The technique of phase measuring profilometry using a single phase step method is proposed.This method can automatically obtain phase value at each pixel by using a discret cosine transform algorithm.The method is abl...The technique of phase measuring profilometry using a single phase step method is proposed.This method can automatically obtain phase value at each pixel by using a discret cosine transform algorithm.The method is able to automatically recognize any position between depression and elevation on an object surface.Theoretical analysis and experimental verification are presented.展开更多
Aiming to address the Unmanned Aerial Vehicle(UAV) formation collision avoidance problem in Three-Dimensional(3-D) low-altitude environments where dense various obstacles exist, a fluid-based path planning framework n...Aiming to address the Unmanned Aerial Vehicle(UAV) formation collision avoidance problem in Three-Dimensional(3-D) low-altitude environments where dense various obstacles exist, a fluid-based path planning framework named the Formation Interfered Fluid Dynamical System(FIFDS) with Moderate Evasive Maneuver Strategy(MEMS) is proposed in this study.First, the UAV formation collision avoidance problem including quantifiable performance indexes is formulated. Second, inspired by the phenomenon of fluids continuously flowing while bypassing objects, the FIFDS for multiple UAVs is presented, which contains a Parallel Streamline Tracking(PST) method for formation keeping and the traditional IFDS for collision avoidance. Third, to rationally balance flight safety and collision avoidance cost, MEMS is proposed to generate moderate evasive maneuvers that match up with collision risks. Comprehensively containing the time and distance safety information, the 3-D dynamic collision regions are modeled for collision prediction. Then, the moderate evasive maneuver principle is refined, which provides criterions of the maneuver amplitude and direction. On this basis, an analytical parameter mapping mechanism is designed to online optimize IFDS parameters. Finally, the performance of the proposed method is validated by comparative simulation results and real flight experiments using fixed-wing UAVs.展开更多
Recent advancements in artificial intelligence have transformed three-dimensional(3D)optical imaging and metrology,enabling high-resolution and high-precision 3D surface geometry measurements from one single fringe pa...Recent advancements in artificial intelligence have transformed three-dimensional(3D)optical imaging and metrology,enabling high-resolution and high-precision 3D surface geometry measurements from one single fringe pattern projection.However,the imaging speed of conventional fringe projection profilometry(FPP)remains limited by the native sensor refresh rates due to the inherent"one-to-one"synchronization mechanism between pattern projection and image acquisition in standard structured light techniques.Here,we present dual-frequency angular-multiplexed fringe projection profilometry(DFAMFPP),a deep learning-enabled 3D imaging technique that achieves high-speed,high-precision,and large-depth-range absolute 3D surface measurements at speeds 16 times faster than the sensor's native frame rate.By encoding multi-timeframe 3D information into a single multiplexed image using multiple pairs of dual-frequency fringes,high-accuracy absolute phase maps are reconstructed using specially trained two-stage number-theoretical-based deep neural networks.We validate the effectiveness of DFAMFPP through dynamic scene measurements,achieving 10,000 Hz 3D imaging of a running turbofan engine prototype with only a 625 Hz camera.By overcoming the sensor hardware bottleneck,DFAMFPP significantly advances high-speed and ultra-high-speed 3D imaging,opening new avenues for exploring dynamic processes across diverse scientific disciplines.展开更多
Nighttime navigation faces challenges from limited data and interference,especially when satellite signals are unavailable.Leveraging lunar polarized light,polarization navigation offers a promising solution for night...Nighttime navigation faces challenges from limited data and interference,especially when satellite signals are unavailable.Leveraging lunar polarized light,polarization navigation offers a promising solution for nighttime autonomous navigation.Current algorithms,however,are limited by the requirement for known horizontal attitudes,restricting applications.This study introduces an autonomous 3-D attitude determination method to overcome this limitation.Our approach utilizes the Angle of Polarization(AOP)at night to extract neutral points from the AOP pattern.This allows for the calculation of polarization meridian plane information for attitude determination.Subsequently,we present an optimized Polarization TRIAD(Pol-TRIAD)algorithm to acquire the 3-D attitude.The proposed method outperforms the existing approaches in outdoor experiments by achieving lower Root Mean Square Error(RMSE).For one baseline attitude,it improves pitch by 31.7%,roll by 21.7%,and yaw by 2.6%,while for the attitude with a larger tilt angle,the improvements are 64.4%,30.4%,and 9.1%,respectively.展开更多
文摘The technique of phase measuring profilometry using a single phase step method is proposed.This method can automatically obtain phase value at each pixel by using a discret cosine transform algorithm.The method is able to automatically recognize any position between depression and elevation on an object surface.Theoretical analysis and experimental verification are presented.
基金supported in part by the National Natural Science Foundations of China(Nos.61175084,61673042 and 62203046)the China Postdoctoral Science Foundation(No.2022M713006).
文摘Aiming to address the Unmanned Aerial Vehicle(UAV) formation collision avoidance problem in Three-Dimensional(3-D) low-altitude environments where dense various obstacles exist, a fluid-based path planning framework named the Formation Interfered Fluid Dynamical System(FIFDS) with Moderate Evasive Maneuver Strategy(MEMS) is proposed in this study.First, the UAV formation collision avoidance problem including quantifiable performance indexes is formulated. Second, inspired by the phenomenon of fluids continuously flowing while bypassing objects, the FIFDS for multiple UAVs is presented, which contains a Parallel Streamline Tracking(PST) method for formation keeping and the traditional IFDS for collision avoidance. Third, to rationally balance flight safety and collision avoidance cost, MEMS is proposed to generate moderate evasive maneuvers that match up with collision risks. Comprehensively containing the time and distance safety information, the 3-D dynamic collision regions are modeled for collision prediction. Then, the moderate evasive maneuver principle is refined, which provides criterions of the maneuver amplitude and direction. On this basis, an analytical parameter mapping mechanism is designed to online optimize IFDS parameters. Finally, the performance of the proposed method is validated by comparative simulation results and real flight experiments using fixed-wing UAVs.
基金supported by National Key Research and Development Program of China(2022YFB2804603,2022YFB2804605)National Natural Science Foundation of China(U21B2033)+4 种基金Fundamental Research Funds forthe Central Universities(2023102001,2024202002)National Key Laborato-ry of Shock Wave and Detonation Physics(JCKYS2024212111)China Post-doctoral Science Fund(2023T160318)Open Research Fund of JiangsuKey Laboratory of Spectral Imaging&Intelligent Sense(JSGP202105,JSGP202201)Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX25_0695,SJCX25_0188)。
文摘Recent advancements in artificial intelligence have transformed three-dimensional(3D)optical imaging and metrology,enabling high-resolution and high-precision 3D surface geometry measurements from one single fringe pattern projection.However,the imaging speed of conventional fringe projection profilometry(FPP)remains limited by the native sensor refresh rates due to the inherent"one-to-one"synchronization mechanism between pattern projection and image acquisition in standard structured light techniques.Here,we present dual-frequency angular-multiplexed fringe projection profilometry(DFAMFPP),a deep learning-enabled 3D imaging technique that achieves high-speed,high-precision,and large-depth-range absolute 3D surface measurements at speeds 16 times faster than the sensor's native frame rate.By encoding multi-timeframe 3D information into a single multiplexed image using multiple pairs of dual-frequency fringes,high-accuracy absolute phase maps are reconstructed using specially trained two-stage number-theoretical-based deep neural networks.We validate the effectiveness of DFAMFPP through dynamic scene measurements,achieving 10,000 Hz 3D imaging of a running turbofan engine prototype with only a 625 Hz camera.By overcoming the sensor hardware bottleneck,DFAMFPP significantly advances high-speed and ultra-high-speed 3D imaging,opening new avenues for exploring dynamic processes across diverse scientific disciplines.
基金supported in part by the National Key Research and Development Program of China(Nos.2020YFA0711200,2022YFB4701301)in part by the Defense Industrial Technology Development Program,China(No.JCKY2021601B016)+1 种基金in part by the Fundamental Research Funds for the Central Universities,China(No.YWF-23-JC-07)in part by the National Natural Science Foundation of China(No.62425302)。
文摘Nighttime navigation faces challenges from limited data and interference,especially when satellite signals are unavailable.Leveraging lunar polarized light,polarization navigation offers a promising solution for nighttime autonomous navigation.Current algorithms,however,are limited by the requirement for known horizontal attitudes,restricting applications.This study introduces an autonomous 3-D attitude determination method to overcome this limitation.Our approach utilizes the Angle of Polarization(AOP)at night to extract neutral points from the AOP pattern.This allows for the calculation of polarization meridian plane information for attitude determination.Subsequently,we present an optimized Polarization TRIAD(Pol-TRIAD)algorithm to acquire the 3-D attitude.The proposed method outperforms the existing approaches in outdoor experiments by achieving lower Root Mean Square Error(RMSE).For one baseline attitude,it improves pitch by 31.7%,roll by 21.7%,and yaw by 2.6%,while for the attitude with a larger tilt angle,the improvements are 64.4%,30.4%,and 9.1%,respectively.
