Multi-UAV cooperative maneuver decision-making for pursuitevasion using improved MADRL

Aiming at the problem of multi-UAV pursuit-evasion confrontation, a UAV cooperative maneuver method based on an improved multi-agent deep reinforcement learning(MADRL) is proposed. In this method, an improved Comm Net network based on a communication mechanism is introduced into a deep reinforcement learning algorithm to solve the multi-agent problem. A layer of gated recurrent unit(GRU) is added to the actor-network structure to remember historical environmental states. Subsequently,another GRU is designed as a communication channel in the Comm Net core network layer to refine communication information between UAVs. Finally, the simulation results of the algorithm in two sets of scenarios are given, and the results show that the method has good effectiveness and applicability.

Research on Maneuver Decision-Making of Multi-Agent Adversarial Game in a Random Interference Environment

The strategy evolution process of game players is highly uncertain due to random emergent situations and other external disturbances.This paper investigates the issue of strategy interaction and behavioral decision-making among game players in simulated confrontation scenarios within a random interference environment.It considers the possible risks that random disturbances may pose to the autonomous decision-making of game players,as well as the impact of participants’manipulative behaviors on the state changes of the players.A nonlinear mathematical model is established to describe the strategy decision-making process of the participants in this scenario.Subsequently,the strategy selection interaction relationship,strategy evolution stability,and dynamic decision-making process of the game players are investigated and verified by simulation experiments.The results show that maneuver-related parameters and random environmental interference factors have different effects on the selection and evolutionary speed of the agent’s strategies.Especially in a highly uncertain environment,even small information asymmetry or miscalculation may have a significant impact on decision-making.This also confirms the feasibility and effectiveness of the method proposed in the paper,which can better explain the behavioral decision-making process of the agent in the interaction process.This study provides feasibility analysis ideas and theoretical references for improving multi-agent interactive decision-making and the interpretability of the game system model.

An air combat maneuver pattern extraction based on time series segmentation and clustering analysis

Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition method on empirical criteria and sample data,and automatically and adaptively complete the task of extracting the target maneuver pattern,in this paper,an air combat maneuver pattern extraction based on time series segmentation and clustering analysis is proposed by combining autoencoder,G-G clustering algorithm and the selective ensemble clustering analysis algorithm.Firstly,the autoencoder is used to extract key features of maneuvering trajectory to remove the impacts of redundant variables and reduce the data dimension;Then,taking the time information into account,the segmentation of Maneuver characteristic time series is realized with the improved FSTS-AEGG algorithm,and a large number of maneuver primitives are extracted;Finally,the maneuver primitives are grouped into some categories by using the selective ensemble multiple time series clustering algorithm,which can prove that each class represents a maneuver action.The maneuver pattern extraction method is applied to small scale air combat trajectory and can recognize and correctly partition at least 71.3%of maneuver actions,indicating that the method is effective and satisfies the requirements for engineering accuracy.In addition,this method can provide data support for various target maneuvering recognition methods proposed in the literature,greatly reduce the workload and improve the recognition accuracy.

Research on Maneuverability and Si mulation of Underwater Vehicles for Pipeline Detection and Maintenance

For the maintenance of the pipelines which pipe oil from oil fields in the Bohai Gulf of China, an underwater vehicle for pipeline detection and maintenance （PDM-UV） is designed. In this paper, the maneuverability and motion simulation of PDM-UV are studied. A general dynamic equation to describe the hydrodynamics of PDM-UV is presented. For a deep understanding of the dynamics of PDM-UV, which is somewhat different from normal underwater vehicles in configuration, the maneuverability is discussed in terms of hydrodynamic coefficients with experimental results. Finally, the whole motion simulation system is introduced and simulation results are presented to demonstrate the feasibility and correctness of the simulation platform.

Maneuverability simulation of a mini autonomous underwater vehicle

In order to simulate the under water motion of a mini autonomous underwater vehicle( AUV) and an- alyze its maneuverability,the dynamical characteristic of the mini AUV was researched. The 6-DOF motion equations were founded. Through model experiment with accessory bodies,the hydrodynamics of AUV body including resistance,main inertial and viscous hydrodynamic coefficients was obtained. The hydrodynamics of rudders was gained through theoretical calculation. Simulation computation of the vehicle was carried out through numerical integration of the motion equations. A motion simulation system was constructed. Four typical maneuvers in horizontal and vertical planes were simulated and the maneuverability of the mini AUV was forecasted. The simulation results reflect the basic motion characteristic of mini AUV and validate the feasibility and correctness of the whole system. The simulation system can be a testing platform for the design and debugging of motion controller and an effective tool for the development of AUV.

Mechatronic Design and Maneuverability Analysis of a Novel Robotic Shark

In this paper,the mechatronic design and maneuverability analysis of a novel robotic shark are presented.To obtain good maneuverability,a barycenter regulating device is designed to assist the posture adjustment at low speeds.Based on the Newton-Euler approach,an analytical dynamic model is established with particular consideration of pectoral fins for threedimensional motions.The hydrodynamic coefficients are computed using computational fluid dynamics(CFD)methods.Oscillation amplitudes and phases are determined by fitting an optimized fish body wave.The performance of the robotic shark is estimated by varying the oscillation frequency and offset angle.The results show that with oscillation frequency increasing,the swimming speed increases linearly.The robotic shark reaches the maximum swimming speed of 1.05 m/s with an oscillation frequency of 1.2 Hz.Furthermore,the turning radius decreases nonlinearly as the offset angle increased.The robotic shark reaches the minimum turning radius of 1.4 times the body length with 0.2 Hz frequency and 12°offset angle.In the vertical plane,as the pectoral fin angle increases,the diving velocity increases nonlinearly with increase rate slowing down.

AIRCRAFT LIFT AND MANEUVERABILITY ENHANCEMENT BY GENETIC GEOMETRY OPTIMIZATION OF LEX CONFIGURATION

To enhance the maneuverability of the selected aircraft model, a standard genetic algorithm （GA） is used as an optimization method for the preliminary design of the leading-edge extension （LEX） layout. The aerodynamic loads and the maximum lift coefficient of the complete aircraft configuration （fuselage＋wing＋tail） are computed by using the modified three-dimensional low-order panel method in conjunction with the semi-empirical formulas of DATCOM. Results show that the lift coefficient increases approximately 20.5%- 15.3% for Mach number 0. 4-0.8 and 6.8% for Mach number 1.2, and its maximum value approximately 9.5% -15.0% for Machnumber 0.2-0.95when LEXis installed. A 6.6%-8.0 % gain at altitudes of 1-5 km on the turn rate maneuverability and the corner speed have been achieved in the subsonic regime.

On Self Recovery Mechanism and Additional Maneuverability of a Mini Mobile Robot

Ordinary mobile robots have some kind of moving mechanisms attached to one rigid body. When working on rough terrain or in other hazard environments, there existed some possibilities that the robot will be turned up side down, thus causing losses to the robot's expedition. Multi bodied mobile robots provide a solution to that problem. Using active joints between bodies, the robot can recover from turnover situation by itself. In this paper, the authors discuss the joint arrangements and the additional maneuverability resulted from joints between body segments.

Discussion on the Correlative Problem of Maneuverability Model Test for Vessels with Large Scale

It is well known that model test is one of approaches to investigate the maneuverability of vessels and the correlative method is an important problem of model test for vessels with large scale. Some of correlative problems,which should be solved with care in model test,are presented from analysis of the similarity principle for the ship maneuverability in this paper.A corrective method of appropriate angle of rudder is provided based on the result of maneuverability model test for a tanker with large scale and the corresponding prediction of the maneuverability for full scale tanker is satisfactory in view of engineering practice.

Optimal maneuvering strategy of spacecraft evasion based on angles-only measurement and observability analysis

Spacecraft orbit evasion is an effective method to ensure space safety. In the spacecraft’s orbital plane, the space non-cooperate target with autonomous approaching to the spacecraft may have a dangerous rendezvous. To deal with this problem, an optimal maneuvering strategy based on the relative navigation observability degree is proposed with angles-only measurements. A maneuver evasion relative navigation model in the spacecraft’s orbital plane is constructed and the observability measurement criteria with process noise and measurement noise are defined based on the posterior Cramer-Rao lower bound. Further, the optimal maneuver evasion strategy in spacecraft’s orbital plane based on the observability is proposed. The strategy provides a new idea for spacecraft to evade safety threats autonomously. Compared with the spacecraft evasion problem based on the absolute navigation, more accurate evasion results can be obtained. The simulation indicates that this optimal strategy can weaken the system’s observability and reduce the state estimation accuracy of the non-cooperative target, making it impossible for the non-cooperative target to accurately approach the spacecraft.

Cooperative maneuver decision making for multi-UAV air combat based on incomplete information dynamic game

Cooperative autonomous air combat of multiple unmanned aerial vehicles(UAVs)is one of the main combat modes in future air warfare,which becomes even more complicated with highly changeable situation and uncertain information of the opponents.As such,this paper presents a cooperative decision-making method based on incomplete information dynamic game to generate maneuver strategies for multiple UAVs in air combat.Firstly,a cooperative situation assessment model is presented to measure the overall combat situation.Secondly,an incomplete information dynamic game model is proposed to model the dynamic process of air combat,and a dynamic Bayesian network is designed to infer the tactical intention of the opponent.Then a reinforcement learning framework based on multiagent deep deterministic policy gradient is established to obtain the perfect Bayes-Nash equilibrium solution of the air combat game model.Finally,a series of simulations are conducted to verify the effectiveness of the proposed method,and the simulation results show effective synergies and cooperative tactics.

Optimal maneuver strategy to improve the observability of angles-only rendezvous

This paper proposes an optimal maneuver strategy to improve the observability of angles-only rendezvous from the perspective of relative navigation.A set of dimensionless relative orbital elements(ROEs)is used to parameterize the relative motion,and the objective function of the observability of anglesonly navigation is established.An analytical solution of the optimal maneuver strategy to improve the observability of anglesonly navigation is obtained by means of numerical analysis.A set of dedicated semi-physical simulation system is built to test the performances of the proposed optimal maneuver strategy.Finally,the effectiveness of the method proposed in this paper is verified through the comparative analysis of the objective function of the observability of angles-only navigation and the performances of the angles-only navigation filter under different maneuver schemes.Compared with the cases without orbital maneuver,it is concluded that the tangential filtering accuracy with the optimal orbital maneuver at the terminal time is increased by 35%on average,and the radial and normal filtering accuracy is increased by 30%on average.

Air combat target maneuver trajectory prediction based on robust regularized Volterra series and adaptive ensemble online transfer learning

Target maneuver trajectory prediction is an important prerequisite for air combat situation awareness and maneuver decision-making.However,how to use a large amount of trajectory data generated by air combat confrontation training to achieve real-time and accurate prediction of target maneuver trajectory is an urgent problem to be solved.To solve this problem,in this paper,a hybrid algorithm based on transfer learning,online learning,ensemble learning,regularization technology,target maneuvering segmentation point recognition algorithm,and Volterra series,abbreviated as AERTrOS-Volterra is proposed.Firstly,the model makes full use of a large number of trajectory sample data generated by air combat confrontation training,and constructs a Tr-Volterra algorithm framework suitable for air combat target maneuver trajectory prediction,which realizes the extraction of effective information from the historical trajectory data.Secondly,in order to improve the real-time online prediction accuracy and robustness of the prediction model in complex electromagnetic environments,on the basis of the TrVolterra algorithm framework,a robust regularized online Sequential Volterra prediction model is proposed by integrating online learning method,regularization technology and inverse weighting calculation method based on the priori error.Finally,inspired by the preferable performance of models ensemble,ensemble learning scheme is also incorporated into our proposed algorithm,which adaptively updates the ensemble prediction model according to the performance of the model on real-time samples and the recognition results of target maneuvering segmentation points,including the adaptation of model weights;adaptation of parameters;and dynamic inclusion and removal of models.Compared with many existing time series prediction methods,the newly proposed target maneuver trajectory prediction algorithm can fully mine the prior knowledge contained in the historical data to assist the current prediction.The rationality and effectiveness of the proposed algorithm are verified by simulation on three sets of chaotic time series data sets and a set of real target maneuver trajectory data sets.

Impact point prediction guidance of ballistic missile in high maneuver penetration condition

An impact point prediction(IPP) guidance based on supervised learning is proposed to address the problem of precise guidance for the ballistic missile in high maneuver penetration condition.An accurate ballistic trajectory model is applied to generate training samples,and ablation experiments are conducted to determine the mapping relationship between the flight state and the impact point.At the same time,the impact point coordinates are decoupled to improve the prediction accuracy,and the sigmoid activation function is improved to ameliorate the prediction efficiency.Therefore,an IPP neural network model,which solves the contradiction between the accuracy and the speed of the IPP,is established.In view of the performance deviation of the divert control system,the mapping relationship between the guidance parameters and the impact deviation is analysed based on the variational principle.In addition,a fast iterative model of guidance parameters is designed for reference to the Newton iteration method,which solves the nonlinear strong coupling problem of the guidance parameter solution.Monte Carlo simulation results show that the prediction accuracy of the impact point is high,with a 3 σ prediction error of 4.5 m,and the guidance method is robust,with a 3 σ error of 7.5 m.On the STM32F407 singlechip microcomputer,a single IPP takes about 2.374 ms,and a single guidance solution takes about9.936 ms,which has a good real-time performance and a certain engineering application value.

Research on Maneuverability of Moving Centroid Warhead

The dynamic equations of the moving centroid warhead are derived, simplified and linearized, and a transfer function from moving mass displacement to normal overload of the warhead is obtained. Thus, the normal overload can be calculated when the mass moves to any different places. The research results provide a necessary theoretic reference for general design of control system of the moving centroid warhead.

A two-step method to detect broadcast ephemeris unavailable periods caused by BeiDou satellite orbital maneuvers

The space constellation of the BeiDou navigation satellite system(BDS) is a hybrid constellation containing medium earth orbit(MEO) satellites, geostationary earth orbit(GEO) satellites, and inclined geosynchronous orbit(IGSO) satellites. Due to the geosynchronous characteristics of GEO and IGSO, GEO satellites and IGSO satellites often need to perform orbital maneuvers, which can affect the signal-inspace(SIS) availability performance of BeiDou satellites. A two-step detection method for BeiDou satellite orbital maneuvers has been proposed in this paper. The first step is to identify orbital maneuvers based on time series analysis of broadcast ephemeris, and the second step is to verify orbital maneuvers based on bidirectional orbit prediction. The two-step detection method was used to detect the orbital maneuvers of BeiDou satellites in 2019. Through the double guarantees of identification and verification,the detection accuracy of BeiDou satellite orbital maneuvers has been effectively improved. And the orbital maneuver detection results are continued to be used to assess the SIS availability of BeiDou satellites. The results show that the availability loss of GEO satellite orbital maneuvers is about 0.45%-1.07%, and the availability loss of IGSO satellite orbital maneuvers is about 0.12%-0.19%.

地面机动平台可重构轮履复合推进机构研究现状与展望

可重构轮履复合推进机构兼具轮式推进机构的快速性和履带推进机构的通过性,是提高地面机动平台在极端地形上综合性能的关键技术,在各类机器人和特种车辆上都得到了一定应用.文中在系统调研国内外相关文献的基础上,按轮履构型重构互换原理对推进机构进行分类,着重对比分析了不同构型的结构特点和应用场景,指出了可重构轮履复合推进机构的主要技术难点和发展前景.

浅水效应下智能技术试验船回转操纵性数值模拟

本文借助STAR-CCM+软件对考虑浅水效应的智能技术试验船回转操纵性进行数值模拟研究。首先对无界流场中智能技术试验船的流体动力性能评估,并且与试验结果对比验证了计算方法的准确性;在此基础上,通过改变智能技术试验船与池底的垂向距离,模拟不同浅水工况下智能技术试验船回转操纵性能。数值模拟结果表明,浅水效应下智能技术试验船侧向力系数显著增大,对应的偏航力矩系数亦有所增加。相关结论可为智能技术试验船浅水航行时安全性能提供指导。

船舶操纵性海试实验虚拟仿真设计与应用

操纵性试验是船舶海试的重要组成部分,可有效评估船舶投入运营后操纵性能的优劣。船舶操纵性海试准备时间长、成本高、风险大,难以开展实验教学。依托虚拟仿真软硬件设施,充分融合虚拟现实技术、3D成像投影技术与船舶操纵性能分析方法,探索新型实验教学模式,完成船舶操纵性海试虚拟仿真设计;并以集装箱船为例,开展实验教学应用研究。船舶操纵性海试实验虚拟仿真有效弥补了海试的诸多不足,为学生沉浸式体验船舶操纵性海试全过程、掌握相关实验规程与分析方法提供良好的虚拟仿真环境。

水下航行体极限大机动流动分离大涡模拟研究

针对大机动极限工况下水下航行体流动分离带来的机动性不足问题,本文以Suboff标模为研究对象,采用大涡模拟数值计算方法开展大机动工况下水下航行体的流体动力性能评估,并完成水下航行体尾部及舵面流动分离涡旋结构的精细捕捉。本文研究成果可为后续极限机动工况下水下航行体流动控制提供技术支撑。