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
Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequent...Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequential Convex Programming(SFC-SCP)to improve the computation efficiency and reliability of trajectory generation.SFC-SCP combines the front-end convex polyhedron SFC construction and back-end SCP-based trajectory optimization.A Sparse A^(*)Search(SAS)driven SFC construction method is designed to efficiently generate polyhedron SFC according to the geometric relation among obstacles and collision-free waypoints.Via transforming the nonconvex obstacle-avoidance constraints to linear inequality constraints,SFC can mitigate infeasibility of trajectory planning and reduce computation complexity.Then,SCP casts the nonlinear trajectory optimization subject to SFC into convex programming subproblems to decrease the problem complexity.In addition,a convex optimizer based on interior point method is customized,where the search direction is calculated via successive elimination to further improve efficiency.Simulation experiments on dense obstacle scenarios show that SFC-SCP can generate dynamically feasible safe trajectory rapidly.Comparative studies with state-of-the-art SCP-based methods demonstrate the efficiency and reliability merits of SFC-SCP.Besides,the customized convex optimizer outperforms off-the-shelf optimizers in terms of computation time.展开更多
Cooperative path planning is an important area in fixed-wing UAV swarm.However,avoiding multiple timevarying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic environment.Fir...Cooperative path planning is an important area in fixed-wing UAV swarm.However,avoiding multiple timevarying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic environment.Firstly,a normalized artificial potential field optimization is proposed by reconstructing a novel function with anisotropy in each dimension,which can make the flight speed of a fixed UAV swarm independent of the repulsive/attractive gain coefficient and avoid trapping into local optimization and local oscillation.Then,taking into account minimum velocity and turning angular velocity of fixed-wing UAV swarm,a strategy of decomposing target vector to avoid moving obstacles and pop-up threats is proposed.Finally,several simulations are carried out to illustrate superiority and effectiveness.展开更多
For the automatic tracking of unknown moving targets on the ground,most of the commonly used methods involve circling above the target.With such a tracking mode,there is a moving laser spot on the target,which will br...For the automatic tracking of unknown moving targets on the ground,most of the commonly used methods involve circling above the target.With such a tracking mode,there is a moving laser spot on the target,which will bring trouble for cooperative manned helicopters.In this paper,we propose a new way of tracking,where an unmanned aerial vehicle(UAV) circles on one side of the tracked target.A circular path algorithm is developed for monitoring the relative position between the UAV and the target considering the real-time range and the bearing angle.This can determine the center of the new circular path if the predicted range between the UAV and the target does not meet the monitoring requirements.A transition path algorithm is presented for planning the transition path between circular paths that constrain the turning radius of the UAV.The transition path algorithm can generate waypoints that meet the flight ability.In this paper,we analyze the entire method and detail the scope of applications.We formulate an observation angle as an evaluation index.A series of simulations and evaluation index comparisons verify the effectiveness of the proposed algorithms.展开更多
This paper tackles the formation-containment control problem of fixed-wing unmanned aerial vehicle(UAV)swarm with model uncertainties for dynamic target tracking in three-dimensional space in the faulty case of UAVs’...This paper tackles the formation-containment control problem of fixed-wing unmanned aerial vehicle(UAV)swarm with model uncertainties for dynamic target tracking in three-dimensional space in the faulty case of UAVs’actuator and sensor.The fixed-wing UAV swarm under consideration is organized as a“multi-leader-multi-follower”structure,in which only several leaders can obtain the dynamic target information while others only receive the neighbors’information through the communication network.To simultaneously realize the formation,containment,and dynamic target tracking,a two-layer control framework is adopted to decouple the problem into two subproblems:reference trajectory generation and trajectory tracking.In the upper layer,a distributed finite-time estimator(DFTE)is proposed to generate each UAV’s reference trajectory in accordance with the control objective.Subsequently,a distributed composite robust fault-tolerant trajectory tracking controller is developed in the lower layer,where a novel adaptive extended super-twisting(AESTW)algorithm with a finite-time extended state observer(FTESO)is involved in solving the robust trajectory tracking control problem under model uncertainties,actuator,and sensor faults.The proposed controller simultaneously guarantees rapidness and enhances the system’s robustness with fewer chattering effects.Finally,corresponding simulations are carried out to demonstrate the effectiveness and competitiveness of the proposed two-layer fault-tolerant cooperative control scheme.展开更多
This paper discusses the design and software-in-theloop implementation of adaptive formation controllers for fixedwing unmanned aerial vehicles(UAVs) with parametric uncertainty in their structure, namely uncertain ma...This paper discusses the design and software-in-theloop implementation of adaptive formation controllers for fixedwing unmanned aerial vehicles(UAVs) with parametric uncertainty in their structure, namely uncertain mass and inertia. In fact, when aiming at autonomous flight, such parameters cannot assumed to be known as they might vary during the mission(e.g.depending on the payload). Modeling and autopilot design for such autonomous fixed-wing UAVs are presented. The modeling is implemented in Matlab, while the autopilot is based on ArduPilot, a popular open-source autopilot suite. Specifically, the ArduP ilot functionalities are emulated in Matlab according to the Ardupilot documentation and code, which allows us to perform software-in-the-loop simulations of teams of UAVs embedded with actual autopilot protocols. An overview of realtime path planning, trajectory tracking and formation control resulting from the proposed platform is given. The software-inthe-loop simulations show the capability of achieving different UAV formations while handling uncertain mass and inertia.展开更多
To overcome the problems encountered in predicting the endurance of electricpowered fixed-wing unmanned aerial vehicles(UAVs),which were stemmed from the dynamic changes in electric power system efficiency and battery...To overcome the problems encountered in predicting the endurance of electricpowered fixed-wing unmanned aerial vehicles(UAVs),which were stemmed from the dynamic changes in electric power system efficiency and battery discharge characteristics under different operating conditions,the required battery power model and battery discharge model were studied.The required battery power model was determined using an approximate model of electric power system efficiency based on wind tunnel testing and the self-adaptive penalty function.Furthermore,current correction and ambient temperature correction terms were proposed for the trained Kriging model representing the discharge characteristics under standard operation,and then the discharged capacity-terminal voltage model was established.Through numerical integration of this model with the required battery power model,the electric-powered fixed-wing UAV endurance prediction model was obtained.Laboratory tests indicated that the proposed endurance model could precisely calculate the battery discharge time and accurately describe the battery discharge process.The similarity of the theoretical and flight test results reflected the accuracy of the proposed endurance model as well as the importance of considering dynamic changes in power system efficiency in endurance calculations.The proposed endurance model meeting precision requirements can be used in practical engineering applications.展开更多
This paper studies the problem of tracking a ground target for a fixed-wing unmanned aerial vehicle(UAV) based on the proposed guidance law. The algorithm ensures that a UAV continuously overflies the target whether i...This paper studies the problem of tracking a ground target for a fixed-wing unmanned aerial vehicle(UAV) based on the proposed guidance law. The algorithm ensures that a UAV continuously overflies the target whether it is fixed or moving. The requirements of the UAV flight constraints such as bounded airspeed and acceleration are considered. A Lyapunov function is constructed to prove the stability of the proposed guidance law,and parameter design criteria have been developed. Considering the fixed and moving ground targets, numerical simulations are performed to verify the feasibility and benefits of the proposed guidance algorithm.展开更多
With the development of Unmanned Aerial Vehicles(UAVs), the applications of UAVs have been extensively explored. In the field of wireless communications, the relay nodes are often used to extend network coverage. Howe...With the development of Unmanned Aerial Vehicles(UAVs), the applications of UAVs have been extensively explored. In the field of wireless communications, the relay nodes are often used to extend network coverage. However, traditional fixed ground relays cannot be flexibly deployed due to their low heights and fixed locations. Hence, deploying UAV as relay node is a promising solution and has become a research hotspot. In this paper, we consider an UAVenabled relaying network in which a fixed-wing UAV is deployed between the Base Station(BS)and Ground Users(GUs). We study the energy-efficiency gap between the link “BS-UAV-GUs”and the link “BS-GUs”, and jointly optimize UAV relay transmission power and flight radius to achieve the highest energy-efficiency. Firstly, the UAV/BS-GUs channels models and the UAV energy consumption model are built. Secondly, the optimization objective function is formulated to maximize the energy-efficiency gap. Then, the solution of the optimization problem is divided into a two-step iteration process, in which the UAV relay transmission power and flight radius are adjusted to maximize the energy-efficiency gap. Finally, the experimental results under different simulation scenarios(such as cities, forests, deserts, oceans, etc.) are shown to illustrate the effectiveness of the proposed algorithm. The results show that the proposed algorithm can always find the optimal UAV relay transmission power and flight radius settings, and achieve the largest energy-efficiency gap. The convergency speed of the proposed algorithm is fast, and can obtain the optimal solution within only a few iterations.展开更多
In this paper.Active Disturbance Rejection Control(ADRC)is utilized in the pitch control of a vertical take-off and landing fixed-wing Unmanned Aerial Vehicle(UAV)to address the problem of height fluctuation during th...In this paper.Active Disturbance Rejection Control(ADRC)is utilized in the pitch control of a vertical take-off and landing fixed-wing Unmanned Aerial Vehicle(UAV)to address the problem of height fluctuation during the transition from hover to level flight.Considering the difficulty of parameter tuning of ADRC as well as the requirement of accuracy and rapidity of the controller,a Multi-Strategy Pigeon-Inspired Optimization(MSPIO)algorithm is employed.Particle Swarm Optimization(PSO),Genetic Algorithm(GA),the basic Pigeon-Inspired Optimization(PIO),and an improved PIO algorithm CMPIO are compared.In addition,the optimized ADRC control system is compared with the pure Proportional-Integral-Derivative(PID)control system and the non-optimized ADRC control system.The effectiveness of the designed control strategy for forward transition is verified and the faster convergence speed and better exploitation ability of the proposed MSPIO algorithm are confirmed by simulation results.展开更多
This paper introduces a fault-tolerant control(FTC)design for a faulty fixed-wing unmanned aerial vehicle(UAV).To constrain tracking errors against actuator faults,error constraint inequalities are first transformed t...This paper introduces a fault-tolerant control(FTC)design for a faulty fixed-wing unmanned aerial vehicle(UAV).To constrain tracking errors against actuator faults,error constraint inequalities are first transformed to a new set of variables based on prescribed performance functions.Then,the commonly used and powerful proportional-integral-derivative(PID)control concept is employed to filter the transformed error variables.To handle the fault-induced nonlinear terms,a composite learning algorithm consisting of neural network and disturbance observer is incorporated for increasing flight safety.It is shown by Lyapunov stability analysis that the tracking errors are strictly constrained within the specified error bounds.Experimental results are presented to verify the feasibility of the developed FTC scheme.展开更多
There are fundamental performance compromises between rotary-wing and fixed-wing UAVs. The general solution to address this well-known problem is the design of a platform with some degree of reconfigurable airframes. ...There are fundamental performance compromises between rotary-wing and fixed-wing UAVs. The general solution to address this well-known problem is the design of a platform with some degree of reconfigurable airframes. For critical missions (civilian or military), it is imperative that mechanical complexity is kept to a minimum to help achieve mission success. This work proposes that the tried-and-true radio controlled (RC) aerobatic airplanes can be implemented as basis for fixed-wing UAVs having both speed and vertical takeoff and landing (VTOL) capabilities. These powerful and highly maneuverable airplanes have non-rotatable nacelles, yet capable of deep stall maneuvers. The power requirements for VTOL and level flight of an aerobatic RC airplane are evaluated and they are compared to those of a RC helicopter of similar flying weight. This work provides quantitative validation that commercially available RC aerobatic airplanes can serve as platform to build VTOL capable fixed-wing UAVs that are agile, cost effective, reliable and easy maintenance.展开更多
Four-dimensional trajectory based operation(4D-TBO)is believed to enhance the planning and execution of efficient flights,reduce potential conflicts and resolve upcoming tremendous flight demand.Most of the 4D traject...Four-dimensional trajectory based operation(4D-TBO)is believed to enhance the planning and execution of efficient flights,reduce potential conflicts and resolve upcoming tremendous flight demand.Most of the 4D trajectory planning related studies have focused on manned aircraft instead of unmanned aerial vehicles(UAVs).This paper focuses on planning conflict-free 4D trajectories for fixed-wing UAVs before the departure or during the flight planning.A 4D trajectory generation technique based on Tau theory is developed,which can incorporate the time constraints over the waypoint sequence in the flight plan.Then the 4D trajectory is optimized by the particle swarm optimization(PSO)algorithm.Further simulations are performed to demonstrate the effectiveness of the proposed method,which would offer a good chance for integrating UAV into civil airspace in the future.展开更多
Unmanned Aerial Vehicle(UAV)swarm collaboration enhances mission effectiveness.However,fixed-wing UAV swarm flights face collaborative safety control problems within a limited airspace in complex environments.Aimed at...Unmanned Aerial Vehicle(UAV)swarm collaboration enhances mission effectiveness.However,fixed-wing UAV swarm flights face collaborative safety control problems within a limited airspace in complex environments.Aimed at the cooperative control problem of fixed-wing UAV swarm flights under the airspace constraints of a virtual tube in a complex environment,this paper proposes a behavior-based distributed control method for fixed-wing UAV swarm considering flight safety constraints.Considering the fixed-wing UAV swarm flight problem in complex environment,a virtual tube model based on generator curve is established.The tube keeping,centerline tracking and flight safety behavioral control strategies of the UAV swarm are designed to ensure that the UAV swarm flies along the inside of the virtual tube safety and does not go beyond its boundary.On this basis,a maneuvering decision-making method based on behavioral fusion is proposed to ensure the safe flight of UAV swarm in the restricted airspace.This cooperative control method eliminates the need for respective pre-planned trajectories,reduces communication requirements,and achieves a high level of intelligence.Simulation results show that the proposed behaviorbased UAV swarm cooperative control method is able to make the fixed-wing UAV swarm,which is faster and unable to hover,fly along the virtual tube airspace under various virtual tube shapes and different swarm sizes,and the spacing between the UAVs is larger than the minimum safe distance during the flight.展开更多
This paper presents a control strategy for multiple unmanned aerial vehicle systems(multi-UAVs)time-coordinated path following with desired endpoint roll attitudes.It utilizes the strong maneuvering capabilities of ag...This paper presents a control strategy for multiple unmanned aerial vehicle systems(multi-UAVs)time-coordinated path following with desired endpoint roll attitudes.It utilizes the strong maneuvering capabilities of agile fixed-wing UAVs and incorporates an end-roll expectation.The strategy consists of four steps:time-coordinated control,position control,roll angle planning and attitude control.The position and attitude controllers exhibit Lyapunov exponential stability.The time-coordinated controller addresses the synchronization problem by adjusting the speed based on the coordinated state to achieve progress adjustment.The position controller operates based on the cross-track error and altitude error in the Gravity-Referenced Moving frame.By employing an optimization approach and designing a penalty function,the roll angle sequence is computed.The attitude inner-loop control operates in the SO(3)space and allows for control of large deviations.High-fidelity simulation validates the effectiveness of the proposed method,with normalized coordination error and following error controlled within 2%and 1.2m.展开更多
Unmanned Aerial Vehicles(UAVs)have become integral components in smart city infrastructures,supporting applications such as emergency response,surveillance,and data collection.However,the high mobility and dynamic top...Unmanned Aerial Vehicles(UAVs)have become integral components in smart city infrastructures,supporting applications such as emergency response,surveillance,and data collection.However,the high mobility and dynamic topology of Flying Ad Hoc Networks(FANETs)present significant challenges for maintaining reliable,low-latency communication.Conventional geographic routing protocols often struggle in situations where link quality varies and mobility patterns are unpredictable.To overcome these limitations,this paper proposes an improved routing protocol based on reinforcement learning.This new approach integrates Q-learning with mechanisms that are both link-aware and mobility-aware.The proposed method optimizes the selection of relay nodes by using an adaptive reward function that takes into account energy consumption,delay,and link quality.Additionally,a Kalman filter is integrated to predict UAV mobility,improving the stability of communication links under dynamic network conditions.Simulation experiments were conducted using realistic scenarios,varying the number of UAVs to assess scalability.An analysis was conducted on key performance metrics,including the packet delivery ratio,end-to-end delay,and total energy consumption.The results demonstrate that the proposed approach significantly improves the packet delivery ratio by 12%–15%and reduces delay by up to 25.5%when compared to conventional GEO and QGEO protocols.However,this improvement comes at the cost of higher energy consumption due to additional computations and control overhead.Despite this trade-off,the proposed solution ensures reliable and efficient communication,making it well-suited for large-scale UAV networks operating in complex urban environments.展开更多
This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential g...This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential growth of mobile devices and data traffic has substantially increased network congestion,particularly in urban areas and regions with limited terrestrial infrastructure.Our approach jointly optimizes unmanned aerial vehicle(UAV)trajectories and satellite-assisted offloading strategies to simultaneously maximize data throughput,minimize energy consumption,and maintain equitable resource distribution.The proposed RMAPPO framework incorporates recurrent neural networks(RNNs)to model temporal dependencies in UAV mobility patterns and utilizes a decentralized multi-agent reinforcement learning architecture to reduce communication overhead while improving system robustness.The proposed RMAPPO algorithm was evaluated through simulation experiments,with the results indicating that it significantly enhances the cumulative traffic offloading rate of nodes and reduces the energy consumption of UAVs.展开更多
Fixed-wing unmanned aerial vehicles(UAVs)are a primary focus of current UAV research.Challenges arise in theirflight due to high speed and complex maneuverability.This paper explores the coordinated turn guidance law ...Fixed-wing unmanned aerial vehicles(UAVs)are a primary focus of current UAV research.Challenges arise in theirflight due to high speed and complex maneuverability.This paper explores the coordinated turn guidance law for fixed-wing UAVs and validates an experimental leader-follower formation platform inflight.Results demonstrate the effectiveness of the proposed algorithm and platform in enabling actual leader-follower formationflights for fixed-wing UAVs.展开更多
With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods ...With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.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 National Natural Science Foundation of China(No.62203256)。
文摘Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequential Convex Programming(SFC-SCP)to improve the computation efficiency and reliability of trajectory generation.SFC-SCP combines the front-end convex polyhedron SFC construction and back-end SCP-based trajectory optimization.A Sparse A^(*)Search(SAS)driven SFC construction method is designed to efficiently generate polyhedron SFC according to the geometric relation among obstacles and collision-free waypoints.Via transforming the nonconvex obstacle-avoidance constraints to linear inequality constraints,SFC can mitigate infeasibility of trajectory planning and reduce computation complexity.Then,SCP casts the nonlinear trajectory optimization subject to SFC into convex programming subproblems to decrease the problem complexity.In addition,a convex optimizer based on interior point method is customized,where the search direction is calculated via successive elimination to further improve efficiency.Simulation experiments on dense obstacle scenarios show that SFC-SCP can generate dynamically feasible safe trajectory rapidly.Comparative studies with state-of-the-art SCP-based methods demonstrate the efficiency and reliability merits of SFC-SCP.Besides,the customized convex optimizer outperforms off-the-shelf optimizers in terms of computation time.
文摘Cooperative path planning is an important area in fixed-wing UAV swarm.However,avoiding multiple timevarying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic environment.Firstly,a normalized artificial potential field optimization is proposed by reconstructing a novel function with anisotropy in each dimension,which can make the flight speed of a fixed UAV swarm independent of the repulsive/attractive gain coefficient and avoid trapping into local optimization and local oscillation.Then,taking into account minimum velocity and turning angular velocity of fixed-wing UAV swarm,a strategy of decomposing target vector to avoid moving obstacles and pop-up threats is proposed.Finally,several simulations are carried out to illustrate superiority and effectiveness.
基金the Deanship of Scientific Research at King Saud University through research group number(RG-1440-048)。
文摘For the automatic tracking of unknown moving targets on the ground,most of the commonly used methods involve circling above the target.With such a tracking mode,there is a moving laser spot on the target,which will bring trouble for cooperative manned helicopters.In this paper,we propose a new way of tracking,where an unmanned aerial vehicle(UAV) circles on one side of the tracked target.A circular path algorithm is developed for monitoring the relative position between the UAV and the target considering the real-time range and the bearing angle.This can determine the center of the new circular path if the predicted range between the UAV and the target does not meet the monitoring requirements.A transition path algorithm is presented for planning the transition path between circular paths that constrain the turning radius of the UAV.The transition path algorithm can generate waypoints that meet the flight ability.In this paper,we analyze the entire method and detail the scope of applications.We formulate an observation angle as an evaluation index.A series of simulations and evaluation index comparisons verify the effectiveness of the proposed algorithms.
基金the National Natural Science Foundation of China(61933010)the Natural Science Basic Research Plan in Shaanxi Province of China(2023-JC-QN-0733).
文摘This paper tackles the formation-containment control problem of fixed-wing unmanned aerial vehicle(UAV)swarm with model uncertainties for dynamic target tracking in three-dimensional space in the faulty case of UAVs’actuator and sensor.The fixed-wing UAV swarm under consideration is organized as a“multi-leader-multi-follower”structure,in which only several leaders can obtain the dynamic target information while others only receive the neighbors’information through the communication network.To simultaneously realize the formation,containment,and dynamic target tracking,a two-layer control framework is adopted to decouple the problem into two subproblems:reference trajectory generation and trajectory tracking.In the upper layer,a distributed finite-time estimator(DFTE)is proposed to generate each UAV’s reference trajectory in accordance with the control objective.Subsequently,a distributed composite robust fault-tolerant trajectory tracking controller is developed in the lower layer,where a novel adaptive extended super-twisting(AESTW)algorithm with a finite-time extended state observer(FTESO)is involved in solving the robust trajectory tracking control problem under model uncertainties,actuator,and sensor faults.The proposed controller simultaneously guarantees rapidness and enhances the system’s robustness with fewer chattering effects.Finally,corresponding simulations are carried out to demonstrate the effectiveness and competitiveness of the proposed two-layer fault-tolerant cooperative control scheme.
基金supported by the Fundamental Research Funds for the Central Universities(4007019109)(RECON-STRUCT)the Special Guiding Funds for Double First-class(4007019201)the Joint TU Delft-CSSC Project ‘Multi-agent Coordination with Networked Constraints’(MULTI-COORD)
文摘This paper discusses the design and software-in-theloop implementation of adaptive formation controllers for fixedwing unmanned aerial vehicles(UAVs) with parametric uncertainty in their structure, namely uncertain mass and inertia. In fact, when aiming at autonomous flight, such parameters cannot assumed to be known as they might vary during the mission(e.g.depending on the payload). Modeling and autopilot design for such autonomous fixed-wing UAVs are presented. The modeling is implemented in Matlab, while the autopilot is based on ArduPilot, a popular open-source autopilot suite. Specifically, the ArduP ilot functionalities are emulated in Matlab according to the Ardupilot documentation and code, which allows us to perform software-in-the-loop simulations of teams of UAVs embedded with actual autopilot protocols. An overview of realtime path planning, trajectory tracking and formation control resulting from the proposed platform is given. The software-inthe-loop simulations show the capability of achieving different UAV formations while handling uncertain mass and inertia.
文摘To overcome the problems encountered in predicting the endurance of electricpowered fixed-wing unmanned aerial vehicles(UAVs),which were stemmed from the dynamic changes in electric power system efficiency and battery discharge characteristics under different operating conditions,the required battery power model and battery discharge model were studied.The required battery power model was determined using an approximate model of electric power system efficiency based on wind tunnel testing and the self-adaptive penalty function.Furthermore,current correction and ambient temperature correction terms were proposed for the trained Kriging model representing the discharge characteristics under standard operation,and then the discharged capacity-terminal voltage model was established.Through numerical integration of this model with the required battery power model,the electric-powered fixed-wing UAV endurance prediction model was obtained.Laboratory tests indicated that the proposed endurance model could precisely calculate the battery discharge time and accurately describe the battery discharge process.The similarity of the theoretical and flight test results reflected the accuracy of the proposed endurance model as well as the importance of considering dynamic changes in power system efficiency in endurance calculations.The proposed endurance model meeting precision requirements can be used in practical engineering applications.
基金supported by the Aeronautical Science Foundation of China(20160152001)
文摘This paper studies the problem of tracking a ground target for a fixed-wing unmanned aerial vehicle(UAV) based on the proposed guidance law. The algorithm ensures that a UAV continuously overflies the target whether it is fixed or moving. The requirements of the UAV flight constraints such as bounded airspeed and acceleration are considered. A Lyapunov function is constructed to prove the stability of the proposed guidance law,and parameter design criteria have been developed. Considering the fixed and moving ground targets, numerical simulations are performed to verify the feasibility and benefits of the proposed guidance algorithm.
基金supported in part by Shanghai Rising-Star Program(No.19QA1409100)in part by the National Natural Science Foundation of China(Nos.62071332,61631017 and U1733114)+1 种基金in part by the Fundamental Research Funds for the Central Universities,China。
文摘With the development of Unmanned Aerial Vehicles(UAVs), the applications of UAVs have been extensively explored. In the field of wireless communications, the relay nodes are often used to extend network coverage. However, traditional fixed ground relays cannot be flexibly deployed due to their low heights and fixed locations. Hence, deploying UAV as relay node is a promising solution and has become a research hotspot. In this paper, we consider an UAVenabled relaying network in which a fixed-wing UAV is deployed between the Base Station(BS)and Ground Users(GUs). We study the energy-efficiency gap between the link “BS-UAV-GUs”and the link “BS-GUs”, and jointly optimize UAV relay transmission power and flight radius to achieve the highest energy-efficiency. Firstly, the UAV/BS-GUs channels models and the UAV energy consumption model are built. Secondly, the optimization objective function is formulated to maximize the energy-efficiency gap. Then, the solution of the optimization problem is divided into a two-step iteration process, in which the UAV relay transmission power and flight radius are adjusted to maximize the energy-efficiency gap. Finally, the experimental results under different simulation scenarios(such as cities, forests, deserts, oceans, etc.) are shown to illustrate the effectiveness of the proposed algorithm. The results show that the proposed algorithm can always find the optimal UAV relay transmission power and flight radius settings, and achieve the largest energy-efficiency gap. The convergency speed of the proposed algorithm is fast, and can obtain the optimal solution within only a few iterations.
基金supported by Science and Technology Innovation 2030-Key Project of"New Generation Artificial Intelli-gence",China(No.2018AAA0100803)National Natural Science Foundation of China(Nos.U20B2071,91948204,U1913602)Aeronautical Foundation of China(No.20185851022).
文摘In this paper.Active Disturbance Rejection Control(ADRC)is utilized in the pitch control of a vertical take-off and landing fixed-wing Unmanned Aerial Vehicle(UAV)to address the problem of height fluctuation during the transition from hover to level flight.Considering the difficulty of parameter tuning of ADRC as well as the requirement of accuracy and rapidity of the controller,a Multi-Strategy Pigeon-Inspired Optimization(MSPIO)algorithm is employed.Particle Swarm Optimization(PSO),Genetic Algorithm(GA),the basic Pigeon-Inspired Optimization(PIO),and an improved PIO algorithm CMPIO are compared.In addition,the optimized ADRC control system is compared with the pure Proportional-Integral-Derivative(PID)control system and the non-optimized ADRC control system.The effectiveness of the designed control strategy for forward transition is verified and the faster convergence speed and better exploitation ability of the proposed MSPIO algorithm are confirmed by simulation results.
基金This work was supported by the National Natural Science Foundation of China(62003162,61833013,62020106003)the Natural Science Foundation of Jiangsu Province of China(BK20200416)+3 种基金the China Postdoctoral Science Foundation(2020TQ0151,2020M681590)the State Key Laboratory of Synthetical Automation for Process Industries,Northeastern University(2019-KF-23-05)the 111 Project(B20007)the Natural Sciences and Engineering Research Council of Canada.
文摘This paper introduces a fault-tolerant control(FTC)design for a faulty fixed-wing unmanned aerial vehicle(UAV).To constrain tracking errors against actuator faults,error constraint inequalities are first transformed to a new set of variables based on prescribed performance functions.Then,the commonly used and powerful proportional-integral-derivative(PID)control concept is employed to filter the transformed error variables.To handle the fault-induced nonlinear terms,a composite learning algorithm consisting of neural network and disturbance observer is incorporated for increasing flight safety.It is shown by Lyapunov stability analysis that the tracking errors are strictly constrained within the specified error bounds.Experimental results are presented to verify the feasibility of the developed FTC scheme.
文摘There are fundamental performance compromises between rotary-wing and fixed-wing UAVs. The general solution to address this well-known problem is the design of a platform with some degree of reconfigurable airframes. For critical missions (civilian or military), it is imperative that mechanical complexity is kept to a minimum to help achieve mission success. This work proposes that the tried-and-true radio controlled (RC) aerobatic airplanes can be implemented as basis for fixed-wing UAVs having both speed and vertical takeoff and landing (VTOL) capabilities. These powerful and highly maneuverable airplanes have non-rotatable nacelles, yet capable of deep stall maneuvers. The power requirements for VTOL and level flight of an aerobatic RC airplane are evaluated and they are compared to those of a RC helicopter of similar flying weight. This work provides quantitative validation that commercially available RC aerobatic airplanes can serve as platform to build VTOL capable fixed-wing UAVs that are agile, cost effective, reliable and easy maintenance.
文摘Four-dimensional trajectory based operation(4D-TBO)is believed to enhance the planning and execution of efficient flights,reduce potential conflicts and resolve upcoming tremendous flight demand.Most of the 4D trajectory planning related studies have focused on manned aircraft instead of unmanned aerial vehicles(UAVs).This paper focuses on planning conflict-free 4D trajectories for fixed-wing UAVs before the departure or during the flight planning.A 4D trajectory generation technique based on Tau theory is developed,which can incorporate the time constraints over the waypoint sequence in the flight plan.Then the 4D trajectory is optimized by the particle swarm optimization(PSO)algorithm.Further simulations are performed to demonstrate the effectiveness of the proposed method,which would offer a good chance for integrating UAV into civil airspace in the future.
基金co-supported by the National Natural Science Foundation of China(No.61903350)the Ministry of Education industry-university-research innovation project,China(No.2021ZYA02002)the Beijing Institute of Technology Research Fund Program for Young Scholars,China(No.3010011182130)。
文摘Unmanned Aerial Vehicle(UAV)swarm collaboration enhances mission effectiveness.However,fixed-wing UAV swarm flights face collaborative safety control problems within a limited airspace in complex environments.Aimed at the cooperative control problem of fixed-wing UAV swarm flights under the airspace constraints of a virtual tube in a complex environment,this paper proposes a behavior-based distributed control method for fixed-wing UAV swarm considering flight safety constraints.Considering the fixed-wing UAV swarm flight problem in complex environment,a virtual tube model based on generator curve is established.The tube keeping,centerline tracking and flight safety behavioral control strategies of the UAV swarm are designed to ensure that the UAV swarm flies along the inside of the virtual tube safety and does not go beyond its boundary.On this basis,a maneuvering decision-making method based on behavioral fusion is proposed to ensure the safe flight of UAV swarm in the restricted airspace.This cooperative control method eliminates the need for respective pre-planned trajectories,reduces communication requirements,and achieves a high level of intelligence.Simulation results show that the proposed behaviorbased UAV swarm cooperative control method is able to make the fixed-wing UAV swarm,which is faster and unable to hover,fly along the virtual tube airspace under various virtual tube shapes and different swarm sizes,and the spacing between the UAVs is larger than the minimum safe distance during the flight.
基金supported in part by the National Natural Science Foundation of China under Grant No.61876187.
文摘This paper presents a control strategy for multiple unmanned aerial vehicle systems(multi-UAVs)time-coordinated path following with desired endpoint roll attitudes.It utilizes the strong maneuvering capabilities of agile fixed-wing UAVs and incorporates an end-roll expectation.The strategy consists of four steps:time-coordinated control,position control,roll angle planning and attitude control.The position and attitude controllers exhibit Lyapunov exponential stability.The time-coordinated controller addresses the synchronization problem by adjusting the speed based on the coordinated state to achieve progress adjustment.The position controller operates based on the cross-track error and altitude error in the Gravity-Referenced Moving frame.By employing an optimization approach and designing a penalty function,the roll angle sequence is computed.The attitude inner-loop control operates in the SO(3)space and allows for control of large deviations.High-fidelity simulation validates the effectiveness of the proposed method,with normalized coordination error and following error controlled within 2%and 1.2m.
基金funded by Hung Yen University of Technology and Education under grand number UTEHY.L.2025.62.
文摘Unmanned Aerial Vehicles(UAVs)have become integral components in smart city infrastructures,supporting applications such as emergency response,surveillance,and data collection.However,the high mobility and dynamic topology of Flying Ad Hoc Networks(FANETs)present significant challenges for maintaining reliable,low-latency communication.Conventional geographic routing protocols often struggle in situations where link quality varies and mobility patterns are unpredictable.To overcome these limitations,this paper proposes an improved routing protocol based on reinforcement learning.This new approach integrates Q-learning with mechanisms that are both link-aware and mobility-aware.The proposed method optimizes the selection of relay nodes by using an adaptive reward function that takes into account energy consumption,delay,and link quality.Additionally,a Kalman filter is integrated to predict UAV mobility,improving the stability of communication links under dynamic network conditions.Simulation experiments were conducted using realistic scenarios,varying the number of UAVs to assess scalability.An analysis was conducted on key performance metrics,including the packet delivery ratio,end-to-end delay,and total energy consumption.The results demonstrate that the proposed approach significantly improves the packet delivery ratio by 12%–15%and reduces delay by up to 25.5%when compared to conventional GEO and QGEO protocols.However,this improvement comes at the cost of higher energy consumption due to additional computations and control overhead.Despite this trade-off,the proposed solution ensures reliable and efficient communication,making it well-suited for large-scale UAV networks operating in complex urban environments.
文摘This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential growth of mobile devices and data traffic has substantially increased network congestion,particularly in urban areas and regions with limited terrestrial infrastructure.Our approach jointly optimizes unmanned aerial vehicle(UAV)trajectories and satellite-assisted offloading strategies to simultaneously maximize data throughput,minimize energy consumption,and maintain equitable resource distribution.The proposed RMAPPO framework incorporates recurrent neural networks(RNNs)to model temporal dependencies in UAV mobility patterns and utilizes a decentralized multi-agent reinforcement learning architecture to reduce communication overhead while improving system robustness.The proposed RMAPPO algorithm was evaluated through simulation experiments,with the results indicating that it significantly enhances the cumulative traffic offloading rate of nodes and reduces the energy consumption of UAVs.
基金supported by the National Natural Science Foundation of China under Grant Nos.62350048,T2121003 and U20B2071.
文摘Fixed-wing unmanned aerial vehicles(UAVs)are a primary focus of current UAV research.Challenges arise in theirflight due to high speed and complex maneuverability.This paper explores the coordinated turn guidance law for fixed-wing UAVs and validates an experimental leader-follower formation platform inflight.Results demonstrate the effectiveness of the proposed algorithm and platform in enabling actual leader-follower formationflights for fixed-wing UAVs.
文摘With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.
