CN114727217A - Low-cost double-collar heterogeneous unmanned aerial vehicle formation cooperative positioning method based on data chain communication - Google Patents
Low-cost double-collar heterogeneous unmanned aerial vehicle formation cooperative positioning method based on data chain communication Download PDFInfo
- Publication number
- CN114727217A CN114727217A CN202210200264.6A CN202210200264A CN114727217A CN 114727217 A CN114727217 A CN 114727217A CN 202210200264 A CN202210200264 A CN 202210200264A CN 114727217 A CN114727217 A CN 114727217A
- Authority
- CN
- China
- Prior art keywords
- unmanned aerial
- aerial vehicle
- low
- cost
- heterogeneous
- 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
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3407—Route searching; Route guidance specially adapted for specific applications
- G01C21/3415—Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
-
- 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
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- Remote Sensing (AREA)
- Radar, Positioning & Navigation (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Biophysics (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Computational Linguistics (AREA)
- Biomedical Technology (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Automation & Control Theory (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention provides a double-collar heterogeneous formation cooperative positioning method based on data chain communication, aiming at the existing heterogeneous formation cooperative positioning technology which is not fully researched and based on double-collar heterogeneous formation. The method comprises the steps of firstly constructing a data chain communication relation under a dual-collar structure, constructing a nonlinear equation set according to a relative distance between low-cost unmanned aerial vehicles and a relative distance between leading unmanned aerial vehicles measured by a data chain on the basis, and performing small-range search by using a particle swarm algorithm according to inertial measurement information to obtain cooperative positioning resolving position information meeting the positioning precision requirement. The method has good applicability, and the cooperative positioning result can be applied by matching with each cooperative task. The method can be applied to unmanned aerial vehicles in an expanded mode, and the accuracy and the calculation speed of the cooperative positioning can be improved by improving the optimization method.
Description
Technical Field
The invention relates to a low-cost double-collar heterogeneous unmanned aerial vehicle formation cooperative positioning method based on data chain communication.
Background
Unmanned aerial vehicle has the efficiency of damaging, as last mace, successfully prevent suddenly can be very big about the war bureau. However, the continuous improvement and development of the systematic combat not only bring about the upgrade of defense means, but also realize the increase of system redundancy, so that the unmanned aerial vehicle individual combat often faces the embarrassment of four hands of two-fist difficult enemies. Along with the continuous development of network technology, the unmanned aerial vehicle formation based on network communication can realize winning with quality, volume through the cooperation then, accomplishes the battle task of different grade type to fill the firepower that task target dispersion, defensive power promote and bring and be not enough scheduling problem, realize the promotion of the probability of suddenly defending.
Although the formation cooperation can fill up the defect of individual combat with the number advantage, the consumption of the unmanned aerial vehicle by the formation combat is also huge, and the method is of great importance in reducing the cost of the single unmanned aerial vehicle and realizing the flexible formation cooperation by combining the network communication technology in combination with the current production capacity. The positioning of the unmanned aerial vehicle is a prerequisite for completing tasks, however, in the face of the existing complex battlefield environment, considering the influence caused by the upgrading of a target defense system and the environment of rejection/degradation of a GPS, the requirement for the technical optimization of a guidance system and a navigation system will inevitably increase the cost burden. Although the low-cost unmanned aerial vehicle can utilize the inertial navigation system to carry out autonomous navigation positioning, the drift error of the inertial navigation system increases along with the increase of flight time, and the positioning deviation gradually increases, so that the target miss is easily caused. Therefore, with the core goal of reducing the formation cost and exerting the quantity advantage, how to change the formation structure and improve the formation coordination quality by combining the network communication technology is a technical problem to be solved urgently for the cost reduction development of the unmanned aerial vehicle formation.
At present, theoretical research results of cooperative positioning completed by heterogeneous unmanned aerial vehicle formation are rarely reported, and how to combine part of high-performance leaders and realize cooperative positioning of low-cost unmanned aerial vehicles in heterogeneous formation through data chain communication is urgently needed to make theoretical breakthrough. Therefore, the invention provides a low-cost dual-leader heterogeneous unmanned aerial vehicle formation cooperative positioning method based on data chain communication, aiming at dual-leader heterogeneous unmanned aerial vehicle formation.
Disclosure of Invention
Aiming at the existing heterogeneous formation cooperative positioning technology which is not fully researched, the invention provides a data chain communication-based heterogeneous formation cooperative positioning method for double-collar heterogeneous formations based on the formation of the double-collar heterogeneous formations. The method comprises the steps of firstly constructing a data chain communication relation under a dual-collar structure, constructing a nonlinear equation set according to a relative distance between low-cost unmanned aerial vehicles and a relative distance between leading unmanned aerial vehicles measured by a data chain on the basis, and performing small-range search by using a particle swarm algorithm according to inertial measurement information to obtain cooperative positioning resolving position information meeting the positioning precision requirement. The method has good applicability, and the cooperative positioning result can be applied by matching with each cooperative task.
The present invention is explained in detail by the following design steps.
Step one, constructing heterogeneous formation communication network of double leaders
High performance leader M1And M2Real-time update of the exact position coordinates as (x)1，y1，z1) And (x)2，y2，z2).。
Low-precision unmanned aerial vehicle with inertial navigation system is sequentially recorded as Mm3, 4, 7, the real coordinate position to be resolved is unknown, noted as (x)m，ym，zm)。
The maximum drift error of the inertial navigation system is recorded as egThe inertial measurement position information is known and recorded as
The centralized data chain communication structure is shown in fig. 1, communication between the leader and all the low-cost unmanned aerial vehicles is guaranteed, undirected full communication is guaranteed between 5 low-cost unmanned aerial vehicles, and the unmanned aerial vehicle-to-vehicle distance obtained by data chain measurement is recorded as RmnThen where m ≠ n, Rmn＝Rnm
All the unmanned aerial vehicle distance information can be concentrated to any selected leader for processing, and the resolving result is fed back to each low-cost unmanned aerial vehicle through data chain communication. The communication relationship between the leaders can be designed and adjusted according to the requirements of the guidance scheme.
Step two, constructing a double-leader heterogeneous unmanned aerial vehicle formation cooperative positioning equation set
Establishing a nonlinear equation system containing 20 equations for relative distance between two heterogeneous unmanned aerial vehicle formation machines of the two lead teams as follows
The unmanned aerial vehicle position information comprises information about positions of unmanned aerial vehicles, wherein the positions of the unmanned aerial vehicles are to be searched and solved, and X is 3, y3, z3, X4, y4, z4, X7, y7 and z 7.
The ith row vector in the system F (X) containing 20 equations is denoted as FiThen order function
Is composed of
Wherein | FiL is FiAbsolute value of (a).
Therefore, the solving problem of the nonlinear equation system (3) is converted into solving X so as to obtain a cost function
With minimal problems.
Step three, co-locating heterogeneous formation of double-collar team
Firstly, initializing a particle swarm algorithm, and initializing a particle swarm and algorithm parameters.
Initializing a particle swarm:in measuring position with current inertial navigation system
Is the upper bound e of the error of the spherical center and the inertial navigation systemgN particles are randomly generated in a spherical space of a radius.
Initializing algorithm parameters:
the iteration times are recorded as k, and the iteration upper limit Num is recorded;
particle velocity V, particle maximum velocity V+；
Weight weUpper bound of weight
Lower bound of weightw；
Learning factor s1And s2；
Stopping condition population precision peAnd individual precision condition peg。
Secondly, the optimal position is calculated and updated.
The optimal position that the particle has undergone is pb, the current adaptive value is compared with the self-history best adaptive value, and pb is updated.
And the optimal position which the population has undergone is gb, the current adaptive value is compared with the self-history best adaptive value, and gb is updated.
Again, the particle position and velocity are updated.
Generating a random number q1And q is2Updating particle position and velocity according to the following formula
Judging whether a stop error precision condition p is satisfiedeThat is to say have
And if the position of the unmanned aerial vehicle is not satisfied, continuously and iteratively solving the position of the low-cost unmanned aerial vehicle.
By utilizing the iterative solution of the steps, the position information of the current low-cost unmanned aerial vehicle can be obtained when the stopping condition set by the particle swarm algorithm is met.
The invention provides a cooperative positioning method based on data chain communication based on low-cost double-leader heterogeneous formation, which organically links heterogeneous formations consisting of a high-performance leader and a low-cost unmanned aerial vehicle carrying an inertial navigation system by using data chain distance measurement, and solves the problem of positioning of low-precision unmanned aerial vehicles in the current low-cost double-leader heterogeneous formation.
The low-precision low-cost unmanned aerial vehicle position information acquired by the cooperative positioning method can be applied to various cooperative tasks, can be applied to unmanned aerial vehicles in an expanded mode, can improve the precision and the calculation speed of cooperative positioning by improving an optimization method, is not reported in a similar method, belongs to original theoretical innovation, and can obtain additional advantages through practice of the method.
Drawings
For ease of understanding and description, a framework construction diagram is given and explained in connection with the embodiments.
Fig. 1 is a low-cost dual-leader heterogeneous formation data chain communication network topology.
Fig. 2 is a real-time location distribution of dual-lead-5 low-cost drone heterogeneous formation.
FIG. 3 is a dual-fleet-5 low-cost unmanned aerial vehicle co-location solution result X-Y planar projection.
FIG. 4 is a dual-fleet-5 low-cost unmanned aerial vehicle co-location solution result X-Z planar projection.
Fig. 5 is a dual-lead-5 low-cost unmanned aerial vehicle co-location fitness curve.
Detailed Description
The low-cost dual-fleet heterogeneous unmanned aerial vehicle formation cooperative positioning method based on the undirected fully-connected topology, disclosed by the invention, is combined with the inter-aircraft distance measured by the data chain to establish a cooperative positioning nonlinear equation set, and further combined with the low-cost unmanned aerial vehicle inertial navigation system to measure position information and utilize the high-performance fleet to calculate the cooperative positioning position information through the information transfer capability of the network. The following detailed description describes embodiments of the invention, which are exemplary and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The double-collar-5 low-cost unmanned aerial vehicle cooperative positioning method designed according to the method realizes the formation cooperative positioning of the low-cost double-collar heterogeneous unmanned aerial vehicle, and verifies the effectiveness through bringing in the cooperative guidance law.
The true location information for the low cost drone in this example is given below
Low-cost unmanned aerial vehicle M3Location (2km, 1.8km, 2.415 km);
low-cost unmanned aerial vehicle M4Location (2.5km, 2.4km, 1.568 km);
low-cost unmanned aerial vehicle M5Location (3.2km, 1.2km, 2.058 km);
low-cost unmanned aerial vehicle M6Location (3.6km, 2.0km, 1.8 km);
low-cost unmanned aerial vehicle M7Location (2.8km, 1.6km, 2.13 km).
The verification of the low-cost formation cooperative positioning of the double-leading-team unmanned aerial vehicles provided by the invention is as follows:
step one, constructing a heterogeneous formation communication network of double leaders-5 low-cost unmanned aerial vehicles
And (3) constructing a data chain communication network structure shown in the figure 1, selecting a high-performance leader as a computing center node, and acquiring the organic spacing and leader position of the formation at each moment by the node through data chain communication.
Leader M1And M2The real-time location information of (3km, 4km, 2.5km) and (5km, 2km, 2.5 km).
The maximum drift error of the inertial navigation system is eg＝150m。
The information of the measurement position of the low-cost unmanned aerial vehicle inertial measurement unit is known:
low-cost unmanned aerial vehicle M3Measuring the position (1.998km, 1.7834km, 2.465 km);
low-cost unmanned aerial vehicle M4Measuring location (2.5395km, 2.5145km, 1.6194 km);
low-cost unmanned aerial vehicle M5Measuring location (3.2024km, 1.1561km, 2.061 km);
low-cost unmanned aerial vehicle M6Measuring the position (3.584km, 1.947km, 1.806 km);
low-cost unmanned aerial vehicle M7The location was measured (2.774km, 1.698km, 2.207 km).
The data chain ranging information is known:
data chain measurement and leader M1Lead low-cost unmanned aerial vehicle interval
R31＝2418.1m，R41＝1917.97m，R51＝2841.72m，R61＝2202.71m，R71＝2436.58m。
Data chain measurement and leader M2Lead low-cost unmanned aerial vehicle interval
R32＝3007.86m，R42＝2697.89m，R52＝2018.75m，R62＝1565.25m，R72＝2266.47m。
Data chain measurement low-cost unmanned aerial vehicle machine interval
R43＝1152.13m，R53＝1388.32m，R63＝1725.75m，R73＝872.48m，R54＝1473.13m，R64＝1193.24m，R74＝1022.67m，R65＝930.89m，R75＝570.25m，R76＝953.36m。
Step two, establishing a double-leader low-cost heterogeneous formation cooperative positioning equation set
And constructing a double-leader low-cost heterogeneous formation co-location equation set as a formula (2).
Step three, solving the unmanned aerial vehicle formation cooperative positioning equation set
The parameters are initialized as follows
The number of particles N is 100, and the iteration upper limit Num is 1000; maximum velocity V of particles +10; upper bound of weight
Lower bound of weightw0.2; learning factor s11.5 and s20.5; accuracy of stop condition pe＝35m，peg＝6m。
Iterative solution of the cooperative position information by using the particle swarm algorithm can be used, and the fitness of the cooperative position information when the time is 0.198s after 26 iterations is known
max|FiIf the condition of group precision is met, iteration stops, and the result of cooperative positioning is as follows:
low-cost unmanned aerial vehicle M3The positioning position (1.9984km, 1.8031km, 2.4066km), the measurement error is 16.74m, and the positioning error is 9.15 m.
Low-cost unmanned aerial vehicle M4The positioning position (2.5031km, 2.4015km, 1.5636km), the measurement error 131.55m and the positioning error 5.54 m.
Low-cost unmanned aerial vehicle M5The positioning position (3.199km, 1.202km, 2.0474km), the measurement error is 44.03m, and the positioning error is 10.78 m.
Low-cost unmanned aerial vehicle M6The positioning position (3.601km, 2.001km, 1.7988km), the measurement error 55.95m and the positioning error 1.7 m.
Low-cost unmanned aerial vehicle M7The positioning position (2.7961km, 1.6002km, 2.1168km), the measurement error 127.11m and the positioning error 13.79 m.
Fig. 2 is a real-time position diagram of heterogeneous formation of double-collar-5 low-cost unmanned aerial vehicles. FIG. 3 is a projection of the position of the co-location solution results on the X-Y plane. FIG. 4 is a projection of the position of the co-location solution in the X-Z plane. FIG. 5 is a co-location solver fitness curve. In fig. 3 and 4, the position of the low-cost unmanned aerial vehicle is searched in each circle, and a search sphere is drawn by taking the inertial measurement position as the center of the sphere and the maximum value of the inertial measurement error drift error 150m as the radius. FIG. 5 shows the calculation result of the fitness in the iterative solution process of the particle swarm optimization. According to the calculation example, the search is carried out according to the inertial measurement position information and the inertial measurement error range by combining the particle swarm algorithm, the distance measurement precision condition can be met by a small iteration number, the positioning precision is smaller than 15m, and the cooperative positioning effect is good. The method can perform high-precision cooperative positioning based on data chain distance measurement and low-cost inertial navigation system positioning with relatively large errors, and provides a good information basis for low-cost dual-leader heterogeneous unmanned aerial vehicle formation cooperative combat.
The embodiment is an explanation and an explanation of the technical solution of the present invention, which are only examples to verify the effectiveness of the low-cost dual-fleet heterogeneous formation co-location scheme based on data chain ranging, and the scope of right protection cannot be limited thereby. For those skilled in the art, based on the dual-fleet heterogeneous formation cooperative positioning scheme based on data chain ranging, several changes, modifications, substitutions, variations and improvements can be made according to engineering practical requirements. It is intended that all such modifications and variations be included within the scope of the invention as defined in the following claims and the description.
Claims (2)
1. A low-cost double-fleet heterogeneous unmanned aerial vehicle formation cooperative positioning method based on data link communication is characterized by comprising the following steps:
step one, constructing a heterogeneous formation communication network of double leaders:
high performance leader M1And M2Real-time update of the exact position coordinates as (x)1，y1，z1) And (x)2，y2，z2)；
Low-precision unmanned aerial vehicle with inertial navigation system is sequentially recorded as Mm3, 4, 7, the real coordinate position to be resolved is unknown, noted as (x)m，ym，zm)；
The maximum drift error of the inertial navigation system is recorded as egThe inertial measurement position information is known and recorded as
Communication between the leader assurance and all low-cost unmanned aerial vehicles, guarantee undirected full intercommunication between 5 low-cost unmanned aerial vehicles, the unmanned aerial vehicle machine interval that the data chain measurement obtained is marked as RmnThen where m ≠ n, Rmn＝Rnm
All the unmanned aerial vehicle distance information can be concentrated to any selected leader for processing, and the resolving result is fed back to each low-cost unmanned aerial vehicle through data chain communication; the communication relation between the leaders can be designed and adjusted according to the requirement of a guidance scheme;
step two, constructing a double-leader heterogeneous unmanned aerial vehicle formation cooperative positioning equation set:
establishing a nonlinear equation system containing 20 equations for relative distance between two-lead-team heterogeneous unmanned aerial vehicle formation machines as follows
The unmanned aerial vehicle position information comprises position information of an unmanned aerial vehicle to be searched and solved, wherein X is [ X3, y3, z3, X4, y4, z4,. ], X7, y7 and z7 ];
the ith row vector in the equation set F (X) containing 20 equations is denoted as FiThen order function
Is composed of
Wherein | FiL is FiAbsolute value of (d);
therefore, the solving problem of the nonlinear equation set (3) is converted into the solving problem of X so as to obtain the cost function
Minimal problems;
and step three, co-locating the double-collar heterogeneous formation to obtain the current low-cost unmanned aerial vehicle position information.
2. The data chain communication-based low-cost double-fleet heterogeneous unmanned aerial vehicle formation cooperative positioning method according to claim 1, wherein the double-fleet heterogeneous formation cooperative positioning process of the third step specifically includes the following steps:
firstly, initializing a particle swarm algorithm, and initializing a particle swarm and algorithm parameters;
initializing a particle swarm: in measuring position with current inertial navigation system
Is the upper bound e of the error of the spherical center and the inertial navigation systemgRandomly generating N particles in a spherical space with the radius;
initializing algorithm parameters:
the iteration times are recorded as k, and the iteration upper limit Num is recorded;
particle velocity V, maximum particle velocity V+；
Weight weUpper bound of weight
Lower bound of weightw；
Learning factor s1And s2；
Stopping condition population precision peAnd individual precision condition peg；
Secondly, calculating and updating the optimal position;
the optimal position where the particle has been subjected to is pb, the current adaptive value is compared with the self-history best adaptive value, and pb is updated;
the optimal position that the population has undergone is gb, compare the current adaptive value with the historical best adaptive value of oneself, upgrade gb;
furthermore, the particle position and velocity are updated;
generating a random number q1And q is2The particle position and velocity are updated as follows
Judging whether a stop error precision condition p is satisfiedeThat is to say have
Stopping if the position of the unmanned aerial vehicle is met, and continuously iterating to solve the position of the low-cost unmanned aerial vehicle if the position of the unmanned aerial vehicle is not met;
by utilizing the iterative solution of the steps, the position information of the current low-cost unmanned aerial vehicle can be obtained when the stopping condition set by the particle swarm algorithm is met.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210200264.6A CN114727217A (en) | 2022-03-02 | 2022-03-02 | Low-cost double-collar heterogeneous unmanned aerial vehicle formation cooperative positioning method based on data chain communication |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210200264.6A CN114727217A (en) | 2022-03-02 | 2022-03-02 | Low-cost double-collar heterogeneous unmanned aerial vehicle formation cooperative positioning method based on data chain communication |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114727217A true CN114727217A (en) | 2022-07-08 |
Family
ID=82236348
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210200264.6A Pending CN114727217A (en) | 2022-03-02 | 2022-03-02 | Low-cost double-collar heterogeneous unmanned aerial vehicle formation cooperative positioning method based on data chain communication |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114727217A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116320990A (en) * | 2023-05-18 | 2023-06-23 | 北京航空航天大学 | Node dynamic collaborative sensing method for space-based navigation enhanced ad hoc network |
-
2022
- 2022-03-02 CN CN202210200264.6A patent/CN114727217A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116320990A (en) * | 2023-05-18 | 2023-06-23 | 北京航空航天大学 | Node dynamic collaborative sensing method for space-based navigation enhanced ad hoc network |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10140875B1 (en) | Method and apparatus for joint optimization of multi-UAV task assignment and path planning | |
| CN106705970B (en) | Multi-unmanned aerial vehicle collaborative path planning method based on ant colony algorithm | |
| CN103557867B (en) | The collaborative path planning method of a kind of many UAV of three-dimensional based on sparse A* search | |
| CN112947592B (en) | Reentry vehicle trajectory planning method based on reinforcement learning | |
| CN112462785A (en) | Mobile robot path planning method and device and storage medium | |
| CN105700549A (en) | Unmanned plane multi-track planning method based on sequence ecological niche PSO (particle swarm optimization) algorithm | |
| CN107343025A (en) | Time delay optimization method under the distributed satellites cloud and mist network architecture and power consumption constraint | |
| CN102506863A (en) | Universal gravitation search-based unmanned plane air route planning method | |
| CN104390640A (en) | Unmanned aerial vehicle three-dimensional air route planning method based on calculation of ideal fluid numerical value | |
| CN103175516A (en) | Distributed computing method for adjustment of large-scale geodesic control net | |
| CN105300383A (en) | Unmanned aerial vehicle air refueling position and attitude estimation method based on backtracking and searching | |
| CN114727217A (en) | Low-cost double-collar heterogeneous unmanned aerial vehicle formation cooperative positioning method based on data chain communication | |
| Xie et al. | Application of improved Cuckoo search algorithm to path planning unmanned aerial vehicle | |
| Huang et al. | A novel particle swarm optimization algorithm based on reinforcement learning mechanism for AUV path planning | |
| Zhao et al. | A method of path planning for unmanned aerial vehicle based on the hybrid of selfish herd optimizer and particle swarm optimizer | |
| Xie et al. | Mathematical problems in engineering improved CNP‐Method‐Based local real‐time cooperative task allocation of heterogeneous multi‐UAV in communication‐constrained environment | |
| Yujie et al. | Ship path planning based on improved particle swarm optimization | |
| CN113778082B (en) | Unmanned vehicle track tracking control method and system based on self-triggering mechanism | |
| Zhang et al. | An improved heuristic algorithm for UCAV path planning | |
| Khalil et al. | A hybrid modified ABC-PSO algorithm for optimal robotic path planner | |
| CN116257084A (en) | Spacecraft formation track tracking distributed control method and related equipment | |
| Jia et al. | Autonomous mobile robot global path planning: A prior information-based particle swarm optimization approach | |
| Li et al. | A path planning for one UAV based on geometric algorithm | |
| Liu et al. | Research on Adjustment of Bearings-only passive location of UAV Based on Computational geometry | |
| Zhang et al. | Evaluation method of UAV cluster simulation models with different granularity based on sensitivity analysis |
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 |