The purpose of this research is to improve the robustness of the autonomous system in order to improve the position and velocity estimation of an Unmanned Aerial Vehicle(UAV).Therefore, new integrated SINS/GPS navigat...The purpose of this research is to improve the robustness of the autonomous system in order to improve the position and velocity estimation of an Unmanned Aerial Vehicle(UAV).Therefore, new integrated SINS/GPS navigation scheme based on Interacting Multiple Nonlinear Fuzzy Adaptive H_∞ Models(IMM-NFAH_∞) filtering technique for UAV is presented. The proposed IMM-NFAH_∞ strategy switches between two different Nonlinear Fuzzy Adaptive H_∞(NFAH_∞) filters and each NFAH_∞ filter is based on different fuzzy logic inference systems. The newly proposed technique takes into consideration the high order Taylor series terms and adapts the nonlinear H_∞ filter based on different fuzzy inference systems via adaptive filter bounds(di),along with disturbance attenuation parameter c. Simulation analysis validates the performance of the proposed algorithm, and the comparison with nonlinear H_∞(NH_∞) filter and that with different NFAH_∞ filters demonstrate the effectiveness of UAV localization utilizing IMM-NFAH_∞ filter.展开更多
To address the challenges of multi-scale differences,complex background interference,and unstable small target positioning in visual inspection of power towers,the existing methods still face issues such as insufficie...To address the challenges of multi-scale differences,complex background interference,and unstable small target positioning in visual inspection of power towers,the existing methods still face issues such as insufficient feature interaction and unstable confidence estimation,which lead to performance degradation in complex backgrounds and occlusion conditions.This paper proposes a precise inspection method for key power tower components using autonomous drone positioning.To this end,this paper makes three key improvements to the you only look once version 11(YOLOv11)framework.First,it constructs C3k2-adaptive multi-receptive field block(C3k2-AMRB),combining multiple dilated convolutions with a reparameterization mechanism to significantly expand the receptive field and enhance multi-scale feature extraction.Second,it designs a hierarchical wavelet interaction unit(HWIU),which leverages high-and low-frequency decomposition and reconstruction of wavelet transform(WT)to achieve cross-scale semantic alignment,enhancing feature discriminability in complex backgrounds.Third,it proposes a distribution-aware confidence refinement head(DACR-Head),which adaptively calibrates classification confidence based on the statistical characteristics of the predicted bounding-box corner distribution,improving the localization stability and accuracy of small targets.Experiments on the inspection of power line assets dataset(InsPLAD)dataset show that the integrated approach achieves a component detection accuracy at intersection over union(IoU)=0.5(CDA_(50))of 88.3%and a component detection robustness(CDR_(50:95))of 69.8%,respectively,improvements of 4.4%and 7.0%over the baseline.展开更多
The accurate and robust unmanned aerial vehicle(UAV)localization is significant due to the requirements of safety-critical monitoring and emergency wireless communication in hostile underground environments.Existing r...The accurate and robust unmanned aerial vehicle(UAV)localization is significant due to the requirements of safety-critical monitoring and emergency wireless communication in hostile underground environments.Existing range-based localization approaches fundamentally rely on the assumption that the environment is relatively ideal,which enables a precise range for localization.However,radio propagation in the underground environments may be dramatically influenced by various equipments,obstacles,and ambient noises.In this case,inaccurate range measurements and intermittent ranging failures inevitably occur,which leads to severe localization performance degradation.To address the challenges,a novel UAV localization scheme is proposed in this paper,which can effectively handle unreliable observations in hostile underground environments.We first propose an adaptive extended Kalman filter(EKF)based on the fusion of ultra-wideband(UWB)and inertial measurement unit(IMU)to detect and adjust the inaccurate range measurements.Aiming to deal with intermittent ranging failures,we further design the constraint condition by limiting the system state.Specifically,the auto-regressive model is proposed to implement the localization in the ranging blind areas by reconstructing the lost measurements.Finally,extensive simulations have been conducted to verify the effectiveness.We carry out field experiments in an underground garage and a coal mine based on P440 UWB sensors.Results show that the localization accuracy is improved by 16.9%compared with the recent methods in the hostile underground environments.展开更多
Navigation without Global Navigation Satellite Systems(GNSS)poses a significant challenge in aerospace engineering,particularly in the environments where satellite signals are obstructed or unavailable.This paper offe...Navigation without Global Navigation Satellite Systems(GNSS)poses a significant challenge in aerospace engineering,particularly in the environments where satellite signals are obstructed or unavailable.This paper offers an in-depth review of various methods,sensors,and algorithms for Unmanned Aerial Vehicle(UAV)localization in outdoor environments where GNSS signals are unavailable or denied.A key contribution of this study is the establishment of a critical classification system that divides GNSS-denied navigation techniques into two primary categories:absolute and relative localization.This classification enhances the understanding of the strengths and weaknesses of different strategies in various operational contexts.Vision-based localization is identified as the most effective approach in GNSS-denied environments.Nonetheless,it’s clear that no single-sensor-based localization algorithm can fulfill all the needs of a comprehensive navigation system in outdoor environments.Therefore,it’s vital to implement a hybrid strategy that merges various algorithms and sensors for effective outcomes.This detailed analysis emphasizes the challenges and possible solutions for achieving reliable and effective outdoor UAV localization in environments where GNSS is unreliable or unavailable.This multi-faceted analysis,highlights the complexities and potential pathways for achieving efficient and dependable outdoor UAV localization in GNSS-denied environments.展开更多
Purpose-This paper aims to describe a recently proposed algorithm in terrain-based cooperative UAV mapping of the unknown complex obstacle in a stationary environment where the complex obstacles are represented as cur...Purpose-This paper aims to describe a recently proposed algorithm in terrain-based cooperative UAV mapping of the unknown complex obstacle in a stationary environment where the complex obstacles are represented as curved in nature.It also aims to use an extended Kalman filter(EKF)to estimate the fused position of the UAVs and to apply the 2-D splinegon technique to build the map of the complex shaped obstacles.The path of the UAVs are dictated by the Dubins path planning algorithm.The focus is to achieve a guaranteed performance of sensor based mapping of the uncertain environments using multiple UAVs.Design/methodology/approach–An extended Kalman filter is used to estimate the position of the UAVs,and the 2-D splinegon technique is used to build the map of the complex obstacle where the path of the UAVs are dictated by the Dubins path planning algorithm.Findings-The guaranteed performance is quantified by explicit bounds of the position estimate of the multiple UAVs for mapping of the complex obstacles using 2-D splinegon technique.This is a newly proposed algorithm,the most efficient and a robust way in terrain based mapping of the complex obstacles.The proposed method can provide mathematically provable and performance guarantees that are achievable in practice.Originality/value-The paper describes the main contribution in mapping the complex shaped curvilinear objects using the 2-D splinegon technique.This is a new approach where the fused EKF estimated positions are used with the limited number of sensors’measurements in building the map of the complex obstacles.展开更多
基金supported by a grant from the National Natural Science Foundation of China(No.61375082)
文摘The purpose of this research is to improve the robustness of the autonomous system in order to improve the position and velocity estimation of an Unmanned Aerial Vehicle(UAV).Therefore, new integrated SINS/GPS navigation scheme based on Interacting Multiple Nonlinear Fuzzy Adaptive H_∞ Models(IMM-NFAH_∞) filtering technique for UAV is presented. The proposed IMM-NFAH_∞ strategy switches between two different Nonlinear Fuzzy Adaptive H_∞(NFAH_∞) filters and each NFAH_∞ filter is based on different fuzzy logic inference systems. The newly proposed technique takes into consideration the high order Taylor series terms and adapts the nonlinear H_∞ filter based on different fuzzy inference systems via adaptive filter bounds(di),along with disturbance attenuation parameter c. Simulation analysis validates the performance of the proposed algorithm, and the comparison with nonlinear H_∞(NH_∞) filter and that with different NFAH_∞ filters demonstrate the effectiveness of UAV localization utilizing IMM-NFAH_∞ filter.
基金supported by the National Natural Science Foundation of China(No.61702347)Hebei Academy of Sciences Basic Research Operating Fund Project(No.2025PF21)。
文摘To address the challenges of multi-scale differences,complex background interference,and unstable small target positioning in visual inspection of power towers,the existing methods still face issues such as insufficient feature interaction and unstable confidence estimation,which lead to performance degradation in complex backgrounds and occlusion conditions.This paper proposes a precise inspection method for key power tower components using autonomous drone positioning.To this end,this paper makes three key improvements to the you only look once version 11(YOLOv11)framework.First,it constructs C3k2-adaptive multi-receptive field block(C3k2-AMRB),combining multiple dilated convolutions with a reparameterization mechanism to significantly expand the receptive field and enhance multi-scale feature extraction.Second,it designs a hierarchical wavelet interaction unit(HWIU),which leverages high-and low-frequency decomposition and reconstruction of wavelet transform(WT)to achieve cross-scale semantic alignment,enhancing feature discriminability in complex backgrounds.Third,it proposes a distribution-aware confidence refinement head(DACR-Head),which adaptively calibrates classification confidence based on the statistical characteristics of the predicted bounding-box corner distribution,improving the localization stability and accuracy of small targets.Experiments on the inspection of power line assets dataset(InsPLAD)dataset show that the integrated approach achieves a component detection accuracy at intersection over union(IoU)=0.5(CDA_(50))of 88.3%and a component detection robustness(CDR_(50:95))of 69.8%,respectively,improvements of 4.4%and 7.0%over the baseline.
基金supported by the National Natural Science Foundation of China under Grant No.62272462the Natural Science Foundation of Jiangsu Province of China for Distinguished Young Scholars under Grant No.BK20230045the Shenzhen Science and Technology Program under Grant No.JCYJ20230807154300002.
文摘The accurate and robust unmanned aerial vehicle(UAV)localization is significant due to the requirements of safety-critical monitoring and emergency wireless communication in hostile underground environments.Existing range-based localization approaches fundamentally rely on the assumption that the environment is relatively ideal,which enables a precise range for localization.However,radio propagation in the underground environments may be dramatically influenced by various equipments,obstacles,and ambient noises.In this case,inaccurate range measurements and intermittent ranging failures inevitably occur,which leads to severe localization performance degradation.To address the challenges,a novel UAV localization scheme is proposed in this paper,which can effectively handle unreliable observations in hostile underground environments.We first propose an adaptive extended Kalman filter(EKF)based on the fusion of ultra-wideband(UWB)and inertial measurement unit(IMU)to detect and adjust the inaccurate range measurements.Aiming to deal with intermittent ranging failures,we further design the constraint condition by limiting the system state.Specifically,the auto-regressive model is proposed to implement the localization in the ranging blind areas by reconstructing the lost measurements.Finally,extensive simulations have been conducted to verify the effectiveness.We carry out field experiments in an underground garage and a coal mine based on P440 UWB sensors.Results show that the localization accuracy is improved by 16.9%compared with the recent methods in the hostile underground environments.
基金funded by PSDSARC seed project number(PSDSARC Project ID:PID-000085_01_02)the APC was funded by PSU.
文摘Navigation without Global Navigation Satellite Systems(GNSS)poses a significant challenge in aerospace engineering,particularly in the environments where satellite signals are obstructed or unavailable.This paper offers an in-depth review of various methods,sensors,and algorithms for Unmanned Aerial Vehicle(UAV)localization in outdoor environments where GNSS signals are unavailable or denied.A key contribution of this study is the establishment of a critical classification system that divides GNSS-denied navigation techniques into two primary categories:absolute and relative localization.This classification enhances the understanding of the strengths and weaknesses of different strategies in various operational contexts.Vision-based localization is identified as the most effective approach in GNSS-denied environments.Nonetheless,it’s clear that no single-sensor-based localization algorithm can fulfill all the needs of a comprehensive navigation system in outdoor environments.Therefore,it’s vital to implement a hybrid strategy that merges various algorithms and sensors for effective outcomes.This detailed analysis emphasizes the challenges and possible solutions for achieving reliable and effective outdoor UAV localization in environments where GNSS is unreliable or unavailable.This multi-faceted analysis,highlights the complexities and potential pathways for achieving efficient and dependable outdoor UAV localization in GNSS-denied environments.
文摘Purpose-This paper aims to describe a recently proposed algorithm in terrain-based cooperative UAV mapping of the unknown complex obstacle in a stationary environment where the complex obstacles are represented as curved in nature.It also aims to use an extended Kalman filter(EKF)to estimate the fused position of the UAVs and to apply the 2-D splinegon technique to build the map of the complex shaped obstacles.The path of the UAVs are dictated by the Dubins path planning algorithm.The focus is to achieve a guaranteed performance of sensor based mapping of the uncertain environments using multiple UAVs.Design/methodology/approach–An extended Kalman filter is used to estimate the position of the UAVs,and the 2-D splinegon technique is used to build the map of the complex obstacle where the path of the UAVs are dictated by the Dubins path planning algorithm.Findings-The guaranteed performance is quantified by explicit bounds of the position estimate of the multiple UAVs for mapping of the complex obstacles using 2-D splinegon technique.This is a newly proposed algorithm,the most efficient and a robust way in terrain based mapping of the complex obstacles.The proposed method can provide mathematically provable and performance guarantees that are achievable in practice.Originality/value-The paper describes the main contribution in mapping the complex shaped curvilinear objects using the 2-D splinegon technique.This is a new approach where the fused EKF estimated positions are used with the limited number of sensors’measurements in building the map of the complex obstacles.
