Cooperative Target Research Articles

Multi-target automatic positioning based on angle and distance parallel measurement

Precisely and efficiently measuring three-dimensional coordinates of key points on large-scale components in the manufacturing process of aircraft and ships is critically essential. This study presents a multi-target automatic positioning method based on rapid angle and distance measurement in parallel. The measurement processes for angles and distances are decoupled and, when executed simultaneously, aims to enhance the measurement efficiency and automation compared with conventional metrology systems. A cooperative target is devised to realize the rotary-laser scanning angle measurement and absolute distance measurement in parallel. The method of multi-target rough positioning based on rotary-laser scanning and then the precise coordinate measurement method introducing absolute distance constraint are detailed. Especially for the distance measurement, we propose a method to determine the internal zero length and compensate for the distance error caused by mirror offset. A real-site experiment is implemented to verify the method's feasibility and demonstrate that the 3D coordinate measurement accuracy is better than 0.17 mm compared with laser tracker.

Cooperative Localization and Target Tracking in Mobile Sensor Networks With Uncertainties in Measurement Models

Cooperative Localization and Target Tracking in Mobile Sensor Networks With Uncertainties in Measurement Models

A Cooperative Target Localization Method Based on UAV Aerial Images

A passive localization algorithm based on UAV aerial images and Angle of Arrival (AOA) is proposed to solve the target passive localization problem. In this paper, the images are captured using fixed-focus shooting. A target localization factor is defined to eliminate the effect of focal length and simplify calculations. To synchronize the positions of multiple UAVs, a dynamic navigation coordinate system is defined with the leader at its center. The target positioning factor is calculated based on image information and azimuth elements within the UAV photoelectric reconnaissance device. The covariance equation is used to derive AOA, which is then used to obtain the target coordinate value by solving the joint UAV swarm positional information. The accuracy of the positioning algorithm is verified by actual aerial images. Based on this, an error model is established, the calculation method of the co-localization PDOP is given, and the correctness of the error model is verified through the simulation of the Monte Carlo statistical method. At the end of the article, the trackless Kalman filter algorithm is designed to improve positioning accuracy, and the simulation analysis is performed on the stationary and moving states of the target. The experimental results show that the algorithm can significantly improve the target positioning accuracy and ensure stable tracking of the target.

An Evolutionary Game-Theoretic Approach to Unmanned Aerial Vehicle Network Target Assignment in Three-Dimensional Scenarios

Target assignment has been a hot topic of research in the academic and industrial communities for swarms of multiple unmanned aerial vehicle (multi-UAVs). Traditional methods mainly focus on cooperative target assignment in planes, and they ignore three-dimensional scenarios for the multi-UAV network target assignment problem. This paper proposes a method for target assignment in three-dimensional scenarios based on evolutionary game theory to achieve cooperative targeting for multi-UAVs, significantly improving operational efficiency and achieving maximum utility. Firstly, we construct an evolutionary game model including game participants, a tactical strategy space, a payoff matrix, and a strategy selection probability space. Then, a multi-level information fusion algorithm is designed to evaluate the overall attack effectiveness of multi-UAVs against multiple targets. The replicator equation is leveraged to obtain the evolutionarily stable strategy (ESS) and dynamically update the optimal strategy. Finally, a typical scenario analysis and an effectiveness experiment are carried out on the RflySim platform to analyze the calculation process and verify the effectiveness of the proposed method. The results show that the proposed method can effectively provide a target assignment solution for multi-UAVs.

Cooperative CAR targeting to selectively eliminate AML and minimize escape

Acute myeloid leukemia (AML) poses a singular challenge for chimeric antigen receptor (CAR) therapy owing to its phenotypic heterogeneity and similarity to normal hematopoietic stem/progenitor cells (HSPCs). Here we expound a CAR strategy intended to efficiently target AML while minimizing HSPC toxicity. Quantification of target expression in relapsed/refractory patient samples and normal HSPCs reveals a therapeutic window for gated co-targeting of ADGRE2 and CLEC12A: We combine an attenuated ADGRE2-CAR with a CLEC12A-chimeric costimulatory receptor (ADCLEC.syn1) to preferentially engage ADGRE2posCLEC12Apos leukemic stem cells over ADGRE2lowCLEC12Aneg normal HSPCs. ADCLEC.syn1 prevents antigen escape in AML xenograft models, outperforms the ADGRE2-CAR alone and eradicates AML despite proximate myelopoiesis in humanized mice. Off-target HSPC toxicity is similar to that of a CD19-CAR and can be mitigated by reducing CAR Tcell-derived interferon-γ. Overall, we demonstrate the ability of target density-adapted cooperative CAR targeting to selectively eliminate AML and potentially obviate the need for hematopoietic rescue.

A localization method of manipulator towards achieving more precision control

AbstractThe monocular vision system is a crucial branch of machine vision research widely used in multiple industries as a research hotspot in the field of vision. Although the monocular vision system is of simple structure and cost‐effectiveness, its positioning accuracy is insufficient in some industries. This article researched the robot arm positioning method via monocular vision. First, we built a vision system model and designed the style of a cooperative target for target positioning. Second, a target feature screening method based on conditions is composed for the existence of interference. Furthermore, combining the principle of pose estimation on the PNP (Perspective‐n‐Point) problem with the results of the visual system calibration to realize the positioning of the target. Finally, complete the construction of the experimental platform and design accuracy evaluation experiments and positioning experiments. The experimental results show that the location measurement error range of the system in this article is below 4 mm, and the measurement error of the rotation angle is below 2. The system can adapt to the requirements of general industrial use.

Inverse-Covariance-Intersection-Based Distributed Estimation and Application in Wireless Sensor Network

In a sensor network, estimation performance may degrade when unknown correlations among local estimates are not addressed carefully. This article presents a novel distributed estimation algorithm based on inverse covariance intersection (ICI) for effectively solving the cross-correlation and dynamic state estimation problems in a wireless sensor network. The intermediate results of a set of consensus filters running in parallel are utilized to realize a global fusion of estimates, which involves all agents' local estimates and improves the estimation accuracy. Meanwhile, the global consistency of the proposed algorithm can be guaranteed theoretically since the fusion process considers all local estimates jointly and in a unified way. Furthermore, besides the asymptotic performance, the boundedness and consistency of the fused estimate can be achieved under finite iterations, which demonstrates its robustness and potential in practical applications. A cooperative target tracking problem illustrates the performance of the proposed algorithm.

Coordinated target localization and tracking with node scheduling for a network of autonomous underwater vehicles

Underwater mobile sensor networks (UMSNs) have the advantages of large area coverage, flexible network structure, and good local resolution. Particularly, spatial diversity can be exploited through node mobility to get more target information. To take those advantages in the context of regional cooperative target localization and tracking, a joint node scheduling and acoustic model fusion method is proposed for an UMSN consisting of multiple autonomous underwater vehicles (AUVs). First, a target tracking algorithm framework based on the random finite set theory is established. To take the acoustic environment into account, the a priori acoustic field and target distribution information are combined to evaluate the detection probability. A path-planning scheme with node scheduling is then developed based on the information gain to optimize the detection performance. To achieve effective node scheduling, three reward functions with different information gains respectively derived from the Rényi divergence and Fisher information matrix are proposed and compared. The rationale and the performance of the developed method are verified via simulations. A simplified version of the approach is also tested in field experiments with three AUVs, verifying the effectiveness of node scheduling in a realistic scenario.

Cooperative Standoff Target Tracking using Multiple Fixed-Wing UAVs with Input Constraints in Unknown Wind

This paper investigates the problem of cooperative standoff tracking using multiple fixed-wing unmanned aerial vehicles (UAVs) with control input constraints. In order to achieve accurate moving target tracking in the presence of unknown background wind, a coordinated standoff target tracking algorithm is proposed. The objective of the research is to steer multiple UAVs to fly a circular orbit around a moving target with prescribed intervehicle angular spacing. To achieve this goal, two control laws are proposed, including relative range regulation and space phase separation. On one hand, a heading rate control law based on a Lyapunov guidance vector field is proposed. The convergence analysis shows that the UAVs can asymptotically converge to a desired circular orbit around the target, regardless of their initial position and heading. Through a rigorous theoretical proof, it is concluded that the command signal of the proposed heading rate controller will not violate the boundary constraint on the heading rate. On the other hand, a temporal phase is introduced to represent the phase separation and avoid discontinuity of the wrapped space phase angle. On this basis, a speed controller is developed to achieve equal phase separation. The proposed airspeed controller meets the requirements of the airspeed constraint. Furthermore, to improve the robustness of the aircraft during target tracking, an estimator is developed to estimate the composition velocity of the unknown wind and target motion. The proposed estimator uses the offset vector between the UAV’s actual flight path and the desired orbit, which is defined by the Lyapunov guidance vector field, to estimate the composition velocity. The stability of the estimator is proved. Simulations are conducted under different scenarios to demonstrate the effectiveness of the proposed cooperative standoff target tracking algorithm. The simulation results indicate that the temporal-phase-based speed controller can achieve a fast convergence speed and small phase separation error. Additionally, the composition velocity estimator exhibits a fast response speed and high estimation accuracy.

Research on the Cooperative Target State Estimation and Tracking Optimization Method of Multi-UUV.

This work studied two sub-problems of the cooperative state estimation and cooperative optimization of tracking paths in multiple unmanned underwater vehicle (multi-UUV) cooperative target tracking. The mathematical model of each component of the multi-UUV cooperative target tracking system was established. According to the target bearing-only information obtained by each unmanned underwater vehicle's (UUV) detection, the extended Kalman filter algorithm based on interacting with multiple model bearing-only data was used to estimate the target state in a distributed way, and the federal fusion algorithm was used to fuse the estimated results of each UUV. The fused target state was predicted, and, based on the predicted target state, to achieve the persistent tracking of the target, the particle swarm optimization algorithm was used for the online collaborative optimization of the UUV tracking path. The simulation results showed that the multi-UUV distributed fusion filtering algorithm could obtain a better target state estimation effect, and the online path collaborative optimization method based on the prediction of the target state could achieve persistent target tracking.

Model-guided Reinforcement Learning Enclosing for UAVs with Collision-free and Reinforced Tracking Capability

Enclosing a maneuverable target for Unmanned Aerial Vehicles (UAVs) in a constrained environment is intractable as existing methods fail to coordinate collision safety and tracking. This paper aims to propose a model-guided reinforcement learning (RL) enclosing algorithm for UAVs to approximate a cooperative target within a fixed time, where collision-free behaviors are guaranteed and reinforced tracking capability is attained. Firstly, a polar frame-based fixed-time enclosing controller is contrived to enforce UAVs to reach the target circles within a prescribed time. Secondly, by interpreting disturbance rejection and collision avoidance constraints as two skilled reward functions, deep deterministic policy gradient (DDPG) is employed to generate the supplement component, which can enhance tracking performance and learn collision avoidance skills. Furthermore, a novel curriculum study training procedure that transfers the past learned policy to the current episode is introduced, which results in faster convergence rate and fewer numbers of sampling data collections than classical random parameter settings. The remarkable innovation lies in that an optimal intelligent enclosing policy is formulated to realize a good balance between collision avoidance and enclosing performances. Moreover, by utilizing a model-based control to generate confidential experienced data, unnecessary explorations can be reduced effectively, while a better generalization ability can be assured. Finally, abundant simulations are offered to substantiate the utility and superiorities of developed algorithm.

한·튀르키예 경제협력 확대 방안에 관한 연구

Purpose - Despite friendly diplomatic relations between Korea and Turkey, relatively little attention has been paid to economic cooperation. This research aims to find ways to expand future-oriented economic cooperation between the two countries based on the demand for cooperation in each industry.
 Design/Methodology/Approach - In order to examine the possibility of cooperation with Turkey, the industrial structure of Turkey and related policies of were analyzed. In addition, the current status and direction of each industry in Turkey, along with foreign economic cooperation in Turkey, including Korea, were examined.
 Findings - First, technology compatibility and standardization can be realized by participating as a collaboration partner from the infrastructure establishment stage of the industry fostered by the Turkish government. Second, it is necessary to expand cooperation targets to state-owned enterprises and local conglomerates with monopolistic market power. Third, it is necessary to diversify cooperation methods by industry and growth stage because there is a large difference in the degree of development of each industry in Turkey.
 Research Implications - In order to deepen Korea-Turkey economic cooperation, it is essential to respond promptly to the actual demands for cooperation in each industry. However, since it is difficult to identify such demands at the level of individual companies, a cooperation system involving government departments, relevant public organizations, and major companies in each industry of the two countries needs to be built.

A monocular visual single-axis rotation measurement method of multi-rudders based on cooperative targets

To tackle the problem that the rotation angle of multiple rudders needs to be measured in real time and high precision, a measurement method based on cooperative targets is proposed in this paper. Firstly, to detect and encode multiple similar rudders with sparse feature points in complex backgrounds, we design a novel visual cooperative target with encoding information, and study a cooperative target localization algorithm based on contour extraction as well as a corner point extraction algorithm based on a checkerboard grid. Then, through the PnP algorithms and numerous corner points extracted from the cooperative target, an unconstrained optimization model is developed based on the re-projection error, and the Levenberg-Marquard (LM) algorithm is applied to estimate the rotation angle. Finally, an experiment platform is set up to evaluate the accuracy and speed of the algorithm. Compared with the existing methods, the experimental results show that the proposed cooperative target exhibits favorable localization under various typical experimental environments with a detection success rate better than 98%, meanwhile, the actual measurement accuracy of the angle estimation model is better than 0.05° and the average execution time of each measurement is less than 20 ms. Moreover, the proposed method is easy to install and operate in actual measurement, and can achieve simultaneous measurement of the spatial rotation angle of multiple rudders, which is well suited to actual industrial production needs.

System error estimation for sensor network with integrated sensing and communication application

The integrated sensing and communication base station can perform both communication and sensing tasks simultaneously. The key premise of accurate localization and tracking of targets is sensor registration process. If system errors of multi-sensor system are not corrected properly, it can lead to significant sensing errors and reduce the overall system performance. In this paper, we propose a system error estimation method for a 3-dimensional asynchronous base station network to achieve satisfactory localization and tracking performance. Specifically, by making use of the priori information of the cooperative targets, we propose to minimize the sum of squares of the position mismatch errors of the targets, this leads to a nonlinear error estimation model for the system errors. To handle the high nonlinearity and ensure fast convergence of online registration, an efficient inexact block coordinate descent optimization algorithm incorporating the proximal method is proposed. The algorithm divides the estimation problem into a linear least square subproblem and several quadratic constrained quadratic program subproblems, enabling different kinds of system errors to be alternatively updated. Finally, the effectiveness and feasibility of the proposed estimation algorithm are demonstrated through numerical simulation.

Observability-Enhancement Boresight Calibration of Camera-IMU System: Theory and Experiments

Airborne target tracking with vision sensors is one of the indispensable and critical components in unmanned aerial vehicles (UAVs). It is critical to calibrate the misalignment angles between the onboard camera and Inertial Measurement Unit (IMU) to improve the accuracy of target tracking. In this paper, the observability of boresight misalignment angle estimation using a cooperative target is first analyzed. Based on the observability Gramian, an optimization criterion for maximizing the degree of observability is determined, and the optimal geometries between the UAV and the target are derived. Taking the physical constraints of the UAV into consideration, an online trajectory optimization approach for a UAV with a vision sensor is proposed to achieve in-flight calibration of the misalignment angles. Both numerical simulations and experiments are conducted to validate the proposed method.

Integrated Design of Cooperative Area Coverage and Target Tracking with Multi-UAV System

Integrated Design of Cooperative Area Coverage and Target Tracking with Multi-UAV System

Open-path sensor for atmospheric NO detection based on mid-infrared laser absorption spectroscopy

Nitric oxide (NO) is one of the primary air pollutants, and open optical path detection of NO holds immense significance in safeguarding the ecological environment and human health. In this paper, a high precision and high sensitivity NO open optical path sensor based on mid-infrared laser absorption spectroscopy technology is developed. The average gas concentration inversion of the NO path is achieved by collecting the backscattered light from the cooperative target. The gas measurement deploys a wavelength modulation method to remove the effects from light intensity variations. Furthermore, a Kalman filter and a temperature–pressure sensor optimization software algorithm were utilized to reduce the effects of temperature–pressure variations during the measurement process, leading to a 33% improvement in detection accuracy. The performance of the sensor was evaluated in the laboratory environment in terms of accuracy, sensitivity, and detection limit. The atmospheric NO concentration was also measured under varying time periods and weather conditions in the field environment, thus laying a strong foundation for studying the spatial and temporal evolution of NO in the atmosphere.

Space Relative Position and Attitude Measurement Method Based on Solid State Lidar and High Precision Cooperative Target

With the continuous advancement and upgrading of human space exploration, the complexity and difficulty of space missions are increasing. Space manipulation, rendezvous and docking, and situation awareness of non cooperative targets have become important components of space missions. Relative position and attitude navigation system is an important part of space rendezvous and docking and space manipulation system. At present, the method of matching with cooperative target is mainly used for high-precision relative position and attitude measurement.This paper introduces a method of matching solid-state lidar with high-precision cooperative target for attitude determination. First, a design method of three-dimensional cooperative target is carried out, and then a mathematical model of target recognition is constructed. Three criteria for recognition and matching are proposed. In the third part of the article, the specific process of this method is introduced, and experimental verification is carried out. The experiment shows that the method can achieve the position measurement accuracy of 0.006m and the attitude measurement accuracy of 0.15 ° , and has important application value in various high-precision space missions.

MUTS-Based Cooperative Target Stalking for A Multi-USV System

This paper is concerned with the cooperative target stalking for a multi-unmanned surface vehicle (multi-USV) system. Based on the multi-agent deep deterministic policy gradient (MADDPG) algorithm, a multi-USV target stalking (MUTS) algorithm is proposed. Firstly, a V-type probabilistic data extraction method is proposed for the first time to overcome shortcomings of the MADDPG algorithm. The advantages of the proposed method are twofold: 1) it can reduce the amount of data and shorten training time; 2) it can filter out more important data in the experience buffer for training. Secondly, in order to avoid the collisions of USVs during the stalking process, an action constraint method called Safe DDPG is introduced. Finally, the MUTS algorithm and some existing algorithms are compared in cooperative target stalking scenarios. In order to demonstrate the effectiveness of the proposed MUTS algorithm in stalking tasks, mission operating scenarios and reward functions are well designed in this paper. The proposed MUTS algorithm can help the multi-USV system avoid internal collisions during the mission execution. Moreover, compared with some existing algorithms, the newly proposed one can provide a higher convergence speed and a narrower convergence domain.

Multi-UAV Cooperative Search Based on Reinforcement Learning With a Digital Twin Driven Training Framework

This paper considers the cooperative search for stationary targets by multiple unmanned aerial vehicles (UAVs) with limited sensing range and communication ability in a dynamic threatening environment. The main purpose is to use multiple UAVs to find more unknown targets as soon as possible, increase the coverage rate of the mission area, and more importantly, guide UAVs away from threats. However, traditional search methods are mostly unscalable and perform poorly in dynamic environments. A new multi-agent deep reinforcement learning (MADRL) method, DNQMIX, is proposed in this study to solve the multi-UAV cooperative target search (MCTS) problem. The reward function is also newly designed for the MCTS problem to guide UAVs to explore and exploit the environment information more efficiently. Moreover, this paper proposes a digital twin (DT) driven training framework “centralized training, decentralized execution, and continuous evolution” (CTDECE). It can facilitate the continuous evolution of MADRL models and solve the tradeoff between training speed and environment fidelity when MADRL is applied to real-world multi-UAV systems. Simulation results show that DNQMIX outperforms state-of-art methods in terms of search rate and coverage rate.