213200A (en) * | 2018-11-07 | 2019-01-15 | 长光卫星技术有限公司 | Multiple no-manned plane cooperates with formation flight management system and method |
| CN109657868A (en) * | 2018-12-26 | 2019-04-19 | 北京理工大学 | A kind of probabilistic programming recognition methods of task sequential logic constraint |
| CN113031593A (en) * | 2021-02-25 | 2021-06-25 | 上海交通大学 | Active sensing task path planning method and system, robot and controller |
| CN113128828A (en) * | 2021-03-05 | 2021-07-16 | 中国科学院国家空间科学中心 | Satellite observation distributed online planning method based on multi-agent reinforcement learning |
| CN112861442A (en) * | 2021-03-10 | 2021-05-28 | 中国人民解放军国防科技大学 | Multi-machine collaborative air combat planning method and system based on deep reinforcement learning |
Non-Patent Citations (4)
| Title |
|---|
| ADIL BAYKASOĞLU 等: "An application oriented multi-agent based approach to dynamic load/truck planning", 《EXPERT SYSTEMS WITH APPLICATIONS》, vol. 42, no. 16, pages 6008 - 6025, XP029155396, DOI: 10.1016/j.eswa.2015.04.011 * |
| XU CHU DING 等: "Automatic Deployment of Robotic Teams", 《IEEE ROBOTICS & AUTOMATION MAGAZINE》, vol. 18, no. 3, pages 75 - 86, XP011359297, DOI: 10.1109/MRA.2011.942117 * |
| 刘庆周 等: "多智能体路径规划研究进展", 《计算机工程》, vol. 46, no. 4, pages 1 - 10 * |
| 曹文静 等: "多无人机协同方法研究", 《飞航导弹》, no. 01, pages 44 - 48 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114722946A (en) * | 2022-04-12 | 2022-07-08 | 中国人民解放军国防科技大学 | Unmanned aerial vehicle asynchronous action and cooperation strategy synthesis method based on probability model detection |
| CN114722946B (en) * | 2022-04-12 | 2022-12-20 | 中国人民解放军国防科技大学 | Unmanned aerial vehicle asynchronous action and cooperation strategy synthesis method based on probability model detection |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113672362B (en) | 2023-11-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Bonci et al. | A cyber-physical system approach for building efficiency monitoring | |
| Lins et al. | Industry 4.0 Retrofitting | |
| CN105467997B (en) | Based on the storage robot path planning method that linear time temporal logic is theoretical | |
| Nagy et al. | Ten questions concerning reinforcement learning for building energy management | |
| US20240029177A1 (en) | Factory simulator-based scheduling system using reinforcement learning | |
| Tian et al. | Real-time shop floor scheduling method based on virtual queue adaptive control: Algorithm and experimental results | |
| CN113672362A (en) | Intelligent cooperative operation method and system for epidemic prevention machine group in complex and multi-environment | |
| Carbonari et al. | Development of a BIM-based holonic system for real-time monitoring of building operational efficiency | |
| Correa | Integrating Industry 4.0 associated technologies into automated and traditional construction | |
| Losev et al. | Infographic oriented management model of cyber-physical systems during a building life cycle | |
| Stanescu et al. | Supervisory control and data acquisition for virtual enterprise | |
| Ablett et al. | Learning from guided play: Improving exploration for adversarial imitation learning with simple auxiliary tasks | |
| Yin et al. | Formal Synthesis of Controllers for Safety-Critical Autonomous Systems: Developments and Challenges | |
| Ben-Naoum et al. | Methodologies for discrete event dynamic systems: A survey | |
| Čapkovič | Modelling, analysing and control of interactions among agents in MAS | |
| Carnì et al. | Agent-based software architecture for distributed measurement systems and cyber-physical systems design | |
| Gamzayev et al. | Handling of expert knowledge in software product lines development with usage of repertory grids method | |
| Shoaei et al. | Abstractions for nonblocking supervisory control of extended finite automata | |
| Macktoobian | Automatic reconfiguration of untimed and timed discrete-event systems | |
| Zaytoon | On the recent advances in grafcet | |
| Luo et al. | Robotic Planning under Hierarchical Temporal Logic Specifications | |
| Hayane et al. | Optimal routing and scheduling for unreliable Markovian systems modeled with Timed Petri nets | |
| Yitmen et al. | Enabling Technologies for Cognitive Digital Twins Towards Construction 4.0 | |
| Belta et al. | Formal Synthesis of Cyber-Physical Systems (Dagstuhl Seminar 17201) | |
| Hengst et al. | Online Learning and Planning in Cognitive Hierarchies |
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 |