Complex multi-area collaborative coverage path planning in dynamic environments poses a significant challenge for multi-fixed-wing UAVs(multi-UAV).This study establishes a comprehensive framework that incorporates UAV...Complex multi-area collaborative coverage path planning in dynamic environments poses a significant challenge for multi-fixed-wing UAVs(multi-UAV).This study establishes a comprehensive framework that incorporates UAV capabilities,terrain,complex areas,and mission dynamics.A novel dynamic collaborative path planning algorithm is introduced,designed to ensure complete coverage of designated areas.This algorithm meticulously optimizes the operation,entry,and transition paths for each UAV,while also establishing evaluation metrics to refine coverage sequences for each area.Additionally,a three-dimensional path is computed utilizing an altitude descent method,effectively integrating twodimensional coverage paths with altitude constraints.The efficacy of the proposed approach is validated through digital simulations and mixed-reality semi-physical experiments across a variety of dynamic scenarios,including both single-area and multi-area coverage by multi-UAV.Results show that the coverage paths generated by this method significantly reduce both computation time and path length,providing a reliable solution for dynamic multi-UAV mission planning in semi-physical environments.展开更多
The design of unmanned aerial vehicles(UAVs)revolves around the careful selection of materials that are both lightweight and robust.Carbon fiber-reinforced polymer(CFRP)emerged as an ideal option for wing construction...The design of unmanned aerial vehicles(UAVs)revolves around the careful selection of materials that are both lightweight and robust.Carbon fiber-reinforced polymer(CFRP)emerged as an ideal option for wing construction,with its mechanical qualities thoroughly investigated.In this study,we developed and optimized a conceptual UAV wing to withstand structural loads by establishing progressive composite stacking sequences,and we conducted a series of experimental characterizations on the resulting material.In the optimization phase,the objective was defined as weight reduction,while the Hashin damage criterion was established as the constraint for the optimization process.The optimization algorithm adaptively monitors regional damage criterion values,implementing necessary adjustments to facilitate the mitigation process in a cost-effective manner.Optimization of the analytical model using Simulia Abaqus~(TM)and a Python-based user-defined sub-routine resulted in a 34.7%reduction in the wing's structural weight after 45 iterative rounds.Then,the custom-developed optimization algorithm was compared with a genetic algorithm optimization.This comparison has demonstrated that,although the genetic algorithm explores numerous possibilities through hybridization,the custom-developed algorithm is more result-oriented and achieves optimization in a reduced number of steps.To validate the structural analysis,test specimens were fabricated from the wing's most critically loaded segment,utilizing the identical stacking sequence employed in the optimization studies.Rigorous mechanical testing revealed unexpectedly high compressive strength,while tensile and bending strengths fell within expected ranges.All observed failure loads remained within the established safety margins,thereby confirming the reliability of the analytical predictions.展开更多
As battlefield scale enlarges,cross-platform collaborative combat provides an appealing paradigm for modern warfare.Complicated constraints and vast solution space pose great challenge for reasonable and efficient mis...As battlefield scale enlarges,cross-platform collaborative combat provides an appealing paradigm for modern warfare.Complicated constraints and vast solution space pose great challenge for reasonable and efficient mission planning,where path planning and target assignment are tightly coupled.In this paper,we focus on UAV mission planning under carrier delivery mode(e.g.,by aircraft carrier,ground vehicle,or transport aircraft) and design a three-layer hierarchical solution framework.In the first layer,we simultaneously determine delivery points and target set division by clustering.To address the safety concerns of radar risk and UAV endurance,an improved density peak clustering algorithm is developed by constraint fusio n.In the second layer,mission planning within each cluster is viewed as a coope rative multiple-task assignment problem.A hybrid heuristic algorithm that integrates a voting-based heuristic solution generation strategy(VHSG) and a stochastic variable neighborhood search(SVNS),called VHSG-SVNS,is proposed for rapid solution.Based on the results of the first two layers,the third layer transforms carrier path planning into a multiple-vehicle routing problem with time window.The cost between any two nodes is calculated by the A~* algorithm,and the genetic algorithm is then implemented to determine the global route.Finally,a practical mission scenario containing 200 targets is used to validate the effectiveness of the designed framework,where three layers cooperate well with each other to generate satisfactory combat scheduling.Comparisons are made in each layer to highlight optimum-seeking capability and efficiency of the proposed algorithms.Works done in this paper provide a simple but efficient solution framework for cross-platform cooperative mission planning problems,and can be potentially extended to other applications such as post-disaster search and rescue,forest surveillance and firefighting,logistics pick and delivery,etc.展开更多
To address the confrontation decision-making issues in multi-round air combat,a dynamic game decision method is proposed based on decision tree for the confrontation of unmanned aerial vehicle(UAV)air combat.Based on ...To address the confrontation decision-making issues in multi-round air combat,a dynamic game decision method is proposed based on decision tree for the confrontation of unmanned aerial vehicle(UAV)air combat.Based on game the-ory and the confrontation characteristics of air combat,a dynamic game process is constructed including the strategy sets,the situation information,and the maneuver decisions for both sides of air combat.By analyzing the UAV’s flight dyna-mics and the both sides’information,a payment matrix is estab-lished through the situation advantage function,performance advantage function,and profit function.Furthermore,the dynamic game decision problem is solved based on the linear induction method to obtain the Nash equilibrium solution,where the decision tree method is introduced to obtain the optimal maneuver decision,thereby improving the situation advantage in the next round of confrontation.According to the analysis,the simulation results for the confrontation scenarios of multi-round air combat are presented to verify the effectiveness and advan-tages of the proposed method.展开更多
To overcome nonlinear and 6-DOF(degrees of freedom)under-actuated problems for the attitude and position of quadrotor UAVs,an adaptive backstepping sliding mode method for flight attitude of quadrotor UAVs is proposed...To overcome nonlinear and 6-DOF(degrees of freedom)under-actuated problems for the attitude and position of quadrotor UAVs,an adaptive backstepping sliding mode method for flight attitude of quadrotor UAVs is proposed,in which an adaptive law is designed to online estimate the parameter variations and the upper bound of external disturbances and the assessments is utilized to compensate the backstepping sliding mode control.In addition,the tracking error of the design method is shown to asymptotically converge to zero by using Lyapunov theory.Finally,based on the numerical simulation of quadrotor UAVs using the setting parameters,the results show that the proposed control approach can stabilize the attitude and has hover flight capabilities under the parameter perturbations and external disturbances.展开更多
In this paper,based on a bidirectional parallel multi-branch feature pyramid network(BPMFPN),a novel one-stage object detector called BPMFPN Det is proposed for real-time detection of ground multi-scale targets by swa...In this paper,based on a bidirectional parallel multi-branch feature pyramid network(BPMFPN),a novel one-stage object detector called BPMFPN Det is proposed for real-time detection of ground multi-scale targets by swarm unmanned aerial vehicles(UAVs).First,the bidirectional parallel multi-branch convolution modules are used to construct the feature pyramid to enhance the feature expression abilities of different scale feature layers.Next,the feature pyramid is integrated into the single-stage object detection framework to ensure real-time performance.In order to validate the effectiveness of the proposed algorithm,experiments are conducted on four datasets.For the PASCAL VOC dataset,the proposed algorithm achieves the mean average precision(mAP)of 85.4 on the VOC 2007 test set.With regard to the detection in optical remote sensing(DIOR)dataset,the proposed algorithm achieves 73.9 mAP.For vehicle detection in aerial imagery(VEDAI)dataset,the detection accuracy of small land vehicle(slv)targets reaches 97.4 mAP.For unmanned aerial vehicle detection and tracking(UAVDT)dataset,the proposed BPMFPN Det achieves the mAP of 48.75.Compared with the previous state-of-the-art methods,the results obtained by the proposed algorithm are more competitive.The experimental results demonstrate that the proposed algorithm can effectively solve the problem of real-time detection of ground multi-scale targets in aerial images of swarm UAVs.展开更多
Aiming at the suppression of enemy air defense(SEAD)task under the complex and complicated combat sce-nario,the spatiotemporal cooperative path planning methods are studied in this paper.The major research contents in...Aiming at the suppression of enemy air defense(SEAD)task under the complex and complicated combat sce-nario,the spatiotemporal cooperative path planning methods are studied in this paper.The major research contents include opti-mal path points generation,path smoothing and cooperative rendezvous.In the path points generation part,the path points availability testing algorithm and the path segments availability testing algorithm are designed,on this foundation,the swarm intelligence-based path point generation algorithm is utilized to generate the optimal path.In the path smoothing part,taking ter-minal attack angle constraint and maneuverability constraint into consideration,the Dubins curve is introduced to smooth the path segments.In cooperative rendezvous part,we take esti-mated time of arrival requirement constraint and flight speed range constraint into consideration,the speed control strategy and flight path control strategy are introduced,further,the decoupling scheme of the circling maneuver and detouring maneuver is designed,in this case,the maneuver ways,maneu-ver point,maneuver times,maneuver path and flight speed are determined.Finally,the simulation experiments are conducted and the acquired results reveal that the time-space cooperation of multiple unmanned aeriel vehicles(UAVs)is effectively real-ized,in this way,the combat situation suppression against the enemy can be realized in SEAD scenarios.展开更多
This paper addresses a multicircular circumnavigation control for UAVs with desired angular spacing around a nonstationary target.By defining a coordinated error relative to neighboring angular spacing,under the premi...This paper addresses a multicircular circumnavigation control for UAVs with desired angular spacing around a nonstationary target.By defining a coordinated error relative to neighboring angular spacing,under the premise that target information is perfectly accessible by all nodes,a centralized circular enclosing control strategy is derived for multiple UAVs connected by an undirected graph to allow for formation behaviors concerning the moving target.Besides,to avoid the requirement of target’s states being accessible for each UAV,fixed-time distributed observers are introduced to acquire the state estimates in a fixed-time sense,and the upper boundary of settling time can be determined offline irrespective of initial properties,greatly releasing the burdensome communication traffic.Then,with the aid of fixed-time distributed observers,a distributed circular circumnavigation controller is derived to force all UAVs to collaboratively evolve along the preset circles while keeping a desired angular spacing.It is inferred from Lyapunov stability that all errors are demonstrated to be convergent.Simulations are offered to verify the utility of proposed protocol.展开更多
This paper mainly studies the problem of using UAVs to provide accurate remote target indication for hypersonic projectiles.Based on the optimal trajectory trends and feedback guidance methods,a new cooperative contro...This paper mainly studies the problem of using UAVs to provide accurate remote target indication for hypersonic projectiles.Based on the optimal trajectory trends and feedback guidance methods,a new cooperative control algorithm is proposed to optimize trajectories of multi-UAVs for target tracking in approaching stage.Based on UAV kinematics and sensor performance models,optimal trajectory trends of UAVs are analyzed theoretically.Then,feedback guidance methods are proposed under the optimal observation trends of UAVs in the approaching target stage,producing trajectories with far less computational complexity and performance very close to the best-known trajectories.Next,the sufficient condition for the UAV to form the optimal observation configuration by the feedback guidance method is presented,which guarantees that the proposed method can optimize the observation trajectory of the UAV in approaching stage.Finally,the feedback guidance method is numerically simulated.Simulation results demonstrate that the estimation performance of the feedback guidance method is superior to the Lyapunov guidance vector field(LGVF)method and verify the effectiveness of the proposed method.Additionally,compared with the receding horizon optimization(RHO)method,the proposed method has the same optimization ability as the RHO method and better real-time performance.展开更多
Most researches associated with target encircling control are focused on moving along a circular orbit under an ideal environment free from external disturbances.However,elliptical encirclement with a time-varying obs...Most researches associated with target encircling control are focused on moving along a circular orbit under an ideal environment free from external disturbances.However,elliptical encirclement with a time-varying observation radius,may permit a more flexible and high-efficacy enclosing solution,whilst the non-orthogonal property between axial and tangential speed components,non-ignorable environmental perturbations,and strict assignment requirements empower elliptical encircling control to be more challenging,and the relevant investigations are still open.Following this line,an appointed-time elliptical encircling control rule capable of reinforcing circumnavigation performances is developed to enable Unmanned Aerial Vehicles(UAVs)to move along a specified elliptical path within a predetermined reaching time.The remarkable merits of the designed strategy are that the relative distance controlling error can be guaranteed to evolve within specified regions with a designer-specified convergence behavior.Meanwhile,wind perturbations can be online counteracted based on an unknown system dynamics estimator(USDE)with only one regulating parameter and high computational efficiency.Lyapunov tool demonstrates that all involved error variables are ultimately limited,and simulations are implemented to confirm the usability of the suggested control algorithm.展开更多
This paper studies the problem of using multiple unmanned air vehicles (UAVs) to search for moving targets with sensing capabilities. When multiple UAVs (multi-UAV) search for a number of moving targets in the mission...This paper studies the problem of using multiple unmanned air vehicles (UAVs) to search for moving targets with sensing capabilities. When multiple UAVs (multi-UAV) search for a number of moving targets in the mission area, the targets can intermittently obtain the position information of the UAVs from sensing devices, and take appropriate actions to increase the distance between themselves and the UAVs. Aiming at this problem, an environment model is established using the search map, and the updating method of the search map is extended by considering the sensing capabilities of the moving targets. A multi-UAV search path planning optimization model based on the model predictive control (MPC) method is constructed, and a hybrid particle swarm optimization algorithm with a crossover operator is designed to solve the model. Simulation results show that the proposed method can effectively improve the cooperative search efficiency and can find more targets per unit time compared with the coverage search method and the random search method.展开更多
To ensure multiple unmanned aerial vehicles （UAVs）reach stable formation quickly, a cooperative guidance law basedon the back-stepping-like approach is designed in this paper.Adopting the guidance mechanism of virtu...To ensure multiple unmanned aerial vehicles （UAVs）reach stable formation quickly, a cooperative guidance law basedon the back-stepping-like approach is designed in this paper.Adopting the guidance mechanism of virtue leader vehicle, thedynamic equation of tracking errors for each UAV is built. Thecommunication interactive relationships are described based ongraph theory, and the guidance law for formation reaching is ob-tained by the back-stepping-like approach. The formation stabilityis analyzed by constructing an appropriate Lyapunov function. Thesimulation results have shown that this guidance and control lawcan make each UAV converge to the trajectory of the virtue leaderultimately, and has the quicker rate of convergence and lowertracking error.展开更多
Based on multiple unmanned aerial vehicles（UAVs） flight at a constant altitude,a fault-tolerant cooperative localization algorithm against global positioning system（GPS） signal loss due to GPS receiver malfunction...Based on multiple unmanned aerial vehicles（UAVs） flight at a constant altitude,a fault-tolerant cooperative localization algorithm against global positioning system（GPS） signal loss due to GPS receiver malfunction is proposed.Contrast to the traditional means with single UAV,the proposed method is based on the use of inter-UAV relative range measurements against GPS signal loss and more suitable for the small-size and low-cost UAV applications.Firstly,for re-localizing an UAV with a malfunction in its GPS receiver,an algorithm which makes use of any other three healthy UAVs in the cooperative flight as the reference points for re-localization is proposed.Secondly,by using the relative ranges from the faulty UAV to the other three UAVs,its horizontal location can be determined after the GPS signal is lost.In order to improve an accuracy of the localization,a Kalman filter is further exploited to provide the estimated location of the UAV with the GPS signal loss.The Kalman filter calculates the variance of observations in terms of horizontal dilution of positioning（HDOP） automatically.Then,during each discrete computing time step,the best reference points are selected adaptively by minimizing the HDOP.Finally,two simulation examples in Matlab/Simulink environment with five UAVs in cooperative flight are shown to evaluate the effectiveness of the proposed method.展开更多
This paper focuses on the solution to the dynamic affine formation control problem for multiple networked underactuated quad-rotor unmanned aerial vehicles(UAVs)to achieve a configuration that preserves collinearity a...This paper focuses on the solution to the dynamic affine formation control problem for multiple networked underactuated quad-rotor unmanned aerial vehicles(UAVs)to achieve a configuration that preserves collinearity and ratios of distances for a target configuration.In particular,it is investigated that the quad-rotor UAVs are steered to track a reference linear velocity while maintaining a desired three-dimensional target formation.Firstly,by integrating the properties of the affine transformation and the stress matrix,the design of the target formation is convenient and applicable for various three-dimensional geometric patterns.Secondly,a distributed control method is proposed under a hierarchical framework.By introducing an intermediary control input for each quad-rotor UAV in the position loop,the necessary thrust input and the desired attitude are extracted.In the attitude loop,the desired attitude represented by the unit quaternion is tracked by the designed torque input.Both conditions of linear velocity unavailability and mutual collision avoidance are also tackled.In terms of Lyapunov theory,it is prooved that the overall closed-loop error system is asymptotically stable.Finally,two illustrative examples are simulated to validate the effectiveness of the proposed theoretical results.展开更多
在林业管理中,及时发现火灾并识别其规模对于安全防护和治理火灾至关重要。针对现有火灾检测算法存在的精度低、漏检误检和实时性不足等问题,提出一种无人机航拍图像下火灾实时检测算法——MDSYOLOv8。以YOLOv8为基线算法,将骨干网络第...在林业管理中,及时发现火灾并识别其规模对于安全防护和治理火灾至关重要。针对现有火灾检测算法存在的精度低、漏检误检和实时性不足等问题,提出一种无人机航拍图像下火灾实时检测算法——MDSYOLOv8。以YOLOv8为基线算法,将骨干网络第7层卷积模块和颈部网络卷积模块替换成动态蛇形卷积(DSConv),提高算法的特征提取性能,并强化算法对微小特征的学习能力;然后在颈部与检测头之间添加多维协作注意力机制(MCA),加强颈部特征融合,增强算法对小目标的检测能力,并抑制无关背景信息;最后使用SIoU损失函数替换原YOLOv8中的CIoU损失函数,加快算法的收敛速度和回归精度。实验结果表明,MDSYOLOv8在公开数据集KMU上对烟雾目标的检测精度mAP达到95.89%,相较于基线YOLOv8提高了3.33个百分点,具有卓越的检测性能。此外,本研究采集互联网上的无人机航拍火灾图像制作UFF(UAV field fire)数据集,主要对象为火焰和烟雾,包含森林和城市等火灾隐患可能发生场景。在自制数据集UFF上进行深度实验分析,MDSYOLOv8的检测精度达到93.98%,检测速度为54帧/s,并且能同时识别烟雾和火焰两种火灾场景中的主要目标,与主流目标检测方法相比,在检测精度和效率方面均展现出明显优势,更加契合航拍场景下的火灾检测应用。展开更多
基金National Natural Science Foundation of China(Grant No.52472417)to provide fund for conducting experiments.
文摘Complex multi-area collaborative coverage path planning in dynamic environments poses a significant challenge for multi-fixed-wing UAVs(multi-UAV).This study establishes a comprehensive framework that incorporates UAV capabilities,terrain,complex areas,and mission dynamics.A novel dynamic collaborative path planning algorithm is introduced,designed to ensure complete coverage of designated areas.This algorithm meticulously optimizes the operation,entry,and transition paths for each UAV,while also establishing evaluation metrics to refine coverage sequences for each area.Additionally,a three-dimensional path is computed utilizing an altitude descent method,effectively integrating twodimensional coverage paths with altitude constraints.The efficacy of the proposed approach is validated through digital simulations and mixed-reality semi-physical experiments across a variety of dynamic scenarios,including both single-area and multi-area coverage by multi-UAV.Results show that the coverage paths generated by this method significantly reduce both computation time and path length,providing a reliable solution for dynamic multi-UAV mission planning in semi-physical environments.
基金supported by the Istanbul Technical University Office of Scientific Research Projects(ITUBAPSIS),under grant MYL-2022-43776。
文摘The design of unmanned aerial vehicles(UAVs)revolves around the careful selection of materials that are both lightweight and robust.Carbon fiber-reinforced polymer(CFRP)emerged as an ideal option for wing construction,with its mechanical qualities thoroughly investigated.In this study,we developed and optimized a conceptual UAV wing to withstand structural loads by establishing progressive composite stacking sequences,and we conducted a series of experimental characterizations on the resulting material.In the optimization phase,the objective was defined as weight reduction,while the Hashin damage criterion was established as the constraint for the optimization process.The optimization algorithm adaptively monitors regional damage criterion values,implementing necessary adjustments to facilitate the mitigation process in a cost-effective manner.Optimization of the analytical model using Simulia Abaqus~(TM)and a Python-based user-defined sub-routine resulted in a 34.7%reduction in the wing's structural weight after 45 iterative rounds.Then,the custom-developed optimization algorithm was compared with a genetic algorithm optimization.This comparison has demonstrated that,although the genetic algorithm explores numerous possibilities through hybridization,the custom-developed algorithm is more result-oriented and achieves optimization in a reduced number of steps.To validate the structural analysis,test specimens were fabricated from the wing's most critically loaded segment,utilizing the identical stacking sequence employed in the optimization studies.Rigorous mechanical testing revealed unexpectedly high compressive strength,while tensile and bending strengths fell within expected ranges.All observed failure loads remained within the established safety margins,thereby confirming the reliability of the analytical predictions.
文摘As battlefield scale enlarges,cross-platform collaborative combat provides an appealing paradigm for modern warfare.Complicated constraints and vast solution space pose great challenge for reasonable and efficient mission planning,where path planning and target assignment are tightly coupled.In this paper,we focus on UAV mission planning under carrier delivery mode(e.g.,by aircraft carrier,ground vehicle,or transport aircraft) and design a three-layer hierarchical solution framework.In the first layer,we simultaneously determine delivery points and target set division by clustering.To address the safety concerns of radar risk and UAV endurance,an improved density peak clustering algorithm is developed by constraint fusio n.In the second layer,mission planning within each cluster is viewed as a coope rative multiple-task assignment problem.A hybrid heuristic algorithm that integrates a voting-based heuristic solution generation strategy(VHSG) and a stochastic variable neighborhood search(SVNS),called VHSG-SVNS,is proposed for rapid solution.Based on the results of the first two layers,the third layer transforms carrier path planning into a multiple-vehicle routing problem with time window.The cost between any two nodes is calculated by the A~* algorithm,and the genetic algorithm is then implemented to determine the global route.Finally,a practical mission scenario containing 200 targets is used to validate the effectiveness of the designed framework,where three layers cooperate well with each other to generate satisfactory combat scheduling.Comparisons are made in each layer to highlight optimum-seeking capability and efficiency of the proposed algorithms.Works done in this paper provide a simple but efficient solution framework for cross-platform cooperative mission planning problems,and can be potentially extended to other applications such as post-disaster search and rescue,forest surveillance and firefighting,logistics pick and delivery,etc.
基金supported by the Major Projects for Science and Technology Innovation 2030(2018AAA0100805).
文摘To address the confrontation decision-making issues in multi-round air combat,a dynamic game decision method is proposed based on decision tree for the confrontation of unmanned aerial vehicle(UAV)air combat.Based on game the-ory and the confrontation characteristics of air combat,a dynamic game process is constructed including the strategy sets,the situation information,and the maneuver decisions for both sides of air combat.By analyzing the UAV’s flight dyna-mics and the both sides’information,a payment matrix is estab-lished through the situation advantage function,performance advantage function,and profit function.Furthermore,the dynamic game decision problem is solved based on the linear induction method to obtain the Nash equilibrium solution,where the decision tree method is introduced to obtain the optimal maneuver decision,thereby improving the situation advantage in the next round of confrontation.According to the analysis,the simulation results for the confrontation scenarios of multi-round air combat are presented to verify the effectiveness and advan-tages of the proposed method.
基金Project(61203021)supported by the National Natural Science Foundation of ChinaProject(2011216011)supported by the Scientific and Technological Project of Liaoning Province,China+1 种基金Project(2013020024)supported by the Natural Science Foundation of Liaoning Province,ChinaProjects(LJQ2015061,LR2015034)supported by the Program for Liaoning Excellent Talents in University,China
文摘To overcome nonlinear and 6-DOF(degrees of freedom)under-actuated problems for the attitude and position of quadrotor UAVs,an adaptive backstepping sliding mode method for flight attitude of quadrotor UAVs is proposed,in which an adaptive law is designed to online estimate the parameter variations and the upper bound of external disturbances and the assessments is utilized to compensate the backstepping sliding mode control.In addition,the tracking error of the design method is shown to asymptotically converge to zero by using Lyapunov theory.Finally,based on the numerical simulation of quadrotor UAVs using the setting parameters,the results show that the proposed control approach can stabilize the attitude and has hover flight capabilities under the parameter perturbations and external disturbances.
文摘In this paper,based on a bidirectional parallel multi-branch feature pyramid network(BPMFPN),a novel one-stage object detector called BPMFPN Det is proposed for real-time detection of ground multi-scale targets by swarm unmanned aerial vehicles(UAVs).First,the bidirectional parallel multi-branch convolution modules are used to construct the feature pyramid to enhance the feature expression abilities of different scale feature layers.Next,the feature pyramid is integrated into the single-stage object detection framework to ensure real-time performance.In order to validate the effectiveness of the proposed algorithm,experiments are conducted on four datasets.For the PASCAL VOC dataset,the proposed algorithm achieves the mean average precision(mAP)of 85.4 on the VOC 2007 test set.With regard to the detection in optical remote sensing(DIOR)dataset,the proposed algorithm achieves 73.9 mAP.For vehicle detection in aerial imagery(VEDAI)dataset,the detection accuracy of small land vehicle(slv)targets reaches 97.4 mAP.For unmanned aerial vehicle detection and tracking(UAVDT)dataset,the proposed BPMFPN Det achieves the mAP of 48.75.Compared with the previous state-of-the-art methods,the results obtained by the proposed algorithm are more competitive.The experimental results demonstrate that the proposed algorithm can effectively solve the problem of real-time detection of ground multi-scale targets in aerial images of swarm UAVs.
文摘Aiming at the suppression of enemy air defense(SEAD)task under the complex and complicated combat sce-nario,the spatiotemporal cooperative path planning methods are studied in this paper.The major research contents include opti-mal path points generation,path smoothing and cooperative rendezvous.In the path points generation part,the path points availability testing algorithm and the path segments availability testing algorithm are designed,on this foundation,the swarm intelligence-based path point generation algorithm is utilized to generate the optimal path.In the path smoothing part,taking ter-minal attack angle constraint and maneuverability constraint into consideration,the Dubins curve is introduced to smooth the path segments.In cooperative rendezvous part,we take esti-mated time of arrival requirement constraint and flight speed range constraint into consideration,the speed control strategy and flight path control strategy are introduced,further,the decoupling scheme of the circling maneuver and detouring maneuver is designed,in this case,the maneuver ways,maneu-ver point,maneuver times,maneuver path and flight speed are determined.Finally,the simulation experiments are conducted and the acquired results reveal that the time-space cooperation of multiple unmanned aeriel vehicles(UAVs)is effectively real-ized,in this way,the combat situation suppression against the enemy can be realized in SEAD scenarios.
基金supported in part by the National Natural Science Foundation of China under Grant Nos.62173312,61922037,61873115,and 61803348in part by the National Major Scientific Instruments Development Project under Grant 61927807+6 种基金in part by the State Key Laboratory of Deep Buried Target Damage under Grant No.DXMBJJ2019-02in part by the Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi under Grant 2020L0266in part by the Shanxi Province Science Foundation for Youths under Grant No.201701D221123in part by the Youth Academic North University of China under Grant No.QX201803in part by the Program for the Innovative Talents of Higher Education Institutions of Shanxiin part by the Shanxi“1331Project”Key Subjects Construction under Grant 1331KSCin part by the Supported by Shanxi Province Science Foundation for Excellent Youths。
文摘This paper addresses a multicircular circumnavigation control for UAVs with desired angular spacing around a nonstationary target.By defining a coordinated error relative to neighboring angular spacing,under the premise that target information is perfectly accessible by all nodes,a centralized circular enclosing control strategy is derived for multiple UAVs connected by an undirected graph to allow for formation behaviors concerning the moving target.Besides,to avoid the requirement of target’s states being accessible for each UAV,fixed-time distributed observers are introduced to acquire the state estimates in a fixed-time sense,and the upper boundary of settling time can be determined offline irrespective of initial properties,greatly releasing the burdensome communication traffic.Then,with the aid of fixed-time distributed observers,a distributed circular circumnavigation controller is derived to force all UAVs to collaboratively evolve along the preset circles while keeping a desired angular spacing.It is inferred from Lyapunov stability that all errors are demonstrated to be convergent.Simulations are offered to verify the utility of proposed protocol.
基金support from the National Natural Science Foundation of China(No.61773395)。
文摘This paper mainly studies the problem of using UAVs to provide accurate remote target indication for hypersonic projectiles.Based on the optimal trajectory trends and feedback guidance methods,a new cooperative control algorithm is proposed to optimize trajectories of multi-UAVs for target tracking in approaching stage.Based on UAV kinematics and sensor performance models,optimal trajectory trends of UAVs are analyzed theoretically.Then,feedback guidance methods are proposed under the optimal observation trends of UAVs in the approaching target stage,producing trajectories with far less computational complexity and performance very close to the best-known trajectories.Next,the sufficient condition for the UAV to form the optimal observation configuration by the feedback guidance method is presented,which guarantees that the proposed method can optimize the observation trajectory of the UAV in approaching stage.Finally,the feedback guidance method is numerically simulated.Simulation results demonstrate that the estimation performance of the feedback guidance method is superior to the Lyapunov guidance vector field(LGVF)method and verify the effectiveness of the proposed method.Additionally,compared with the receding horizon optimization(RHO)method,the proposed method has the same optimization ability as the RHO method and better real-time performance.
基金National Natural Science Foundation of China(Grant Nos.61803348,62173312,51922009)Shanxi Province Key Laboratory of Quantum Sensing and Precision Measurement(Grant No.201905D121001).
文摘Most researches associated with target encircling control are focused on moving along a circular orbit under an ideal environment free from external disturbances.However,elliptical encirclement with a time-varying observation radius,may permit a more flexible and high-efficacy enclosing solution,whilst the non-orthogonal property between axial and tangential speed components,non-ignorable environmental perturbations,and strict assignment requirements empower elliptical encircling control to be more challenging,and the relevant investigations are still open.Following this line,an appointed-time elliptical encircling control rule capable of reinforcing circumnavigation performances is developed to enable Unmanned Aerial Vehicles(UAVs)to move along a specified elliptical path within a predetermined reaching time.The remarkable merits of the designed strategy are that the relative distance controlling error can be guaranteed to evolve within specified regions with a designer-specified convergence behavior.Meanwhile,wind perturbations can be online counteracted based on an unknown system dynamics estimator(USDE)with only one regulating parameter and high computational efficiency.Lyapunov tool demonstrates that all involved error variables are ultimately limited,and simulations are implemented to confirm the usability of the suggested control algorithm.
基金supported by the National Natural Science Foundation of China(7140104871671059)the National Natural Science Funds of China for Innovative Research Groups(71521001)
文摘This paper studies the problem of using multiple unmanned air vehicles (UAVs) to search for moving targets with sensing capabilities. When multiple UAVs (multi-UAV) search for a number of moving targets in the mission area, the targets can intermittently obtain the position information of the UAVs from sensing devices, and take appropriate actions to increase the distance between themselves and the UAVs. Aiming at this problem, an environment model is established using the search map, and the updating method of the search map is extended by considering the sensing capabilities of the moving targets. A multi-UAV search path planning optimization model based on the model predictive control (MPC) method is constructed, and a hybrid particle swarm optimization algorithm with a crossover operator is designed to solve the model. Simulation results show that the proposed method can effectively improve the cooperative search efficiency and can find more targets per unit time compared with the coverage search method and the random search method.
文摘To ensure multiple unmanned aerial vehicles （UAVs）reach stable formation quickly, a cooperative guidance law basedon the back-stepping-like approach is designed in this paper.Adopting the guidance mechanism of virtue leader vehicle, thedynamic equation of tracking errors for each UAV is built. Thecommunication interactive relationships are described based ongraph theory, and the guidance law for formation reaching is ob-tained by the back-stepping-like approach. The formation stabilityis analyzed by constructing an appropriate Lyapunov function. Thesimulation results have shown that this guidance and control lawcan make each UAV converge to the trajectory of the virtue leaderultimately, and has the quicker rate of convergence and lowertracking error.
基金supported by the National Natural Science Foundation of China(60974146)the Natural Science and Engineering Research Council of Canada(NSERC)
文摘Based on multiple unmanned aerial vehicles（UAVs） flight at a constant altitude,a fault-tolerant cooperative localization algorithm against global positioning system（GPS） signal loss due to GPS receiver malfunction is proposed.Contrast to the traditional means with single UAV,the proposed method is based on the use of inter-UAV relative range measurements against GPS signal loss and more suitable for the small-size and low-cost UAV applications.Firstly,for re-localizing an UAV with a malfunction in its GPS receiver,an algorithm which makes use of any other three healthy UAVs in the cooperative flight as the reference points for re-localization is proposed.Secondly,by using the relative ranges from the faulty UAV to the other three UAVs,its horizontal location can be determined after the GPS signal is lost.In order to improve an accuracy of the localization,a Kalman filter is further exploited to provide the estimated location of the UAV with the GPS signal loss.The Kalman filter calculates the variance of observations in terms of horizontal dilution of positioning（HDOP） automatically.Then,during each discrete computing time step,the best reference points are selected adaptively by minimizing the HDOP.Finally,two simulation examples in Matlab/Simulink environment with five UAVs in cooperative flight are shown to evaluate the effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China(61673327)the Industrial Development and Foster Project of Yangtze River Delta Research Institute of NPU,Taicang(CY20210202)+1 种基金the Fundamental Research Funds for the Central Universities(G2021KY05116,G2022WD01026)the Basic Research Programs of Taicang(TC2021JC28)。
文摘This paper focuses on the solution to the dynamic affine formation control problem for multiple networked underactuated quad-rotor unmanned aerial vehicles(UAVs)to achieve a configuration that preserves collinearity and ratios of distances for a target configuration.In particular,it is investigated that the quad-rotor UAVs are steered to track a reference linear velocity while maintaining a desired three-dimensional target formation.Firstly,by integrating the properties of the affine transformation and the stress matrix,the design of the target formation is convenient and applicable for various three-dimensional geometric patterns.Secondly,a distributed control method is proposed under a hierarchical framework.By introducing an intermediary control input for each quad-rotor UAV in the position loop,the necessary thrust input and the desired attitude are extracted.In the attitude loop,the desired attitude represented by the unit quaternion is tracked by the designed torque input.Both conditions of linear velocity unavailability and mutual collision avoidance are also tackled.In terms of Lyapunov theory,it is prooved that the overall closed-loop error system is asymptotically stable.Finally,two illustrative examples are simulated to validate the effectiveness of the proposed theoretical results.
文摘在林业管理中,及时发现火灾并识别其规模对于安全防护和治理火灾至关重要。针对现有火灾检测算法存在的精度低、漏检误检和实时性不足等问题,提出一种无人机航拍图像下火灾实时检测算法——MDSYOLOv8。以YOLOv8为基线算法,将骨干网络第7层卷积模块和颈部网络卷积模块替换成动态蛇形卷积(DSConv),提高算法的特征提取性能,并强化算法对微小特征的学习能力;然后在颈部与检测头之间添加多维协作注意力机制(MCA),加强颈部特征融合,增强算法对小目标的检测能力,并抑制无关背景信息;最后使用SIoU损失函数替换原YOLOv8中的CIoU损失函数,加快算法的收敛速度和回归精度。实验结果表明,MDSYOLOv8在公开数据集KMU上对烟雾目标的检测精度mAP达到95.89%,相较于基线YOLOv8提高了3.33个百分点,具有卓越的检测性能。此外,本研究采集互联网上的无人机航拍火灾图像制作UFF(UAV field fire)数据集,主要对象为火焰和烟雾,包含森林和城市等火灾隐患可能发生场景。在自制数据集UFF上进行深度实验分析,MDSYOLOv8的检测精度达到93.98%,检测速度为54帧/s,并且能同时识别烟雾和火焰两种火灾场景中的主要目标,与主流目标检测方法相比,在检测精度和效率方面均展现出明显优势,更加契合航拍场景下的火灾检测应用。
