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    31 December 2023, Volume 44 Issue S2
    Electronic edition of this issue
    Electronic edition of this issue
    2023, 44(S2):  0. 
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    Contents
    Contents
    2023, 44(S2):  0. 
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    Key Technologies and Application Prospects for High-definition Map in Off-road Environments
    LI Zhaodong, ZHAO Xijun, YANG Tingting, QI Xiaolong, ZHOU Changyi, ZHANG Liming
    2023, 44(S2):  1-11.  doi:10.12382/bgxb.2023.0854
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    With the development of technologies such as artificial intelligence, the unmanned driving technology has emerged as the times require, and more and more unmanned equipment has also been put into combat applications. In the complex off-road environments, a high-definition map can provide rich prior information for unmanned ground vehicles, assist the unmanned ground vehicles in environmental perception, path planning and decision-making, and improve the off-road mobility of unmanned ground vehicles. This paper analyzes the research status of the standardization, construction and application of high-precision map, puts forward the research objectives and technical systems of high-precision map in off-road environments for the autonomous maneuvering tasks of unmanned vehicles in off-road environments, summarizes the basic theories and key technologies of high-precision map in off-road environments, and looks forward to the application and development of high-precision map in off-road environments, which provides a reference for the convenient application of high-precision map in unmanned driving.

    Anti-disturbance Composite Controller Design of Quadruped Robot Based on Extended State Observer and Model Predictive Control Technique
    XU Peng, XING Boyang, LIU Yufei, LI Yongyao, ZENG Yi, ZHENG Dongdong
    2023, 44(S2):  12-21.  doi:10.12382/bgxb.2023.0962
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    A novel composite control algorithm that combines the extended state observer, quadratic programming and model predictive control is proposed to improve the control performance of quadruped robot under model uncertainty and external disturbances. A model predictive controller is proposed for a quadruped robot based on the single rigid body model,and a variable bandwidth nonlinear extended state observer is developed to estimate the lumped disturbance, including the model mismatch and external forces. Based on the estimated result, a compensator is further constructed using the quadratic programming technique.The proposed control algorithm is validated through simulation. The simulated results demonstrate that the anti-disturbance composite controller is used to allow the robot to achieve the satisfactory control performance under the conditions of the changes in mass and the application of external forces. In comparison to existing studies, the proposed controller exhibits significant improvements in control accuracy and disturbance rejection capabilities.

    Relative Positioning Technology for Two Points Based on Cluster Cooperative Orientation
    DENG Tingxiang, REN Peng, CHENG Jia, WANG Jianbing, LIANG Zhenjie, XIANG Zheng
    2023, 44(S2):  22-34.  doi:10.12382/bgxb.2023.0829
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    Precise inter-node position acquisition is the basis for coordination in unmanned vehicle clusters, but the global navigation satellite system (GNSS) struggles to provide the stable and accurate position information in complex and dynamic application environments. It is difficult to deploy the auxiliary anchor points, and most traditional relative positioning methods have limitations on the number of nodes. A new relative localization method for cluster nodes under GNSS-denied conditions is proposed to address the above three problems. Taking two nodes equipped with inertial measurement units (IMUs) and ultra-wideband sensors as an example, a relative positioning model is established, and an extended Kalman filter algorithm is used to fuse IMU and inter-node distance information for the optimal estimation of relative position. Simulation experiments show that the proposed method is used to achieve a relative positioning accuracy of about 1.3m within 200m range between unmanned aerial vehicles, improving nearly 4 times over existing multi-node relative positioning algorithms. The high-precision relative positioning between two nodes can be achieved without relying on GNSS or auxiliary anchor points, providing an effective and feasible solution for collaborative positioning of unmanned vehicle clusters in complex application environment.

    A Simulation System for Cooperative Control of Autonomous Convoy
    SU Zhibao, XIANG Shen, YU Xuewei, AN Xuyang
    2023, 44(S2):  35-43.  doi:10.12382/bgxb.2023.0849
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    Autonomous convoy has the potential to be widely used in many fields, and simulation is an effective and safe method to research the cooperative control technology of autonomous convoy. An autonomous convoy simulation system solution is presented by analyzing the requirements for the autonomous convoy, and the basic structure and function of the main software are described. The following control and multiple vehicle cooperative control strategies are designed and validated for a leader-follower style three-vehicles maneuver mission, and the usuability of the proposed simulation system is demonstrated. At last, the advantages of the simulation system and its research directions are summarized.

    Adaptive Inter-vechile Distance Control for Unmanned Ground Vehicle Convoy
    ZHAO Xijun, CUI Xing, LI Zhaodong, WANG Yiquan, YANG Yu
    2023, 44(S2):  44-51.  doi:10.12382/bgxb.2023.0878
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    Formation maneuver is one of the important functions of autonomous navigation technology for unmanned ground platforms.A novel velocity-adaptive inter-vehchiled istance control method is proposed for unmanned ground vehicle (UGV) convoy, and a cascaded adaptive inter-vehchile distance control diagram is introduced,which includes following model, inter-distance control and velocity planning. A modified nonlinear following model which considers relative velocity error is established based on the traditional time-headway policy. Then a self-tuned PID controller is calculated using the weighed combination of distance error and velocity error. The control performance is validated through simulation and field test. The results show that the proposed inter-distance control method is adaptive to velocity change, and improves the overall control performance of UGV convoy in unstructured terrain.

    Collaborative Route Planning of Multiple Unmanned Aerial Vehicles Considering Task Threats Based on Improved Grasshopper Optimization Algorithm
    GUO Zhiming, LOU Wenzhong, LI Tao, ZHANG Mengyu, BAI Zilong, QIAO Hu
    2023, 44(S2):  52-60.  doi:10.12382/bgxb.2023.0937
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    To enable multiple unmanned aerial vehicles (UAVs)to efficiently execute tasks when facing varying degrees of mission threat environments, a collaborative route planning algorithm of UAVs based on improved grasshopper optimization algorithm is proposed. A route planning model is established by taking the comprehensive cost as an objective function. The grasshopper optimization algorithm is improved by introducing a nonlinear descent strategy based on the logistic function. The feasibility of the improved grasshopper optimization algorithm is verified through simulation experiment. The experimental results showed that the improved grasshopper optimization algorithm has faster convergence speed and global search ability, which can provide support for improving the combat effectiveness of unmanned aerial vehicles.

    Two-wheeled Power-assist Trailer Payload Based on Force Control
    MU Lindong, ZHAO Xinlei, XU Peng, QIU Tianqi, JIANG Lei
    2023, 44(S2):  61-70.  doi:10.12382/bgxb.2023.0898
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    A configuration of a two-wheeled power-assist trailer payload, which is towed by a quadruped bionic robot,is proposed to enhance the transportation capacity of quadruped bionic robots. This design enables increased transportation capacity while maintaining overall flexibility, maneuverability, and off-road capabilities. The kinematics and dynamics characteristics of the two-wheeled power-assist trailer payload is analyzed, a dynamic simulation model is developed, and a force-based assist control algorithm is proposed. A physical prototype of two-wheeled trailer payload is designed based on the proposed algorithm,and utilized for algorithm verification. The result shows that the two-wheeled power-assist trailer payload can effectively respond to the motion state of quadruped bionic robot under full load and exhibits significant off-road capability, thereby greatly expanding its transportation capacity.

    Design and Optimization of a Parallel Elastic Actuator Leg for Quadruped Robots
    LIU Siyu, LIAO Junbei, LEI Fei, WANG Zhirui, YAN Tong, DANG Ruina, GUO Zhao
    2023, 44(S2):  71-83.  doi:10.12382/bgxb.2023.0897
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    A parallel elastic actuator (PEA) leg is proposed to improve the motion performance of quadruped robots. By imitating the tendon-driven mechanism of quadruped animals, a tension spring is connected in parallel between the calf link and the thigh link to achieve parallel elastic effect at the knee joint. The PEA leg exhibits small size, low mass, and low inertia characteristics. A dynamics model of PEA knee joint is established. The proposed model is used for a multi-objective optimization of spring stiffness in considering peak torque, peak power, and energy efficiency in a predefined trajectory tracking task. The most optimal spring stiffness is determined to be 5510N/m. The performances of PEA joint and rigid actuator (RA) joint are compared through simulation and prototype experiments. A prismatic joint with stiffness is employed to simulate the effect of PEA tension spring in the Gazebo robot simulation environment. The output torque, power, and mechanical energy consumption of knee motor are obtained through physics engine simulation. A PEA knee joint experimental platform is constructed, and the prototype experiments are conducted to obtain the electrical parameters of the knee joint motor, such as input current, input power, and electrical energy consumption. The experimental results demonstrate that, compared to RA joint, the PEA joint achieves significant improvements. Specifically, within the trajectory period from 0.5s to 2.0s, the PEA joint reduces peak torque by 40% to 79%, peak power by 52% to 89%, and mechanical energy consumption by 40% to 89% in terms of motor output. Regarding motor input, the PEA joint reduces peak current by 46% to 77%, peak power by 43% to 76%, and electrical energy consumption by 62% to 73%.

    The Transformation of Quadruped and Biped Robot Technologies under the New Technological Situation
    JIANG Chenxing, YAO Qichang, XU Peng, ZHOU Yuting, YAN Tong
    2023, 44(S2):  84-89.  doi:10.12382/bgxb.2023.0888
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    The current state of quadruped and bipedrobot technology in China is discussedin terms of scientific research thinking and behavioral patterns. Additionally, it highlights the disparity between this technology and that of Boston Dynamics. From the perspective of scientific research paradigm, the core technology and development direction of quadruped and biped robots shift overtaking driven by new technology are analyzed, the opportunities faced in electric drive joint and legged robot control at home are summarized, and a design method of AI-driven legged robot system is proposed.

    Control Barrier Functions-based Trajectory Planning for Unmanned Ground Vehicles in Unknown Environment
    FANG Qiuyu, ZHANG Yunlin, MA Zhuangzhuang, SHAO Jinliang
    2023, 44(S2):  90-102.  doi:10.12382/bgxb.2023.0882
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    The ummaned ground vehicles (UGVs)have become a practical alternative to humans in performing tasks in high-risk, high-pollution are as due to their strong adaptability and low cost. As a key technology of mission system for UGV, the trajectory planning is to develop a motion trajectory that meets the constraints based on mission objectives. However, the current navigation controllers rely primarily on the pre-acquired maps and prior knowledge, and are lacking in the ability to cope with unknown environments, making UGVs unable to adapt to complex and changing task environments. Therefore, a trajectory planning method based on the control barrier function (CBF) is proposed to solve the autonomous obstacle avoidance and trajectory planning problems of UGVs in unknown environments. The front end of the method utilizes a laser radar to perceive environmental depth information, while its back end uses a least squares support vector machine (LSSVM) to fit the boundary of obstacles to estimate the control barrier function and compensate for negative sample misclassification. Finally, the safety control instruction is obtained by solving a quadratic programming problem. A segmented detection and elimination method is proposed to address the issue of deadlocks that may occur when UGVs come to a standstill. The results of numerical simulation experiments and semi-physical experiments show that the proposed method has good obstacle avoidance and trajectory planning capabilities in various obstacle environments, and is superior to traditional control methods in terms of path length and other aspects.

    Multi-agent Coverage Path Planning Based on Security Reinforcement Learning
    LI Song, MA Zhuangzhuang, ZHANG Yunlin, SHAO Jinliang
    2023, 44(S2):  101-113.  doi:10.12382/bgxb.2023.0881
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    The purpose of coverage path planning is to find a safe path for an agent, which can not only effectively cover the task area, but also avoid obstacles and neighboring agents. Complex and large task areas are always unavoidable when the coverage tasks are performed, so it is worth exploring how to ensure the safety of agents and enhance the collaboration between agents to improve the task efficiency and capacity of cluster. Therefore, a discrete coverage path planning mathematical model is established using raster maps, a secure multi-agent reinforcement learning algorithm based on value decomposition network is proposed, and its reasonableness is theoretically demonstrated. The proposed algorithm helps to strengthen the learning of collaborative coverage strategies among the agents by decomposing the group value function to avoid the false rewards of the agents, thus improving the convergence speed of the algorithm. The safety of the agent during an entire task is guaranteed by introducing a shield in the training process to correct the behaviors of the agent, such as out-of-bounds and collision. The simulated and semi-physical experiment results show that the algorithm can not only ensure the coverage efficiency of the agents, but also effectively maintain the safety of the agents.

    Formation Reconfiguration Control of Multiple Mobile Robots with Severe Actuator Faults Based on GWO-WOA
    JU Shuang, WANG Jing, WANG Hao, ZHOU Meng
    2023, 44(S2):  114-125.  doi:10.12382/bgxb.2023.0880
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    A collaborative formation reconfiguration control strategy based on Grey Wolf Optimizer-Whale optimization algorithm (GWO-WOA) is proposed to solve the formation reconfiguration control problem of multi-robot systems with severe actuator failures. A fault observer is designed to detect the severe actuator failures in a multi-robot system, and cause the robots with severe actuator failures to leave the formation. Hungarian algorithm is used to allocate the positions of the remaining robots in the expected reconfiguration formation, and the GWO-WOA is used to plan the robots’ path. A collaborative formation reconfiguration control strategy is proposed, which includes three parts: consensus-based formation maintenance control, potential-energy-function-based collision avoidance control, and proportional-integral based tracking controller, enabling multiple robots to achieve formation reconfiguration without collision. The simulation experimental results show that the proposed formation reconfiguration control strategy can be used to monitor the faulty robots in real time and effectively prevent the robots from colliding with each other while forming a desired reconfiguration formation.

    Simulation of Reinforcement Learning-based UAV Swarm Adversarial Strategy Deduction
    CAO Zijian, SUN Zelong, YAN Guochuang, FU Yanfang, YANG Bo, LI Qinjie, LEI Kailin, GAO Linghang
    2023, 44(S2):  126-134.  doi:10.12382/bgxb.2023.0877
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    The application of drone clusters in military warfare, public safety, and commercial fields is becoming increasingly widespread. But it is a challenge to develop the efficient strategiesin complex and ever-changing adversarial environments. In order to enable the drone clusters to autonomously learn and adapt to the change in adversarial environment, and improve the efficiency and success rate of task execution, a multi-agent reinforcement learning algorithm framework based on value decomposition is proposed. The behavior of drone clusters in different adversarial scenarios is simulated on a simulation platform, and the ability of drone clusters to make decisions in different situations is cultivated to achieve the optimal task objectives through reinforcement learning algorithms. The application and performance comparison of different reinforcement learning algorithms in drone swarm adversarial strategies are discussed. The experimental results show that the proposed algorithm shows good performance in various cluster confrontation environments, demonstrating its strong support in military drone cluster confrontation.

    Specific Complex Locomotion Skills Control for Quadruped Robots
    XU Peng, ZHAO Jianxin, FAN Wenhui, QIU Tianqi, JIANG Lei, LIANG Zhenjie, LIU Yufei
    2023, 44(S2):  135-145.  doi:10.12382/bgxb.2023.0874
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    A complex locomotion behavior control method is proposedto improve the locomotion diversity and terrain adaptability of quadruped robot. A dynamics model for the quadruped robots is established, and then the offline rolling optimization is predicted to generate the desired trajectory of robot’s complex locomotion behavior. The locomotiontrajectoriesof robot under more comprehensive nonlinear constrains, such as kinematics, joint torque, contact force, locomotion state, and terrain height,etc, are optimized. An online trajectory tracker and a foot placement hopping controller are designed to realize the online control of the quadruped robot. The proposed method is evaluated in dynamic simulation environment of multi complex locomotion. The robot can achieve front jump, backflip, forward flip and rotary jump, and can also jump over obstacles according to the given terrain information. Finally, the online trajectory tracking controller is migrated to the physical prototype of the quadruped robot, and the forward jumping test of the quadruped robot is completed. The experimental results show that the proposed method can be used effectively to achieve the stable control of various specific complex locomotion motion skills for the quadruped robot.

    A Swarm Intelligence Algorithm for UAV Path Planning in Urban Warfare
    LU Ying, PANG Lichen, CHEN Yusi, SONG Wanying, FU Yanfang
    2023, 44(S2):  146-156.  doi:10.12382/bgxb.2023.0869
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    For the traditional path planning algorithm used to solve the tactical path planning problem of unmanned aerial vehicle, there are some problems, such as local optimum and slow convergence. In this paper, an improved grey wolf algorithm is proposed, which changes the updating mode of the initial population, improves the global optimization ability of grey wolf algorithm and speeds up its convergence speed. The Lévy flight random strategy and symbiotic search algorithm are introduced. The Lévy flight strategy isused to update the population individuals, and the symbiotic search algorithm is usedto avoid the local optimal problem. Threat modeling is an important prerequisite for UAV path planning, and the equivalent preprocessing of the threats is performed,and the algorithm is verifiedby combining the fitness function. And a simulation verification platform, incorporating virtual-to-real mapping, was designed to validate the algorithm’s effectiveness through a real-to-virtual approach. The experimental results show that the improved algorithm effectively improves the path planning problems such as local optimum and slow convergence of the traditional path planning algorithm, and has a certain reference value for the improvement of UAV combat capability.

    Multi-UAV Cooperative Navigation Method Based on Fusion of GNSS/INS/VNS Positioning Information
    CAO Zhengyang, ZHANG Bing, BAI Yixuan, GOU Kenan
    2023, 44(S2):  157-166.  doi:10.12382/bgxb.2023.0860
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    In the informationized battlefield, the unmanned aerial vehicles (UAVs) face various potential threats, including intermittent unintentional interference that disrupts the satellite signals and communication links of unmanned aircraft systems (UAS), leading to adverse effects on flight. To address this challenge, the multi-sensor data is utilized, and a joint filter is established usingthe global navigation satellite system (GNSS) and inertial navigation system (INS) combination navigation system as the main filter, and the global positioning system (GPS) and visual navigation system (VNS) as sub-filters. This approach fuses the relative navigation information from multiple UAVs’ digital image maps with the absolute navigation information acquired by each UAV platform, creating a Kalman filter-based multi-landmark relay-assisted navigation algorithm. This effectively enhances the solution accuracy of GNSS/INS relative navigation systems, reduces the computational burden on multi-UAVs, and expands the cruising range of UAVs. Additionally, a parallel distributed system framework is used to deploy the algorithm on multiple UAV platforms and facilitatethe information sharing and interaction among UAVs, thereby achieving the collaborative perception and autonomous positioning of multi-UAVs. The experiments conducted in simulated mission scenarios demonstrate that this approach meets the precision requirements, achieving an average positional estimation error of 0.66 mand maintaining the velocity estimation accuracy within ±0.4m/s in the collaborative navigation of three UAVs.

    A Real-time Compression Algorithm of Color Point Cloud Streams for Environmental Scanning
    MA Jingqi, YU Qiwen, HUANG Ping, WANG Wei, LI Youwei
    2023, 44(S2):  167-177.  doi:10.12382/bgxb.2023.0856
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    The problems of compressing and encoding the large-scale and high-dimensional point cloud data to improve transmission efficiency arise with the continuous development of digital twin technology. However, most point cloud coding methods have weak real-time compression, low compression efficiency, and high requirement of point cloud format. A real-time color stream Draco (RCS-Draco) compression algorithm based on Google Draco geometric compression library is proposed to solve these problems. By integrating the algorithm into the ROS framework, the point cloud stream is encoded and decoded in real time by means of ROS message flow, which improves the real-time performance of the algorithm. An optimal clipping model is established to clip and filter the point cloud, and remove the drift and outlier point cloud, thus improving the compression efficiency of the algorithm. The RGB color information of point cloud is encoded by establishing a quantitative prediction model, which solves the problem that most point cloud compression algorithms cannot process color information. By adjusting the compression grade and quantization parameters, it is proved that the average compression rate of RCS-Draco algorithm can reach 77%, the average compression and decompression time is less than 0.035s, the average position error is less than 0.05m, and the average attribute error is less than 35. The RCS-Draco algorithm is superior to Draco algorithm in every index through the integration test. The experimental results show that the RCS-Draco compression algorithm performs well in terms of real-time compression, efficiency and point cloud format, and can effectively improve transmission efficiency.

    Multi-robot Cooperative Formation Based on Distributed Model Predictive Control
    LI Caoyan, GUO Zhenchuan, ZHENG Dongdong, WEI Yanling
    2023, 44(S2):  178-190.  doi:10.12382/bgxb.2023.0851
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    Multi-robot cooperative system has strong robustness and fault tolerance, which can greatly improve the overall efficiency and complete the complex tasks. At present, the multi-robot formation often adopts a centralized architecture, which relies on the central decision-making module. In particular, there are problems of insufficient scalability and low solvability when dealing with the collaborative tasks of a large number of robots. A distributed model predictive controller (DMPC) based on leader-follower method is proposed to deal with multi-robot cooperative formation tasks. The robot motion and system communication are modeled based on kinematics and graph network. The trajectory tracking and formation keeping tasks in the formation problem are decomposed, and the model predictive controllers are designed for the leader and followers, respectively. A formation matrix is designed and combined with the communication graph network to achieve consensus or formation control. Independent decision-making and parallel computing of each robot show better accuracy and scalability for the collaborative formation of a large number of robots. At the same time, the design of the controller also takes into account the change of control input, which helps to reduce energy consumption. Numerical simulation and scheme comparison are designed, and the effectiveness of the designed control strategy is verified by physical simulation experiment.

    Departure and Landing Technologies of Shipborne Antisubmarine Helicopter Based on the U.S. Army Amphibious Assault Ship
    WANG Guan, WEI Ning, GUO Yu
    2023, 44(S2):  199-208.  doi:10.12382/bgxb.2023.0812
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    Based on the technological development trend of the stowage and release process of U.S. amphibious assault shipborne anti-submarine helicopter, its related programs and processes are analyzed, centering on the stowage and release process technology of U.S. amphibious assault ship. A number of programs for the shipborne helicopters of LHA and LHD ships, to depart from the ship for takeoff, embarkation and land on board, as well as the emergency land on board are studied. The mining of autonomous applicability technology is aimed at refining the off-ship takeoff, land on board, and emergency land on board methods and routes. The research results show that the accident rate of off-ship takeoffs can be controlled at less than 10‱ and the average accident rate is 5‱ under the visual flight weather condition or instrument flight weather condition in 100000 sorties off-ship simulations according to U.S. shipborne helicopter off-ship takeoff programs Ⅰ, Ⅱ and Ⅲ, In 100000 sorties off-ship simulation according to U.S. naval helicopter land on board program Ⅰ, Ⅱ, Ⅲ, the accident rate of land on board process can be controlled below 25‱; the accident rate of land on board process of the land on board program Ⅰ can be controlled to 5‱ or less; the accident rate of land on board process of the land on board program Ⅱ can be controlled to 15‱ or less; the accident rate of land on board process of the land on board program Ⅲ can be controlled to 20‱ or less; and the accident rate of land on board process of emergency land on board program can be controlled to 25‱ or less.

    A Digital Twin System Model of Unmanned Cooperative Game
    LI Zhengjun, DENG Changming
    2023, 44(S2):  209-222.  doi:10.12382/bgxb.2023.0794
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    Based on the background of the rapid development of unmanned cooperative game and approaching to the actual combat, a digital twin system model of unmanned cooperative game is constructed by combining the digital twin technology, and a system architecture of the unmanned intelligent game is built, so as to promote the research and practice of unmanned cooperative game twin simulation. The relevant war cases are inductively analyzed based on KJ method, and then future war styles are deduced, and a deductive reasoning is carried out on the elements and organisational relationships of the unmanned cooperative gaming system.The game matrix, game function and optimization equation matrix of unmanned cooperative game are constructed to establish an optimization model of unmanned cooperative game situational awareness and game scheme, for achieving the global optimum of the stage gaming scheme.Combined with the digital twin technology, a digital twin structure model is constructed from the physical layer, virtual layer, service layer, data layer and data connection. and the technology system of this model is sorted out. It provides a theoretical framework for the real-virtual synchronisation, interactive operation, visual interface and intelligent decision-making of unmanned cooperative game, and provides a reference for the construction of unmanned cooperative game system in the future.

    Fully Distributed Consensus Control of General Linear Multi-agent System Based on Dynamic Event-trigger
    ZHANG Jixiong, LI Zonggang, NING Xiaogang, CHEN Yinjuan
    2023, 44(S2):  223-234.  doi:10.12382/bgxb.2023.0664
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    For the consensus control problem of general linear multi-agent system under event-trigger, the existing algorithms mostly adopt static triggering method, and there is global information in the design of consensus protocol, which can not realize fully distributed control. A consensus protocol based on dynamic event-triggering is proposed to solve this problem. The scalar gain of the graph is adaptively adjusted to realize the fully distributed solution of consensus problem by introducing the time-varying coupling weight. For the problem of continuously detecting the trigger conditions in event-triggered control, an individual-based state estimator is introduced to estimate its own state during the trigger interval, which reduces the number of communications between agents. Based on the selected Lyapunov function, a dynamic trigger function is designed to couple the state estimation value, measurement error and adaptive parameters of the system, which significantly reduces the number of triggers and the energy consumption of the system. Numerically calculated results show that the proposed consensus protocol can achieve asymptotic consensus without Zeno behavior. Simulated results show that the proposed protocol can effectively reduce system energy consumption and save communication resources.