三关节机器人的神经网络补偿自适应控制器设计

三关节机器人广泛用于工业生产、轮式或履带式排爆机器人,为了补偿由于机器人结构参数、作业环境干扰等不确定性因素造成的机器人动力学模型的不确定性,将机器人动力学模型分解为名义模型和误差模型两部分,其误差模型采用RBF神经网络进行补偿,得到其估计信息,神经网络的输出权值根据Lyapunov稳定性理论采用自适应算法进行调整。所设计的神经网络补偿自适应控制器解决了不确定性机器人动力学系统控制器设计的不确定性问题,同时,通过定义Lyapunov函数,证明了控制器能渐近、稳定地跟踪期望轨迹。机器人的3个关节在控制器的作用下,约在5 s时达到期望轨迹,神经网络约在5 s时逼近机器人动力学模型的误差模型,实验结果表明了机器人关节对期望轨迹具有良好的轨迹跟踪性能。     

管道机器人在三通处的通过性分析

三通是管道机器人经常遇到的典型障碍之一,克服该障碍的能力用管道机器人在三通处通过性来描述。文中提出一种描述差压驱动式管道机器人三通通过性的数学模型,该模型由一组组合约束构成。通过对约束方程的分析讨论、与管道机器人弯道通过性的对比分析,得出了规律性的结论。管道机器人在三通处的姿态、单元体的几何尺寸、行走轮结构形式对其通过性都有不同程度的影响。所提出数学模型是管道机器人三通自主行走控制策略设计和相应结构设计的理论基础。     

典型作战场景下排爆机器人性能评价方法

针对目前反恐排爆机器人缺乏实战化性能考核方法的问题,结合其技术特点和典型作战应用环境,通过构建一种以通行、探测、转移、销毁等性能为主要考核科目的排爆作战场景,并以记分的形式给出一种简单、实用的排爆机器人作战评价体系.该评价体系经过武警部队装备部主办的全国首届"反恐突击-2019"智能无人系统挑战赛的实践验证,其具有良好的应用前景,可为现阶段反恐排爆机器人作战性能实战化考核提供参考依据.     

21世纪战场的“钢铁战士”——智能军用机器人

军用机器人是一种用于军事领域的、具有某种仿人功能的自动机,可在战场上执行各种任务,取代人类从事笨重和危险的作业。智能军用机器人具有感觉功能,能够独立决策并采取相应的行动。由于机器人在军事领域中具有广泛的应用前景,世界各国都作常重视智能军用机器人的研究。据外刊透露,美、俄、日、英、德、澳等国都制定了发展智能军用机器人的计划。美国防部还把智能军用机     

新型空间绳系机器人逼近动力学建模及控制

为弥补传统空间机器人操作范围小,风险高的缺点,介绍了一种新型空间绳系机器人系统。建立其任务核心的逼近动力学模型,基于反馈线性化技术和神经网络控制技术设计智能逼近控制系统,仿真结果验证了控制系统的有效性,为新型近距离空间操作技术的发展奠定基础。     

基于神经网络的机器人运动学反解算法

提出了一种基于神经网络的机器人运动学反解算法。详细讨论了神经网络求解的快速算法以及求解精度的自动检验与改进等技术问题,并以六自由度ＰＵＭＡ机器人为例,进行了数值模拟计算。结果表明：用神经网络反对机器人的运动学问题,不仅求解过程简单,还可避免传统反解方法中的许多棘手问题。     

水下军用机器人

水下机器人又称为水下无人潜水器，分为遥控、半自主及自主型，水下机器人是典型的军民两用技术，不仅可用于海上资源的勘探和开发，而且在海战中也有不可替代的作用．为了争夺制海权，各国都在开发各种用途水下机器人，其中有探雷机器人、扫雷机器人、侦察机器人等，     

水下机器人避碰运动研究

本文针对水下机器人与周围障碍物发生碰撞的情况,提出了一种用模糊决策技术判断危险度大小的方法,建立了危险度判断模型,并分析了水下机器人避碰动作决策模型的构造原则。     

21世纪的“机器士兵”(下)

貌似昆虫的小型卫士貌似昆虫的机器人也称为小型自主机器人。它体积小,结构简单,价格便宜。这种小型机器人有些还可以装备具有强攻击力的杀伤兵器,可以装载在飞机或航天器上执行多种空中作战任务,在未来作战领域中有着极其广泛的应用前     

基于DoDAF的空间机器人作战视图研究

介绍了基于DoDAF的体系结构建模方法,采用System Architect工具对空间机器人在轨服务进行了可视化建模,得到了典型的作战视图产品;分析了在轨服务任务单元之间的协同关系,研究了空间机器人在轨服务流程,对空间机器人的在轨服务研究具有一定的参考价值。     

OODA-L模式下的智能无人集群作战仿真建模框架

介绍了智能无人集群作战的相关概念,为反映智能无人仿真实体的自主能力和适应能力,提出将学习过程显性化的观察-判断-决策-行动-学习(Observe, Orient, Decide, Act and Learning, OODA-L)模式,并进一步扩展为适用于集群协同的Co-OODA-L模式。在智能无人仿真实体的总体描述上,采用马尔可夫决策过程进行数学抽象处理,提出智能无人Agent的三域分层结构。为体现智能无人集群作战的自主协同、分布式等特点,提出了利用人工神经网络将可变数量的智能无人Agent融合为同构或异构集群进行协同作战建模的体系结构。     

分级智能化的武器平台——无人平台体系结构解析

通过对现有机器人体系结构的总结,结合任务需求和当前技术发展水平,构造了一种人机智能紧耦合的无人平台系统结构.该结构分为7层,具有3种导航模式,6种控制方式,能够实现从主从遥控到全自主不同智能层次的无人平台构建,为无人平台的设计提供了工程指导,并为无人平台的应用提供了方向.     

【专题】复杂陆战场环境下的智能感知理论现状与发展

近年来,由于基于深度学习方法的智能检测算法不断演进,其网络结构不断进化,实用化程度不断提高,因此,将其应用于复杂战场环境下,形成实用化智能感知能力的可行性不断提高。然而算法的可靠性、可解释性问题目前仍未完全解决。本文认为,在未来的地面无人平台系统框架内,使用基于深度学习的目标检测识别方法,融合多种传感器感知信号,探索如何可靠地收集无人平台附近敌我车辆、人员、相关物体状况以及视距内的地理与气象环境信息,能够实现多元智能感知过程,构建智能复杂体系,为无人平台实现复杂战场环境感知理解,自主环境判定、自主行走、自主危险判定甚至威胁自动处置提供技术储备。同时,这也将是军队下一步智能感知理论方向的主要任务。     

Autonomous maneuver decision-making for a UCAV in short-range aerial combat based on an MS-DDQN algorithm

To solve the problem of realizing autonomous aerial combat decision-making for unmanned combat aerial vehicles (UCAVs) rapidly and accurately in an uncertain environment, this paper proposes a decision-making method based on an improved deep reinforcement learning (DRL) algorithm: the multi-step double deep Q-network (MS-DDQN) algorithm. First, a six-degree-of-freedom UCAV model based on an aircraft control system is established on a simulation platform, and the situation assessment functions of the UCAV and its target are established by considering their angles, altitudes, environments, missile attack performances, and UCAV performance. By controlling the flight path angle, roll angle, and flight velocity, 27 common basic actions are designed. On this basis, aiming to overcome the defects of traditional DRL in terms of training speed and convergence speed, the improved MS-DDQN method is introduced to incorporate the final return value into the previous steps. Finally, the pre-training learning model is used as the starting point for the second learning model to simulate the UCAV aerial combat decision-making process based on the basic training method, which helps to shorten the training time and improve the learning efficiency. The improved DRL algorithm significantly accelerates the training speed and estimates the target value more accurately during training, and it can be applied to aerial combat decision-making.     

Real-time prediction of projectile penetration to laminates by training machine learning models withfinite element solver as the trainer

Studies on ballistic penetration to laminates is complicated, but important for design effective protection of structures. Experimental means of study is expensive and can often be dangerous. Numerical simu-lation has been an excellent supplement, but the computation is time-consuming. Main aim of this thesis was to develop and test an effective tool for real-time prediction of projectile penetrations to laminates by training a neural network and a decision tree regression model. A large number of finite element models were developed;the residual velocities of projectiles fromfinite element simulations were used as the target data and processed to produce sufficient number of training samples. Study focused on steel 4340tpolyurea laminates with various configurations. Four different 3D shapes of the projectiles were modeled and used in the training. The trained neural network and decision tree model was tested using independently generated test samples using finite element models. The predicted projectile velocity values using the trained machine learning models are then compared with thefinite element simulation to verify the effectiveness of the models. Additionally, both models were trained using a published experimental data of projectile impacts to predict residual velocity of projectiles for the unseen samples. Performance of both the models was evaluated and compared. Models trained with Finite element simulation data samples were found capable to give more accurate predication, compared to the models trained with experimental data, becausefinite element modeling can generate much larger training set, and thus finite element solvers can serve as an excellent teacher. This study also showed that neural network model performs better with small experimental dataset compared to decision tree regression model.     

一种鲁棒的场地球类视频中的球门探测方法

在场地球类视频中,球门常常会伴随着精彩片段而出现。因此,球门探测是体育视频的基础,同时也是视频高层语义概念探测的研究热点。目前,利用机器学习方法进行视频中的对象探测是非常有前途的研究领域。基于此,提出一种基于模糊决策树的球门探测算法,用来探测场地球类视频中球门帧的出现,为了提高分类准确性,在模糊决策树训练的过程中加入了平衡处理。实验结果表明,与基于阈值和决策树的算法相比,该算法可以得到更好的分类结果(F-measure>95%),并且我们可以从所建立的树中推导出模糊规则来解释分类模型。     

无人系统路线图自主性分析

在第三次抵消战略的驱动下,受军费预算不断削减、内部成本日益增长的影响,为遏制中俄等国日益崛起的军事力量,谋求和维持未来军事优势,美军只能将目光聚焦于创新作战概念,发展以无人系统为代表的更具成本效益和作战威力的武器装备。随着人工智能和机器学习技术的进步,无人系统的自主能力得到了大幅提高,未来将可能根据任务需要,实现从远程控制、自动化系统到近乎完全自主。本文认为,美国国防部除将加快发展人工智能和机器学习技术,聚焦于自主系统带来的作战效率和效能的提高,持续评估自主系统以提升作战人员的信任水平之外,也正加紧制定相关的政策法规,以确保自主系统的研发更加规范有序。     

Efficient sequential decision‐making algorithms for container inspection operations

Following work of Stroud and Saeger (Proceedings of ISI, Springer Verlag, New York, 2006) and Anand et al. (Proceedings of Computer, Communication and Control Technologies, 2003), we formulate a port of entry inspection sequencing task as a problem of finding an optimal binary decision tree for an appropriate Boolean decision function. We report on new algorithms for finding such optimal trees that are more efficient computationally than those presented by Stroud and Saeger and Anand et al. We achieve these efficiencies through a combination of specific numerical methods for finding optimal thresholds for sensor functions and two novel binary decision tree search algorithms that operate on a space of potentially acceptable binary decision trees. The improvements enable us to analyze substantially larger applications than was previously possible. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

Maneuvering target tracking of UAV based on MN-DDPG and transfer learning

Tracking maneuvering target in real time autonomously and accurately in an uncertain environment is one of the challenging missions for unmanned aerial vehicles(UAVs).In this paper,aiming to address the control problem of maneuvering target tracking and obstacle avoidance,an online path planning approach for UAV is developed based on deep reinforcement learning.Through end-to-end learning powered by neural networks,the proposed approach can achieve the perception of the environment and continuous motion output control.This proposed approach includes:(1)A deep deterministic policy gradient(DDPG)-based control framework to provide learning and autonomous decision-making capa-bility for UAVs;(2)An improved method named MN-DDPG for introducing a type of mixed noises to assist UAV with exploring stochastic strategies for online optimal planning;and(3)An algorithm of task-decomposition and pre-training for efficient transfer learning to improve the generalization capability of UAV's control model built based on MN-DDPG.The experimental simulation results have verified that the proposed approach can achieve good self-adaptive adjustment of UAV's flight attitude in the tasks of maneuvering target tracking with a significant improvement in generalization capability and training efficiency of UAV tracking controller in uncertain environments.     

基于卷积神经网络的卫星网络协调态势评估方法

为充分发掘利用海量卫星网络数据,提高决策效率,加强空间频轨资源获取与储备的分析手段,尤其是对地球静止轨道资源的协调获取问题,提出基于机器学习算法的卫星网络态势评估策略。通过对卫星网络协调因素进行特征分析,选择卷积神经网络(Convolution Neural Network, CNN)为目标算法模型,并建立算法模型的训练数据集及Label规则,采用分裂信息增益度量方法对数据进行降维处理,建立CNN评估模型,并进行了验证分析。结果表明,CNN模型对卫星网络协调态势评估问题测试的正确率高达80%以上,具有较高的评估效能。随着数据量的增多,CNN评估效果逐步提升,是一种在卫星网络协调态势分析、资源储备的有效评估方法。