无人机协同控制与定位
随着无人机技术的快速发展，其在军事、民用领域的应用日益广泛。无人机协同控制与定位是无人机技术的重要组成部分，它关系到无人机编队飞行、任务执行、自主导航等关键功能。本专题围绕着无人机协同与定位技术展开研究成果介绍，主要内容包括无人机集群的规划和运动、无人机的任务调度、无人机及目标的定位，为无人机技术的发展和应用提供支持。
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2022, 44(4):1-12.
DOI: 10.11887/j.cn.202204001
Abstract:
The basic principle, representative methods, and state-of-the-art research of the sub-module related research within the general framework of cooperative maneuvering flight planning from single UAV (unmanned aerial vehicle) maneuvering flight to multi-UAV cooperative planning were introduced. It mainly included real-time navigation map construction, discrete-space path planning, continuous-space trajectory planning, hybrid planning based on discrete-space and continuous-space, and multi-courses/trajectories cooperative planning. The next research directions were proposed based on the major technologies of the planning framework.
The basic principle, representative methods, and state-of-the-art research of the sub-module related research within the general framework of cooperative maneuvering flight planning from single UAV (unmanned aerial vehicle) maneuvering flight to multi-UAV cooperative planning were introduced. It mainly included real-time navigation map construction, discrete-space path planning, continuous-space trajectory planning, hybrid planning based on discrete-space and continuous-space, and multi-courses/trajectories cooperative planning. The next research directions were proposed based on the major technologies of the planning framework.
2021, 43(4):163-170.
DOI: 10.11887/j.cn.202104020
Abstract:
The related concept of intelligent unmanned swarm operation was introduced. In order to reflect the autonomous capability and adaptive capability of intelligent unmanned simulation entity, the OODA-L (observe, orient, decide, act and learning) pattern was proposed to make the learning process explicit, and was further extended to Co-OODA-L pattern. In the general description of the intelligent unmanned simulation entity, Markov decision process was adopted to carry out the mathematical abstraction, and a three-domain hierarchical structure of intelligent unmanned Agent was proposed. In order to embody the characteristics of autonomous cooperation and distribution of intelligent unmanned swarm operation, an architecture for cooperative operation modeling of swarm was proposed, which uses an artificial neural network to integrate a variable number of intelligent unmanned Agents into a homogeneous or heterogeneous swarm.
The related concept of intelligent unmanned swarm operation was introduced. In order to reflect the autonomous capability and adaptive capability of intelligent unmanned simulation entity, the OODA-L (observe, orient, decide, act and learning) pattern was proposed to make the learning process explicit, and was further extended to Co-OODA-L pattern. In the general description of the intelligent unmanned simulation entity, Markov decision process was adopted to carry out the mathematical abstraction, and a three-domain hierarchical structure of intelligent unmanned Agent was proposed. In order to embody the characteristics of autonomous cooperation and distribution of intelligent unmanned swarm operation, an architecture for cooperative operation modeling of swarm was proposed, which uses an artificial neural network to integrate a variable number of intelligent unmanned Agents into a homogeneous or heterogeneous swarm.
2022, 44(4):22-31.
DOI: 10.11887/j.cn.202204003
Abstract:
In order to improve the efficiency of the UAV (unmanned aerial vehicle) swarm in cooperative search for moving targets, a UAV swarm cooperative search method was proposed on the basis of the moth pheromone courtship mechanism. According to the courtship behavior of moth in choosing flight direction by pheromone, a wind direction model in pheromone map and a moth pheromone courtship model were established. Considering the constraint of collision avoidance between UAV swarm, a map from moth pheromone courtship mechanism to UAV swarm distributed cooperative search was proposed, and the specific implementation process was given. Simulation results show the effectiveness and stability of the proposed method in solving the cooperative search problem of single moving target, and the outdoor flight experimental results verified the feasibility of the proposed method in practice.
In order to improve the efficiency of the UAV (unmanned aerial vehicle) swarm in cooperative search for moving targets, a UAV swarm cooperative search method was proposed on the basis of the moth pheromone courtship mechanism. According to the courtship behavior of moth in choosing flight direction by pheromone, a wind direction model in pheromone map and a moth pheromone courtship model were established. Considering the constraint of collision avoidance between UAV swarm, a map from moth pheromone courtship mechanism to UAV swarm distributed cooperative search was proposed, and the specific implementation process was given. Simulation results show the effectiveness and stability of the proposed method in solving the cooperative search problem of single moving target, and the outdoor flight experimental results verified the feasibility of the proposed method in practice.
2022, 44(6):151-162.
DOI: 10.11887/j.cn.202206019
Abstract:
Aiming at the global task planning problem of large-scale heterogeneous UAV (unmanned aerial vehicle) swarm, a task planning method based on balanced clustering market auction mechanism was proposed. Scene of completing tasks by collaborative UAVs was analyzed, and a task planning model with high generality was established by combining the advantages of task clustering and UAV coalition. Considering the demand for load balance of UAVs, a new balanced clustering market auction algorithm which comprehensively considers the travel consumption and task consumption was established by integrating and improving the K-means algorithm and market auction mechanism. The balance parameter was introduced into the auction process. By solving the traveling salesman problem to modify the balance parameter, the total cost was continuously reduced while ensuring the load balance. The simulation results show that the task planning method using balanced clustering market auction mechanism can complete the complex task planning of heterogeneous UAV swarm in a short time, ensures the load balance of UAV coalitions, and has good performance in total cost and total time, exhibiting certain practical application value.
Aiming at the global task planning problem of large-scale heterogeneous UAV (unmanned aerial vehicle) swarm, a task planning method based on balanced clustering market auction mechanism was proposed. Scene of completing tasks by collaborative UAVs was analyzed, and a task planning model with high generality was established by combining the advantages of task clustering and UAV coalition. Considering the demand for load balance of UAVs, a new balanced clustering market auction algorithm which comprehensively considers the travel consumption and task consumption was established by integrating and improving the K-means algorithm and market auction mechanism. The balance parameter was introduced into the auction process. By solving the traveling salesman problem to modify the balance parameter, the total cost was continuously reduced while ensuring the load balance. The simulation results show that the task planning method using balanced clustering market auction mechanism can complete the complex task planning of heterogeneous UAV swarm in a short time, ensures the load balance of UAV coalitions, and has good performance in total cost and total time, exhibiting certain practical application value.
2023, 45(4):45-54.
DOI: 10.11887/j.cn.202304006
Abstract:
Aiming at the problem of autonomous coordination of networked UAV swarm and the advantages and disadvantages of market auction method, the idea of “computation-for-communication” and its corresponding distributed task scheduling method were proposed. By analyzing explicit and implicit conflicting tasks, a set of task-related agents was established. A local optimization method based on task suppression was proposed to resolve some task conflicts in advance, so as to reduce the number of algorithm iterations. An agent position inference method based on historical bidding information was designed to provide necessary information input for local optimization. Monte Carlo simulation experiments were carried out based on the networking simulation platform and the swarm rescue scenario. The results show that compared with the representative consensus-based bundle algorithm and performance impact algorithm in the market auction method, the proposed method can obtain fewer iterations, shorter convergence time and better scheduling performance.
Aiming at the problem of autonomous coordination of networked UAV swarm and the advantages and disadvantages of market auction method, the idea of “computation-for-communication” and its corresponding distributed task scheduling method were proposed. By analyzing explicit and implicit conflicting tasks, a set of task-related agents was established. A local optimization method based on task suppression was proposed to resolve some task conflicts in advance, so as to reduce the number of algorithm iterations. An agent position inference method based on historical bidding information was designed to provide necessary information input for local optimization. Monte Carlo simulation experiments were carried out based on the networking simulation platform and the swarm rescue scenario. The results show that compared with the representative consensus-based bundle algorithm and performance impact algorithm in the market auction method, the proposed method can obtain fewer iterations, shorter convergence time and better scheduling performance.
2024, 46(3):126-136.
DOI: 10.11887/j.cn.202403013
Abstract:
Unmanned swarm operation is becoming a new operation style that changes the shape of warfare. In response to the current problem that unmanned equipment experiments are relatively independent and lack of integrated closed-loop design and integrated verification means, a SoS(system-of-system) design, simulation and evaluation method was proposed. The method contains five stages:operational concept modeling and requirement analysis, SoS design concept and SoS design, infield simulation experiment and scheme exploration, SoS prototype development and evaluation optimization, and outfield integrated test and comprehensive decision. It innovates and develops theoretical methods, tool chains, and integrated environment for SoS design, simulation test, and evaluation optimization, which provides certain theoretical and technical support for the systematization intellectualization and actual combat of unmanned swarm.
Unmanned swarm operation is becoming a new operation style that changes the shape of warfare. In response to the current problem that unmanned equipment experiments are relatively independent and lack of integrated closed-loop design and integrated verification means, a SoS(system-of-system) design, simulation and evaluation method was proposed. The method contains five stages:operational concept modeling and requirement analysis, SoS design concept and SoS design, infield simulation experiment and scheme exploration, SoS prototype development and evaluation optimization, and outfield integrated test and comprehensive decision. It innovates and develops theoretical methods, tool chains, and integrated environment for SoS design, simulation test, and evaluation optimization, which provides certain theoretical and technical support for the systematization intellectualization and actual combat of unmanned swarm.
2021, 43(1):117-126.
DOI: 10.11887/j.cn.202101016
Abstract:
Detecting and recognizing targets on the ground is one of the typical tasks of UAVs (unmanned aerial vehicles), but it is limited by the task particularity so that it is often difficult to obtain sufficient data about target samples to achieve highly reliable target recognition. In view of this problem, a small-sample vehicle target recognition method based on the component model was proposed, which combined the cognitive characteristics of human beings to improve the perception ability of ground targets. The possible region of the target was extracted by visual saliency detection and objectness detection, and then the GrabCut segmentation method based on the Graph theory and the maximum between-class variance was used to segment the target and to extract the components from the target. A component recognition method based on a probability map model was used to perform component recognition by sparsely representing a component outline as a conditional random field and performing probabilistic reasoning. The Bayesian-based target recognition method was used to determine whether the target was a vehicle. Verification on real images captured by the UAV showed that the algorithm can detect and identify the vehicle target with high accuracy under the condition of fewer samples, poorer illumination and certain occlusion. At the same time, the recognition method can achieve the effect of certain interpretability.
Detecting and recognizing targets on the ground is one of the typical tasks of UAVs (unmanned aerial vehicles), but it is limited by the task particularity so that it is often difficult to obtain sufficient data about target samples to achieve highly reliable target recognition. In view of this problem, a small-sample vehicle target recognition method based on the component model was proposed, which combined the cognitive characteristics of human beings to improve the perception ability of ground targets. The possible region of the target was extracted by visual saliency detection and objectness detection, and then the GrabCut segmentation method based on the Graph theory and the maximum between-class variance was used to segment the target and to extract the components from the target. A component recognition method based on a probability map model was used to perform component recognition by sparsely representing a component outline as a conditional random field and performing probabilistic reasoning. The Bayesian-based target recognition method was used to determine whether the target was a vehicle. Verification on real images captured by the UAV showed that the algorithm can detect and identify the vehicle target with high accuracy under the condition of fewer samples, poorer illumination and certain occlusion. At the same time, the recognition method can achieve the effect of certain interpretability.
2021, 43(2):40-46.
DOI: 10.11887/j.cn.202102006
Abstract:
An induction strategy based on position spoofing for UAV (unmanned aerial vehicle) was presented to solve the problem of effective control and disposal of unauthorized UAV. The target UAV navigation and control systems got the fictitious position information caused by the spoofing signals that were produced by combination of accurate position information of the target and the induction strategy. The target UAV changed its flying attitudes and deviation from the pre-specification air route. Experimental results demonstrate the validity and effectiveness of the proposed induction strategy.
An induction strategy based on position spoofing for UAV (unmanned aerial vehicle) was presented to solve the problem of effective control and disposal of unauthorized UAV. The target UAV navigation and control systems got the fictitious position information caused by the spoofing signals that were produced by combination of accurate position information of the target and the induction strategy. The target UAV changed its flying attitudes and deviation from the pre-specification air route. Experimental results demonstrate the validity and effectiveness of the proposed induction strategy.
9 Analysis of UAV multi-frame fusion location and error convergence characteristic for ground target
2021, 43(2):66-73.
DOI: 10.11887/j.cn.202102010
Abstract:
High-precision positioning of the ground target is an important premise for the UAV (unmanned aerial vehicle) to carry out target reconnaissance, firepower guidance, effectiveness evaluation, etc. However, the accuracy of the UAV′ s target positioning is limited by factors such as many error factors and long transmission chain. The method of multi-frame image registration method based on Kalman filter for ground target location was studied. By combining the multi-frame target image acquired by UAV, the high-precision fusion positioning of UAV to ground target was studied based on Kalman filter. Monte Carlo method was introduced to simulate the error convergence, value and distribution of multi-frame fusion location method based on Kalman filter. The influence of observation interval and line-of-sight elevation angle on error convergence was analyzed. Several suggestions for improving the positioning accuracy were proposed.
High-precision positioning of the ground target is an important premise for the UAV (unmanned aerial vehicle) to carry out target reconnaissance, firepower guidance, effectiveness evaluation, etc. However, the accuracy of the UAV′ s target positioning is limited by factors such as many error factors and long transmission chain. The method of multi-frame image registration method based on Kalman filter for ground target location was studied. By combining the multi-frame target image acquired by UAV, the high-precision fusion positioning of UAV to ground target was studied based on Kalman filter. Monte Carlo method was introduced to simulate the error convergence, value and distribution of multi-frame fusion location method based on Kalman filter. The influence of observation interval and line-of-sight elevation angle on error convergence was analyzed. Several suggestions for improving the positioning accuracy were proposed.
2022, 44(6):126-133.
DOI: 10.11887/j.cn.202206016
Abstract:
To solve the problem of radiation-source target localization for a single UAV(unmanned aerial vehicle) in an urban environment, a new trajectory optimization algorithm for bearings-only target localization based on the environment prediction method was proposed. Interacting multiple model methods coupled with the extended Kalman filter was used to estimate the target localization in the line-of-sight and non-line-of-sight mixed environment. Based on the estimated target location and urban geographic information system, the electromagnetic signal occlusion region and the multipath interference region were calculated by using the line of sight tracking method. Under the framework of receding horizon method, the UAV prediction trajectory was generated, so as to maximize the Fisher information matrix determinant as the orientation positioning evaluation criterion. Considering the influence of building obstacles and their occlusion and reflection effects in the localization process, the UAV was controlled to choose the optimal heading flight.The numerical simulation results show that the trajectory optimization algorithm enables the UAV to perform high-precision bearings-only target localization in the complex environment containing obstacles, signal occlusion, and multipath interference. The algorithm provides a new way to solve the problem of bearings-only target localization for single UAV in an urban environment.
To solve the problem of radiation-source target localization for a single UAV(unmanned aerial vehicle) in an urban environment, a new trajectory optimization algorithm for bearings-only target localization based on the environment prediction method was proposed. Interacting multiple model methods coupled with the extended Kalman filter was used to estimate the target localization in the line-of-sight and non-line-of-sight mixed environment. Based on the estimated target location and urban geographic information system, the electromagnetic signal occlusion region and the multipath interference region were calculated by using the line of sight tracking method. Under the framework of receding horizon method, the UAV prediction trajectory was generated, so as to maximize the Fisher information matrix determinant as the orientation positioning evaluation criterion. Considering the influence of building obstacles and their occlusion and reflection effects in the localization process, the UAV was controlled to choose the optimal heading flight.The numerical simulation results show that the trajectory optimization algorithm enables the UAV to perform high-precision bearings-only target localization in the complex environment containing obstacles, signal occlusion, and multipath interference. The algorithm provides a new way to solve the problem of bearings-only target localization for single UAV in an urban environment.
