Evaluating Unmanned Aerial Vehicle(UAV)systems within a System-of-Systems(SoS)environment helps clarify their contribution to the overall combat capability and supports effectiveness-oriented system optimization.When ...Evaluating Unmanned Aerial Vehicle(UAV)systems within a System-of-Systems(SoS)environment helps clarify their contribution to the overall combat capability and supports effectiveness-oriented system optimization.When assessing decision systems in such an environment,cross-level modeling and simulation are required,which often face a trade-off between low modeling cost and high simulation accuracy,while the credibility of results remains challenging to ensure.To address these issues,this study proposes a hybrid-granularity Hardware-In-the-Loop(HIL)SoS environment construction method based on Graphical Evaluation and Review Technique(GERT).The method employs GERT to analyze the relationships between simulation systems,the System Under Test(SUT),and mission outcomes,thereby determining the required model precision for different systems.A dynamic resource allocation algorithm is applied to adjust model granularity on demand,ensuring high-fidelity simulation under constrained total cost.Additionally,GERT estimates the computational frequency and communication bandwidth requirements of the SUT,guiding hardware selection to enhance simulation credibility.A UAV maritime combat case study was conducted for validation.The results demonstrate that,compared to the flat modeling approach,the hybrid-granularity scenario based on GERT analysis achieves higher simulation accuracy with lower overall model complexity.The coefficient of variation in evaluation results significantly decreases in HIL simulations compared to virtual simulations,confirming improved credibility.Under the hybrid-granularity HIL scenario,the decision system was evaluated from an effectiveness perspective,identifying the most sensitive performance parameter.Subsequent targeted optimization led to an 11.90%improvement in effectiveness,validating the method's practical utility.展开更多
Unmanned aircraft are highly vulnerable to crosswind-induced turbulence during complex maneuvers such as turning,which can significantly compromise control and reduce autopilot effectiveness.This paper presents a nove...Unmanned aircraft are highly vulnerable to crosswind-induced turbulence during complex maneuvers such as turning,which can significantly compromise control and reduce autopilot effectiveness.This paper presents a novel control strategy to improve the controllability of unmanned aircraft in challenging wind conditions.First,the equations of motion for the aircraft are reformulated as a system of stochastic differential equations,which are subsequently transformed into a deterministic form.By modeling turbulence as a Gaussian random process and incorporating it directly into the control system,the proposed method proactively compensates for the adverse effects of turbulence.The transformation is achieved using semi-invariant techniques.Second,the control problem is formulated as an optimization task,aiming to minimize the deviation between the actual and desired turn characteristics,specifically the angular velocity.Finally,a new numerical method with proven global convergence is employed to compute the optimal autopilot parameters.Simulation results using a medium-range unmanned aircraft model under continuous turbulent gusts demonstrate that the proposed method significantly outperforms existing approaches,ensuring both stability and precision in turbulent wind conditions.展开更多
To address the challenges of small target detection and significant scale variations in unmanned aerial vehicle(UAV)aerial imagery,which often lead to missed and false detections,we propose Multi-scale Feature Fusion ...To address the challenges of small target detection and significant scale variations in unmanned aerial vehicle(UAV)aerial imagery,which often lead to missed and false detections,we propose Multi-scale Feature Fusion YOLO(MFF-YOLO),an enhanced algorithm based on YOLOv8s.Our approach introduces a Multi-scale Feature Fusion Strategy(MFFS),comprising the Multiple Features C2f(MFC)module and the Scale Sequence Feature Fusion(SSFF)module,to improve feature integration across different network levels.This enables more effective capture of fine-grained details and sequential multi-scale features.Furthermore,we incorporate Inner-CIoU,an improved loss function that uses auxiliary bounding boxes to enhance the regression quality of small object boxes.To ensure practicality for UAV deployment,we apply the Layer-adaptive Magnitude-based pruning(LAMP)method to significantly reduce model size and computational cost.Experiments on the VisDrone2019 dataset show that MFF-YOLO achieves a 5.7% increase in mean average precision(mAP)over the baseline,while reducing parameters by 8.5 million and computation by 17.5%.The results demonstrate that our method effectively improves detection performance in UAV aerial scenarios.展开更多
UAV-mounted intelligent reflecting surface(IRS)helps address the line-of-sight(LoS)blockage between sensor nodes(SNs)and the fusion center(FC)in Internet of Things(IoT).This paper considers an IoT assisted by multiple...UAV-mounted intelligent reflecting surface(IRS)helps address the line-of-sight(LoS)blockage between sensor nodes(SNs)and the fusion center(FC)in Internet of Things(IoT).This paper considers an IoT assisted by multiple UAVs-mounted IRS(U-IRS),where the data from ground SNs are transmitted to the FC.In practice,energy efficiency(EE)and mission completion time are crucial metrics for evaluating system performance and operational costs.Recognizing their importance during data collection,we formulate a multi-objective optimization problem to maximize EE and minimize total mission completion time simultaneously.To characterize this tradeoff while considering optimization objective consistency,we construct an optimization problem that minimizes the weighted sum of the total mission completion time and the reciprocal of EE.Due to the non-convex nature of the formulated problem,obtaining optimal solutions is generally challenging.To tackle this issue,we decompose it into three subproblems:UAV-SN association,number of reflecting elements allocation,andUAVtrajectory optimization.An iterative algorithmcombining genetic algorithm,CS-BJ algorithm,and successive convex approximation technique is proposed to solve these sub-problems.Simulation results demonstrate that when the transmitted data amount is 10 and 30Mbits,compared to the static collection benchmark(the UAV hovers directly above each SN),the EE of the proposed method improves by more than 10.4% and 5.2%,while the total mission completion time is reduced by more than 5.4% and 3.3%,respectively.展开更多
Owing to the development of communication technologies and control systems,the integration of numerous Internet of Things(IoT)nodes into the power grid has become increasingly prevalent.These nodes are deployed to gat...Owing to the development of communication technologies and control systems,the integration of numerous Internet of Things(IoT)nodes into the power grid has become increasingly prevalent.These nodes are deployed to gather operational data from various distributed energy sources and monitor real-time energy consumption,thereby transforming the traditional power grid into a smart grid(SG).However,the openness of wireless communication channels introduces vulnerabilities,as it allows potential eavesdroppers to intercept sensitive information.This poses threats to the secure and efficient operation of the IoT-driven smart grid.To address these challenges,we propose a novel scenario that incorporates an Unmanned Aerial Vehicle(UAV)as a relay gateway for multiple authorized smart meters.This scenario is further enhanced by the integration of Reconfigurable Intelligent Surface(RIS)technology,which dynamically adjusts the direction of information transmission.Our objective is to maximize the secure rate within this UAV-RIS-aided system with multiple authorized smart meters and an eavesdropper based on physical layer security(PLS)techniques.We formulate the problem of secure rate maximization by jointly optimizing the active beamforming of the UAV,the passive beamforming of the RIS,and the UAV’s trajectory.To solve this complex optimization problem,we introduce the Twin Soft Actor-Critic(TSAC)algorithm.This algorithm employs a dual-agent framework,where Agent 1 focuses on optimizing the beamforming for both the UAV and the RIS,while Agent 2 concurrently searches for the optimal trajectory of the UAV.Simulation results demonstrate the TSAC algorithm significantly enhances the secure rate of the system,achieving faster convergence and higher rewards under the worst communication conditions.The TSAC algorithm consistently outperforms the Twin Deep Deterministic Policy Gradient(TDDPG)and Twin Delayed Deep Deterministic Policy Gradient(TTD3)algorithms.Furthermore,the TSAC algorithm exhibits robust performance when the distribution of smart meters follows a Gaussian distribution,further validating its practical applicability and effectiveness in real-world scenarios.展开更多
The rapid proliferation of commercial unmanned aerial vehicles(UAVs)has revolutionized fields such as precision agriculture and disaster response.However,their heavy reliance on GPS navigation leaves them highly vulne...The rapid proliferation of commercial unmanned aerial vehicles(UAVs)has revolutionized fields such as precision agriculture and disaster response.However,their heavy reliance on GPS navigation leaves them highly vulnerable to spoofing attacks,with potentially severe consequences.To mitigate this threat,we present a machine learning-driven framework for real-time GPS spoofing detection,designed with a balance of detection accuracy and computational efficiency.Our work is distinguished by the creation of a comprehensive dataset of 10,000 instances that integrates both simulated and real-world data,enabling robust and generalizable model development.A comprehensive evaluation ofmultiple classification algorithms identifies XGBoost as the superior performer,achieving 93.07% accuracy alongside outstanding precision,recall,and F1-scores.Beyond standard classification metrics,our assessment encompasses ROC-AUC,detection latency,and false positive rate,providing a comprehensive assessment of performance.This work contributes to UAV security by providing a robust and reproducible solution for detecting GPS spoofing attacks,supported by a detailed methodology,a comprehensive evaluation including inference-time latency,and a publicly available dataset.展开更多
Shenzhen,a major city in southern China,has experienced rapid advancements in Unmanned Aerial Vehicle(UAV)technology,resulting in extensive logistics networks with thousands of daily flights.However,frequent disruptio...Shenzhen,a major city in southern China,has experienced rapid advancements in Unmanned Aerial Vehicle(UAV)technology,resulting in extensive logistics networks with thousands of daily flights.However,frequent disruptions due to its subtropical monsoon climate,including typhoons and gusty winds,present ongoing challenges.Despite the growing focus on operational costs and third-party risks,research on low-altitude urban wind fields remains scarce.This study addresses this gap by integrating wind field analysis into UAV path planning,introducing key innovations to the classical model.First,UAV wind resistance and turbulence constraints are analyzed,mapping high-wind-speed and turbulence-prone zones in the airspace.Second,wind dynamics are incorporated into path planning by considering airspeed and groundspeed variation,optimizing waypoint selection and flight speed adjustments to improve overall energy efficiency.Additionally,a wind-aware Theta*algorithm is proposed,leveraging wind vectors to expedite search process,while Computational Fluid Dynamics(CFD)techniques are employed to calculate wind fields.A case study of Shenzhen,examining wind patterns over the past decade,demonstrates a 6.23%improvement in groundspeed and a 7.69%reduction in energy consumption compared to wind-agnostic models.This framework advances UAV logistics by enhancing route safety and energy efficiency,contributing to more cost-effective operations.展开更多
As joint operations have become a key trend in modern military development,unmanned aerial vehicles(UAVs)play an increasingly important role in enhancing the intelligence and responsiveness of combat systems.However,t...As joint operations have become a key trend in modern military development,unmanned aerial vehicles(UAVs)play an increasingly important role in enhancing the intelligence and responsiveness of combat systems.However,the heterogeneity of aircraft,partial observability,and dynamic uncertainty in operational airspace pose significant challenges to autonomous collision avoidance using traditional methods.To address these issues,this paper proposes an adaptive collision avoidance approach for UAVs based on deep reinforcement learning.First,a unified uncertainty model incorporating dynamic wind fields is constructed to capture the complexity of joint operational environments.Then,to effectively handle the heterogeneity between manned and unmanned aircraft and the limitations of dynamic observations,a sector-based partial observation mechanism is designed.A Dynamic Threat Prioritization Assessment algorithm is also proposed to evaluate potential collision threats from multiple dimensions,including time to closest approach,minimum separation distance,and aircraft type.Furthermore,a Hierarchical Prioritized Experience Replay(HPER)mechanism is introduced,which classifies experience samples into high,medium,and low priority levels to preferentially sample critical experiences,thereby improving learning efficiency and accelerating policy convergence.Simulation results show that the proposed HPER-D3QN algorithm outperforms existing methods in terms of learning speed,environmental adaptability,and robustness,significantly enhancing collision avoidance performance and convergence rate.Finally,transfer experiments on a high-fidelity battlefield airspace simulation platform validate the proposed method's deployment potential and practical applicability in complex,real-world joint operational scenarios.展开更多
Aiming at the problem of imbalance between detection accuracy and algorithm model lightweight in UAV aerial image target detection algorithm,a lightweight multi-category abnormal behavior detection algorithm based on ...Aiming at the problem of imbalance between detection accuracy and algorithm model lightweight in UAV aerial image target detection algorithm,a lightweight multi-category abnormal behavior detection algorithm based on improved YOLOv11n is designed.By integrating multi-head grouped self-attention mechanism and Partial-Conv,a two-way feature grouping fusion module(DFPF)was designed,which carried out effective channel segmentation and fusion strategies to reduce redundant calculations andmemory access.C3K2 module was improved,and then unstructured pruning and feature distillation technologywere used.The algorithmmodel is lightweight,and the feature extraction ability for airborne visual abnormal behavior targets is strengthened,and the computational efficiency of the model is improved.Finally,we test the generalization of the baseline model and the improved model on the VisDrone2019 dataset.The results show that com-pared with the baseline model,the detection accuracy of the final improved model on the airborne visual abnormal behavior dataset is improved from 90.2% to 94.8%,and the model parameters are reduced by 50.9% to meet the detection requirements of high efficiency and high precision.The detection accuracy of the improved model on the Vis-Drone2019 public dataset is 1.3% higher than that of the baseline model,indicating the effectiveness of the improved method in this paper.展开更多
The hose-drogue system is a common method for soft aerial refueling,whereby the refueling tanker tows the drogue through the hose.In this paper,a mathematical-physical model of the hose-drogue system is developed and ...The hose-drogue system is a common method for soft aerial refueling,whereby the refueling tanker tows the drogue through the hose.In this paper,a mathematical-physical model of the hose-drogue system is developed and simulated using the Absolute Nodal Coordinate Formulation(ANCF)finite element method.A numerical solution program based on ANCF and ALE(Arbitrary Eulerian-Lagrange)-ANCF method was developed to simulate and analyze the horizontal and elongation release processes of the hose-drogue system at different towing points(underneath the wing and the belly of the aircraft).This program was developed by introducing an ALE description.The numerical solution program,developed based on the ANCF and ALE-ANCF methods,represents a significant advancement in computational efficiency for the rigid-flexible coupled multibody system of the air refueling hose-drogue system.This program can provide a valuable reference for the qualitative design of the hose-drogue multibody system in soft air refueling,while maintaining the necessary accuracy.展开更多
Wind turbine blade defect detection faces persistent challenges in separating small,low-contrast surface faults from complex backgrounds while maintaining reliability under variable illumination and viewpoints.Conven-...Wind turbine blade defect detection faces persistent challenges in separating small,low-contrast surface faults from complex backgrounds while maintaining reliability under variable illumination and viewpoints.Conven-tional image-processing pipelines struggle with scalability and robustness,and recent deep learning methods remain sensitive to class imbalance and acquisition variability.This paper introduces TurbineBladeDetNet,a convolutional architecture combining dual-attention mechanisms with multi-path feature extraction for detecting five distinct blade fault types.Our approach employs both channel-wise and spatial attention modules alongside an Albumentations-driven augmentation strategy to handle dataset imbalance and capture condition variability.The model achieves 97.14%accuracy,98.65%precision,and 98.68%recall,yielding a 98.66%F1-score with 0.0110 s inference time.Class-specific analysis shows uniformly high sensitivity and specificity;lightning damage reaches 99.80%for sensitivity,precision,and F1-score,and crack achieves perfect precision and specificity with a 98.94%F1-score.Comparative evaluation against recent wind-turbine inspection approaches indicates higher performance in both accuracy and F1-score.The resulting balance of sensitivity and specificity limits both missed defects and false alarms,supporting reliable deployment in routine unmanned aerial vehicle(UAV)inspection.展开更多
Unmanned Aerial Vehicle(UAV)plays a prominent role in various fields,and autonomous navigation is a crucial component of UAV intelligence.Deep Reinforcement Learning(DRL)has expanded the research avenues for addressin...Unmanned Aerial Vehicle(UAV)plays a prominent role in various fields,and autonomous navigation is a crucial component of UAV intelligence.Deep Reinforcement Learning(DRL)has expanded the research avenues for addressing challenges in autonomous navigation.Nonetheless,challenges persist,including getting stuck in local optima,consuming excessive computations during action space exploration,and neglecting deterministic experience.This paper proposes a noise-driven enhancement strategy.In accordance with the overall learning phases,a global noise control method is designed,while a differentiated local noise control method is developed by analyzing the exploration demands of four typical situations encountered by UAV during navigation.Both methods are integrated into a dual-model for noise control to regulate action space exploration.Furthermore,noise dual experience replay buffers are designed to optimize the rational utilization of both deterministic and noisy experience.In uncertain environments,based on the Twin Delay Deep Deterministic Policy Gradient(TD3)algorithm with Long Short-Term Memory(LSTM)network and Priority Experience Replay(PER),a Noise-Driven Enhancement Priority Memory TD3(NDE-PMTD3)is developed.We established a simulation environment to compare different algorithms,and the performance of the algorithms is analyzed in various scenarios.The training results indicate that the proposed algorithm accelerates the convergence speed and enhances the convergence stability.In test experiments,the proposed algorithm successfully and efficiently performs autonomous navigation tasks in diverse environments,demonstrating superior generalization results.展开更多
Efficient multiple unmanned aerial vehicles(UAVs)path planning is crucial for improving mission completion efficiency in UAV operations.However,during the actual flight of UAVs,the flight time between nodes is always ...Efficient multiple unmanned aerial vehicles(UAVs)path planning is crucial for improving mission completion efficiency in UAV operations.However,during the actual flight of UAVs,the flight time between nodes is always influenced by external factors,making the original path planning solution ineffective.In this paper,the multi-depot multi-UAV path planning problem with uncertain flight time is modeled as a robust optimization model with a budget uncertainty set.Then,the robust optimization model is transformed into a mixed integer linear programming model by the strong duality theorem,which makes the problem easy to solve.To effectively solve large-scale instances,a simulated annealing algorithm with a robust feasibility check(SA-RFC)is developed.The numerical experiment shows that the SA-RFC can find high-quality solutions within a few seconds.Moreover,the effect of the task location distribution,depot counts,and variations in robustness parameters on the robust optimization solution is analyzed by using Monte Carlo experiments.The results demonstrate that the proposed robust model can effectively reduce the risk of the UAV failing to return to the depot without significantly compromising the profit.展开更多
Manned aerial vehicle-unmanned aerial vehicle(MAV-UAV)combat organization is a MAV-UAV combat collective formed from the perspective of organization design theory and methodology,and the generation of force formation ...Manned aerial vehicle-unmanned aerial vehicle(MAV-UAV)combat organization is a MAV-UAV combat collective formed from the perspective of organization design theory and methodology,and the generation of force formation plan is a key step in the organizational planning.Based on the description of the problem and the definition of organizational elements,the matching model of platform-target attack wave is constructed to minimize the redundancy of command and decision-making capability,resource capability and the number of platforms used.Based on the non-dominated sorting genetic algorithmⅢ(NSGA-Ⅲ)framework,which includes encoding/decoding method and constraint handling method,the generation model of organizational force formation plan is solved,and the effectiveness and superiority of the algorithm are verified by simulation experiments.展开更多
The rapid expansion of the low-altitude economy is driving strong demand for highly accurate and reliable positioning technologies to support diverse aerial operations.This review examines core positioning methodologi...The rapid expansion of the low-altitude economy is driving strong demand for highly accurate and reliable positioning technologies to support diverse aerial operations.This review examines core positioning methodologies within the low-altitude intelligent network(LAIN)framework,beginning with an analysis of positioning requirements and performance metrics for low-altitude flight scenarios.It systematically assesses the principles,strengths,and limitations of mainstream positioning systems,including Global Navigation Satellite Systems(GNSS),terrestrial wireless positioning,and autonomous navigation,and it surveys prevalent integrated and cooperative positioning schemes.Our analysis demonstrates that standalone positioning technologies are inadequate in complex low-altitude settings,underscoring the pivotal role of multi-source fusion and unmanned aerial vehicle(UAV)swarm cooperative positioning as future trends.To address infrastructure gaps and high deployment costs in current LAIN systems,we propose a“space−air−ground”integrated and cooperative positioning architecture centered on GNSS and the 5th generation mobile communication technology(5G).The ground layer integrates 5G and GNSS for wide-area enhanced positioning.The aerial layer uses 5G aircraft-to-everything(A2X)and sidelink(SL)communications to build self-organizing networks for cooperative UAV localization.The space layer leverages low Earth orbit(LEO)satellites to overcome coverage limitations in communication and positioning.This hierarchical architecture reduces deployment costs through infrastructure reuse and enables deep integration of communication and navigation capabilities.By supporting collaborative enhancement across all three domains,the framework improves positioning robustness and delivers cost-effective,ubiquitous,and highly reliable positioning services.Finally,we outline promising research directions.This review aims to provide a systematic reference and a novel architectural perspective for the ongoing development of LAIN.展开更多
For mission-oriented unmanned aerial vehicle(UAV)swarms,mission capability assessment provides an important reference in the design and development process,and is a precondition for mission success.For this multi-crit...For mission-oriented unmanned aerial vehicle(UAV)swarms,mission capability assessment provides an important reference in the design and development process,and is a precondition for mission success.For this multi-criteria decisionmaking(MCDM)problem,the current literature lacks a way to unambiguously present criteria and the popular fuzzy analytic network process(ANP)approaches neglect the hesitancy of subjective judgments.To fill these research gaps,an MCDM method based on unified architecture framework(UAF)and interval-valued spherical fuzzy ANP(IVSF-ANP)is proposed in this paper.Firstly,selected viewpoints in UAF are extended to construct criteria models with standardized representation.Secondly,interval-valued spherical fuzzy sets are introduced to ANP to weight interdependent criteria,handling fuzziness and hesitancy in pairwise comparisons.A method of adjusting weights of experts based on their decision similarities is also included in this process to reduce ambiguity brought by multiple experts.Next,performance characteristics are non-linearly transformed regarding to expectations to get final results.This proposition is applied to assess the mission capability of UAV swarms to search and strike surface vessels.Comparative analysis shows that the proposed method is valid and reasonable.展开更多
In disaster relief operations,multiple UAVs can be used to search for trapped people.In recent years,many researchers have proposed machine le arning-based algorithms,sampling-based algorithms,and heuristic algorithms...In disaster relief operations,multiple UAVs can be used to search for trapped people.In recent years,many researchers have proposed machine le arning-based algorithms,sampling-based algorithms,and heuristic algorithms to solve the problem of multi-UAV path planning.The Dung Beetle Optimization(DBO)algorithm has been widely applied due to its diverse search patterns in the above algorithms.However,the update strategies for the rolling and thieving dung beetles of the DBO algorithm are overly simplistic,potentially leading to an inability to fully explore the search space and a tendency to converge to local optima,thereby not guaranteeing the discovery of the optimal path.To address these issues,we propose an improved DBO algorithm guided by the Landmark Operator(LODBO).Specifically,we first use tent mapping to update the population strategy,which enables the algorithm to generate initial solutions with enhanced diversity within the search space.Second,we expand the search range of the rolling ball dung beetle by using the landmark factor.Finally,by using the adaptive factor that changes with the number of iterations.,we improve the global search ability of the stealing dung beetle,making it more likely to escape from local optima.To verify the effectiveness of the proposed method,extensive simulation experiments are conducted,and the result shows that the LODBO algorithm can obtain the optimal path using the shortest time compared with the Genetic Algorithm(GA),the Gray Wolf Optimizer(GWO),the Whale Optimization Algorithm(WOA)and the original DBO algorithm in the disaster search and rescue task set.展开更多
Unmanned aerial vehicles(UAVs)have become crucial tools in moving target tracking due to their agility and ability to operate in complex,dynamic environments.UAVs must meet several requirements to achieve stable track...Unmanned aerial vehicles(UAVs)have become crucial tools in moving target tracking due to their agility and ability to operate in complex,dynamic environments.UAVs must meet several requirements to achieve stable tracking,including maintaining continuous target visibility amidst occlusions,ensuring flight safety,and achieving smooth trajectory planning.This paper reviews the latest advancements in UAV-based target tracking,highlighting information prediction,tracking strategies,and swarm cooperation.To address challenges including target visibility and occlusion,real-time prediction and tracking in dynamic environments,flight safety and coordination,resource management and energy efficiency,the paper identifies future research directions aimed at improving the performance,reliability,and scalability of UAV tracking system.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
基金funded by Henan Key Laboratory of General Aviation Technology,grant number ZHKF-240202。
文摘Evaluating Unmanned Aerial Vehicle(UAV)systems within a System-of-Systems(SoS)environment helps clarify their contribution to the overall combat capability and supports effectiveness-oriented system optimization.When assessing decision systems in such an environment,cross-level modeling and simulation are required,which often face a trade-off between low modeling cost and high simulation accuracy,while the credibility of results remains challenging to ensure.To address these issues,this study proposes a hybrid-granularity Hardware-In-the-Loop(HIL)SoS environment construction method based on Graphical Evaluation and Review Technique(GERT).The method employs GERT to analyze the relationships between simulation systems,the System Under Test(SUT),and mission outcomes,thereby determining the required model precision for different systems.A dynamic resource allocation algorithm is applied to adjust model granularity on demand,ensuring high-fidelity simulation under constrained total cost.Additionally,GERT estimates the computational frequency and communication bandwidth requirements of the SUT,guiding hardware selection to enhance simulation credibility.A UAV maritime combat case study was conducted for validation.The results demonstrate that,compared to the flat modeling approach,the hybrid-granularity scenario based on GERT analysis achieves higher simulation accuracy with lower overall model complexity.The coefficient of variation in evaluation results significantly decreases in HIL simulations compared to virtual simulations,confirming improved credibility.Under the hybrid-granularity HIL scenario,the decision system was evaluated from an effectiveness perspective,identifying the most sensitive performance parameter.Subsequent targeted optimization led to an 11.90%improvement in effectiveness,validating the method's practical utility.
基金co-supported by the Fund of Robot Technology Used for Special Environment Key Laboratory of Sichuan Province(No.22kftk01)the Key Research and Development Program of Heilongjiang,China(No.2024ZXJ07B05)the National Natural Science Foundation of China(No.92471103)。
文摘Unmanned aircraft are highly vulnerable to crosswind-induced turbulence during complex maneuvers such as turning,which can significantly compromise control and reduce autopilot effectiveness.This paper presents a novel control strategy to improve the controllability of unmanned aircraft in challenging wind conditions.First,the equations of motion for the aircraft are reformulated as a system of stochastic differential equations,which are subsequently transformed into a deterministic form.By modeling turbulence as a Gaussian random process and incorporating it directly into the control system,the proposed method proactively compensates for the adverse effects of turbulence.The transformation is achieved using semi-invariant techniques.Second,the control problem is formulated as an optimization task,aiming to minimize the deviation between the actual and desired turn characteristics,specifically the angular velocity.Finally,a new numerical method with proven global convergence is employed to compute the optimal autopilot parameters.Simulation results using a medium-range unmanned aircraft model under continuous turbulent gusts demonstrate that the proposed method significantly outperforms existing approaches,ensuring both stability and precision in turbulent wind conditions.
基金supported by the National Natural Science Foundation of China(No.61976028).
文摘To address the challenges of small target detection and significant scale variations in unmanned aerial vehicle(UAV)aerial imagery,which often lead to missed and false detections,we propose Multi-scale Feature Fusion YOLO(MFF-YOLO),an enhanced algorithm based on YOLOv8s.Our approach introduces a Multi-scale Feature Fusion Strategy(MFFS),comprising the Multiple Features C2f(MFC)module and the Scale Sequence Feature Fusion(SSFF)module,to improve feature integration across different network levels.This enables more effective capture of fine-grained details and sequential multi-scale features.Furthermore,we incorporate Inner-CIoU,an improved loss function that uses auxiliary bounding boxes to enhance the regression quality of small object boxes.To ensure practicality for UAV deployment,we apply the Layer-adaptive Magnitude-based pruning(LAMP)method to significantly reduce model size and computational cost.Experiments on the VisDrone2019 dataset show that MFF-YOLO achieves a 5.7% increase in mean average precision(mAP)over the baseline,while reducing parameters by 8.5 million and computation by 17.5%.The results demonstrate that our method effectively improves detection performance in UAV aerial scenarios.
基金supported in part by the Opening Project of Guangxi Wireless Broadband Communication and Signal Processing Key Laboratory under Grant AD25069102in part by the Basic Ability Improvement Project of Young and Middle Aged Teachers in Guangxi Universities,under Grant 2023KY0226+6 种基金in part by Key Laboratory of Cognitive Radio and Information Processing,Ministry of Education of China,underGrant CRKL220108in part by the Innovation Project of Guangxi Graduate Education,under Grant YCBZ2023131in part by the Doctoral Research Foundation of Guilin University of Electronic Technology,under Grant UF23038Yin part by the Bagui Youth Top Talent Projectin part by the Guangxi Key Research and Development Program under Grant AB25069510in part by Open Fund of IPOC(BUPT),No.IPOC2024B07in part by Guangxi Key Laboratory of Precision Navigation Technology and Application,under Grant DH202309.
文摘UAV-mounted intelligent reflecting surface(IRS)helps address the line-of-sight(LoS)blockage between sensor nodes(SNs)and the fusion center(FC)in Internet of Things(IoT).This paper considers an IoT assisted by multiple UAVs-mounted IRS(U-IRS),where the data from ground SNs are transmitted to the FC.In practice,energy efficiency(EE)and mission completion time are crucial metrics for evaluating system performance and operational costs.Recognizing their importance during data collection,we formulate a multi-objective optimization problem to maximize EE and minimize total mission completion time simultaneously.To characterize this tradeoff while considering optimization objective consistency,we construct an optimization problem that minimizes the weighted sum of the total mission completion time and the reciprocal of EE.Due to the non-convex nature of the formulated problem,obtaining optimal solutions is generally challenging.To tackle this issue,we decompose it into three subproblems:UAV-SN association,number of reflecting elements allocation,andUAVtrajectory optimization.An iterative algorithmcombining genetic algorithm,CS-BJ algorithm,and successive convex approximation technique is proposed to solve these sub-problems.Simulation results demonstrate that when the transmitted data amount is 10 and 30Mbits,compared to the static collection benchmark(the UAV hovers directly above each SN),the EE of the proposed method improves by more than 10.4% and 5.2%,while the total mission completion time is reduced by more than 5.4% and 3.3%,respectively.
基金supported by State Grid Shanxi Electric Power Company’s Science and Technology Projects(No.52051C230102).
文摘Owing to the development of communication technologies and control systems,the integration of numerous Internet of Things(IoT)nodes into the power grid has become increasingly prevalent.These nodes are deployed to gather operational data from various distributed energy sources and monitor real-time energy consumption,thereby transforming the traditional power grid into a smart grid(SG).However,the openness of wireless communication channels introduces vulnerabilities,as it allows potential eavesdroppers to intercept sensitive information.This poses threats to the secure and efficient operation of the IoT-driven smart grid.To address these challenges,we propose a novel scenario that incorporates an Unmanned Aerial Vehicle(UAV)as a relay gateway for multiple authorized smart meters.This scenario is further enhanced by the integration of Reconfigurable Intelligent Surface(RIS)technology,which dynamically adjusts the direction of information transmission.Our objective is to maximize the secure rate within this UAV-RIS-aided system with multiple authorized smart meters and an eavesdropper based on physical layer security(PLS)techniques.We formulate the problem of secure rate maximization by jointly optimizing the active beamforming of the UAV,the passive beamforming of the RIS,and the UAV’s trajectory.To solve this complex optimization problem,we introduce the Twin Soft Actor-Critic(TSAC)algorithm.This algorithm employs a dual-agent framework,where Agent 1 focuses on optimizing the beamforming for both the UAV and the RIS,while Agent 2 concurrently searches for the optimal trajectory of the UAV.Simulation results demonstrate the TSAC algorithm significantly enhances the secure rate of the system,achieving faster convergence and higher rewards under the worst communication conditions.The TSAC algorithm consistently outperforms the Twin Deep Deterministic Policy Gradient(TDDPG)and Twin Delayed Deep Deterministic Policy Gradient(TTD3)algorithms.Furthermore,the TSAC algorithm exhibits robust performance when the distribution of smart meters follows a Gaussian distribution,further validating its practical applicability and effectiveness in real-world scenarios.
文摘The rapid proliferation of commercial unmanned aerial vehicles(UAVs)has revolutionized fields such as precision agriculture and disaster response.However,their heavy reliance on GPS navigation leaves them highly vulnerable to spoofing attacks,with potentially severe consequences.To mitigate this threat,we present a machine learning-driven framework for real-time GPS spoofing detection,designed with a balance of detection accuracy and computational efficiency.Our work is distinguished by the creation of a comprehensive dataset of 10,000 instances that integrates both simulated and real-world data,enabling robust and generalizable model development.A comprehensive evaluation ofmultiple classification algorithms identifies XGBoost as the superior performer,achieving 93.07% accuracy alongside outstanding precision,recall,and F1-scores.Beyond standard classification metrics,our assessment encompasses ROC-AUC,detection latency,and false positive rate,providing a comprehensive assessment of performance.This work contributes to UAV security by providing a robust and reproducible solution for detecting GPS spoofing attacks,supported by a detailed methodology,a comprehensive evaluation including inference-time latency,and a publicly available dataset.
基金supported by the National Natural Science Foundation of China(No.U2433214)。
文摘Shenzhen,a major city in southern China,has experienced rapid advancements in Unmanned Aerial Vehicle(UAV)technology,resulting in extensive logistics networks with thousands of daily flights.However,frequent disruptions due to its subtropical monsoon climate,including typhoons and gusty winds,present ongoing challenges.Despite the growing focus on operational costs and third-party risks,research on low-altitude urban wind fields remains scarce.This study addresses this gap by integrating wind field analysis into UAV path planning,introducing key innovations to the classical model.First,UAV wind resistance and turbulence constraints are analyzed,mapping high-wind-speed and turbulence-prone zones in the airspace.Second,wind dynamics are incorporated into path planning by considering airspeed and groundspeed variation,optimizing waypoint selection and flight speed adjustments to improve overall energy efficiency.Additionally,a wind-aware Theta*algorithm is proposed,leveraging wind vectors to expedite search process,while Computational Fluid Dynamics(CFD)techniques are employed to calculate wind fields.A case study of Shenzhen,examining wind patterns over the past decade,demonstrates a 6.23%improvement in groundspeed and a 7.69%reduction in energy consumption compared to wind-agnostic models.This framework advances UAV logistics by enhancing route safety and energy efficiency,contributing to more cost-effective operations.
基金supported by the National Key Research and Development Program of China(No.2022YFB4300902).
文摘As joint operations have become a key trend in modern military development,unmanned aerial vehicles(UAVs)play an increasingly important role in enhancing the intelligence and responsiveness of combat systems.However,the heterogeneity of aircraft,partial observability,and dynamic uncertainty in operational airspace pose significant challenges to autonomous collision avoidance using traditional methods.To address these issues,this paper proposes an adaptive collision avoidance approach for UAVs based on deep reinforcement learning.First,a unified uncertainty model incorporating dynamic wind fields is constructed to capture the complexity of joint operational environments.Then,to effectively handle the heterogeneity between manned and unmanned aircraft and the limitations of dynamic observations,a sector-based partial observation mechanism is designed.A Dynamic Threat Prioritization Assessment algorithm is also proposed to evaluate potential collision threats from multiple dimensions,including time to closest approach,minimum separation distance,and aircraft type.Furthermore,a Hierarchical Prioritized Experience Replay(HPER)mechanism is introduced,which classifies experience samples into high,medium,and low priority levels to preferentially sample critical experiences,thereby improving learning efficiency and accelerating policy convergence.Simulation results show that the proposed HPER-D3QN algorithm outperforms existing methods in terms of learning speed,environmental adaptability,and robustness,significantly enhancing collision avoidance performance and convergence rate.Finally,transfer experiments on a high-fidelity battlefield airspace simulation platform validate the proposed method's deployment potential and practical applicability in complex,real-world joint operational scenarios.
基金supported by y the Applied Research Advancement Project in Engineering University of PAP(WYY202304)Research and Innovation Team Project in Engineering University of PAP(KYTD202306)Funding for postgraduate education and teaching.
文摘Aiming at the problem of imbalance between detection accuracy and algorithm model lightweight in UAV aerial image target detection algorithm,a lightweight multi-category abnormal behavior detection algorithm based on improved YOLOv11n is designed.By integrating multi-head grouped self-attention mechanism and Partial-Conv,a two-way feature grouping fusion module(DFPF)was designed,which carried out effective channel segmentation and fusion strategies to reduce redundant calculations andmemory access.C3K2 module was improved,and then unstructured pruning and feature distillation technologywere used.The algorithmmodel is lightweight,and the feature extraction ability for airborne visual abnormal behavior targets is strengthened,and the computational efficiency of the model is improved.Finally,we test the generalization of the baseline model and the improved model on the VisDrone2019 dataset.The results show that com-pared with the baseline model,the detection accuracy of the final improved model on the airborne visual abnormal behavior dataset is improved from 90.2% to 94.8%,and the model parameters are reduced by 50.9% to meet the detection requirements of high efficiency and high precision.The detection accuracy of the improved model on the Vis-Drone2019 public dataset is 1.3% higher than that of the baseline model,indicating the effectiveness of the improved method in this paper.
基金the support from the National Natural Science Foundation of China(No.52472384)the Fundamental Research Funds for the Central Universities,China(No.G2024KY0615)+1 种基金sponsored by the Foundations of National Key Laboratory of Unmanned Aerial Vehicle Technology in NPU,(No.WR202411-2)the National Key Laboratory of Aircraft Configuration Design,China(No.JBGS-2024-01)。
文摘The hose-drogue system is a common method for soft aerial refueling,whereby the refueling tanker tows the drogue through the hose.In this paper,a mathematical-physical model of the hose-drogue system is developed and simulated using the Absolute Nodal Coordinate Formulation(ANCF)finite element method.A numerical solution program based on ANCF and ALE(Arbitrary Eulerian-Lagrange)-ANCF method was developed to simulate and analyze the horizontal and elongation release processes of the hose-drogue system at different towing points(underneath the wing and the belly of the aircraft).This program was developed by introducing an ALE description.The numerical solution program,developed based on the ANCF and ALE-ANCF methods,represents a significant advancement in computational efficiency for the rigid-flexible coupled multibody system of the air refueling hose-drogue system.This program can provide a valuable reference for the qualitative design of the hose-drogue multibody system in soft air refueling,while maintaining the necessary accuracy.
文摘Wind turbine blade defect detection faces persistent challenges in separating small,low-contrast surface faults from complex backgrounds while maintaining reliability under variable illumination and viewpoints.Conven-tional image-processing pipelines struggle with scalability and robustness,and recent deep learning methods remain sensitive to class imbalance and acquisition variability.This paper introduces TurbineBladeDetNet,a convolutional architecture combining dual-attention mechanisms with multi-path feature extraction for detecting five distinct blade fault types.Our approach employs both channel-wise and spatial attention modules alongside an Albumentations-driven augmentation strategy to handle dataset imbalance and capture condition variability.The model achieves 97.14%accuracy,98.65%precision,and 98.68%recall,yielding a 98.66%F1-score with 0.0110 s inference time.Class-specific analysis shows uniformly high sensitivity and specificity;lightning damage reaches 99.80%for sensitivity,precision,and F1-score,and crack achieves perfect precision and specificity with a 98.94%F1-score.Comparative evaluation against recent wind-turbine inspection approaches indicates higher performance in both accuracy and F1-score.The resulting balance of sensitivity and specificity limits both missed defects and false alarms,supporting reliable deployment in routine unmanned aerial vehicle(UAV)inspection.
基金the Collaborative Innovation Project of Shanghai,China for the financial support。
文摘Unmanned Aerial Vehicle(UAV)plays a prominent role in various fields,and autonomous navigation is a crucial component of UAV intelligence.Deep Reinforcement Learning(DRL)has expanded the research avenues for addressing challenges in autonomous navigation.Nonetheless,challenges persist,including getting stuck in local optima,consuming excessive computations during action space exploration,and neglecting deterministic experience.This paper proposes a noise-driven enhancement strategy.In accordance with the overall learning phases,a global noise control method is designed,while a differentiated local noise control method is developed by analyzing the exploration demands of four typical situations encountered by UAV during navigation.Both methods are integrated into a dual-model for noise control to regulate action space exploration.Furthermore,noise dual experience replay buffers are designed to optimize the rational utilization of both deterministic and noisy experience.In uncertain environments,based on the Twin Delay Deep Deterministic Policy Gradient(TD3)algorithm with Long Short-Term Memory(LSTM)network and Priority Experience Replay(PER),a Noise-Driven Enhancement Priority Memory TD3(NDE-PMTD3)is developed.We established a simulation environment to compare different algorithms,and the performance of the algorithms is analyzed in various scenarios.The training results indicate that the proposed algorithm accelerates the convergence speed and enhances the convergence stability.In test experiments,the proposed algorithm successfully and efficiently performs autonomous navigation tasks in diverse environments,demonstrating superior generalization results.
基金supported by the National Natural Science Foundation of China(72571094,72271076,71871079)。
文摘Efficient multiple unmanned aerial vehicles(UAVs)path planning is crucial for improving mission completion efficiency in UAV operations.However,during the actual flight of UAVs,the flight time between nodes is always influenced by external factors,making the original path planning solution ineffective.In this paper,the multi-depot multi-UAV path planning problem with uncertain flight time is modeled as a robust optimization model with a budget uncertainty set.Then,the robust optimization model is transformed into a mixed integer linear programming model by the strong duality theorem,which makes the problem easy to solve.To effectively solve large-scale instances,a simulated annealing algorithm with a robust feasibility check(SA-RFC)is developed.The numerical experiment shows that the SA-RFC can find high-quality solutions within a few seconds.Moreover,the effect of the task location distribution,depot counts,and variations in robustness parameters on the robust optimization solution is analyzed by using Monte Carlo experiments.The results demonstrate that the proposed robust model can effectively reduce the risk of the UAV failing to return to the depot without significantly compromising the profit.
基金supported by the Natural Science Foundation of Shaanxi Province(2023-JC-QN-0728)the China Postdoctoral Science Foundation(2021M693942)。
文摘Manned aerial vehicle-unmanned aerial vehicle(MAV-UAV)combat organization is a MAV-UAV combat collective formed from the perspective of organization design theory and methodology,and the generation of force formation plan is a key step in the organizational planning.Based on the description of the problem and the definition of organizational elements,the matching model of platform-target attack wave is constructed to minimize the redundancy of command and decision-making capability,resource capability and the number of platforms used.Based on the non-dominated sorting genetic algorithmⅢ(NSGA-Ⅲ)framework,which includes encoding/decoding method and constraint handling method,the generation model of organizational force formation plan is solved,and the effectiveness and superiority of the algorithm are verified by simulation experiments.
基金supported by the National Key Research&Development Program of China(No.2024YFB3910102).
文摘The rapid expansion of the low-altitude economy is driving strong demand for highly accurate and reliable positioning technologies to support diverse aerial operations.This review examines core positioning methodologies within the low-altitude intelligent network(LAIN)framework,beginning with an analysis of positioning requirements and performance metrics for low-altitude flight scenarios.It systematically assesses the principles,strengths,and limitations of mainstream positioning systems,including Global Navigation Satellite Systems(GNSS),terrestrial wireless positioning,and autonomous navigation,and it surveys prevalent integrated and cooperative positioning schemes.Our analysis demonstrates that standalone positioning technologies are inadequate in complex low-altitude settings,underscoring the pivotal role of multi-source fusion and unmanned aerial vehicle(UAV)swarm cooperative positioning as future trends.To address infrastructure gaps and high deployment costs in current LAIN systems,we propose a“space−air−ground”integrated and cooperative positioning architecture centered on GNSS and the 5th generation mobile communication technology(5G).The ground layer integrates 5G and GNSS for wide-area enhanced positioning.The aerial layer uses 5G aircraft-to-everything(A2X)and sidelink(SL)communications to build self-organizing networks for cooperative UAV localization.The space layer leverages low Earth orbit(LEO)satellites to overcome coverage limitations in communication and positioning.This hierarchical architecture reduces deployment costs through infrastructure reuse and enables deep integration of communication and navigation capabilities.By supporting collaborative enhancement across all three domains,the framework improves positioning robustness and delivers cost-effective,ubiquitous,and highly reliable positioning services.Finally,we outline promising research directions.This review aims to provide a systematic reference and a novel architectural perspective for the ongoing development of LAIN.
基金supported by the National Natural Science Foundation of China(62073267,61903305)the Fundamental Research Funds for the Central Universities(HXGJXM202214)。
文摘For mission-oriented unmanned aerial vehicle(UAV)swarms,mission capability assessment provides an important reference in the design and development process,and is a precondition for mission success.For this multi-criteria decisionmaking(MCDM)problem,the current literature lacks a way to unambiguously present criteria and the popular fuzzy analytic network process(ANP)approaches neglect the hesitancy of subjective judgments.To fill these research gaps,an MCDM method based on unified architecture framework(UAF)and interval-valued spherical fuzzy ANP(IVSF-ANP)is proposed in this paper.Firstly,selected viewpoints in UAF are extended to construct criteria models with standardized representation.Secondly,interval-valued spherical fuzzy sets are introduced to ANP to weight interdependent criteria,handling fuzziness and hesitancy in pairwise comparisons.A method of adjusting weights of experts based on their decision similarities is also included in this process to reduce ambiguity brought by multiple experts.Next,performance characteristics are non-linearly transformed regarding to expectations to get final results.This proposition is applied to assess the mission capability of UAV swarms to search and strike surface vessels.Comparative analysis shows that the proposed method is valid and reasonable.
基金supported by the National Natural Science Foundation of China(No.62373027).
文摘In disaster relief operations,multiple UAVs can be used to search for trapped people.In recent years,many researchers have proposed machine le arning-based algorithms,sampling-based algorithms,and heuristic algorithms to solve the problem of multi-UAV path planning.The Dung Beetle Optimization(DBO)algorithm has been widely applied due to its diverse search patterns in the above algorithms.However,the update strategies for the rolling and thieving dung beetles of the DBO algorithm are overly simplistic,potentially leading to an inability to fully explore the search space and a tendency to converge to local optima,thereby not guaranteeing the discovery of the optimal path.To address these issues,we propose an improved DBO algorithm guided by the Landmark Operator(LODBO).Specifically,we first use tent mapping to update the population strategy,which enables the algorithm to generate initial solutions with enhanced diversity within the search space.Second,we expand the search range of the rolling ball dung beetle by using the landmark factor.Finally,by using the adaptive factor that changes with the number of iterations.,we improve the global search ability of the stealing dung beetle,making it more likely to escape from local optima.To verify the effectiveness of the proposed method,extensive simulation experiments are conducted,and the result shows that the LODBO algorithm can obtain the optimal path using the shortest time compared with the Genetic Algorithm(GA),the Gray Wolf Optimizer(GWO),the Whale Optimization Algorithm(WOA)and the original DBO algorithm in the disaster search and rescue task set.
基金financial support provided by the Natural Science Foundation of Hunan Province of China(Grant No.2021JJ10045)the Open Research Subject of State Key Laboratory of Intelligent Game(Grant No.ZBKF-24-01)+1 种基金the Postdoctoral Fellowship Program of CPSF(Grant No.GZB20240989)the China Postdoctoral Science Foundation(Grant No.2024M754304)。
文摘Unmanned aerial vehicles(UAVs)have become crucial tools in moving target tracking due to their agility and ability to operate in complex,dynamic environments.UAVs must meet several requirements to achieve stable tracking,including maintaining continuous target visibility amidst occlusions,ensuring flight safety,and achieving smooth trajectory planning.This paper reviews the latest advancements in UAV-based target tracking,highlighting information prediction,tracking strategies,and swarm cooperation.To address challenges including target visibility and occlusion,real-time prediction and tracking in dynamic environments,flight safety and coordination,resource management and energy efficiency,the paper identifies future research directions aimed at improving the performance,reliability,and scalability of UAV tracking system.
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.
