基于指端6维力传感器的接触点测量算法研究

在硬手指接触模型下应用6维力传感器测量值计算接触点位置时,有可能出现无解现象.针对这一问题,分析和证明了硬手指和软手指接触模型下解的存在性和相容性,指出当测量力和力矩正交时,硬手指算法有实根,且符合相容性,反之则不然.当测量力和力矩非正交时,对任意非零接触力旋量,软手指算法存在唯一实数解.因此在实际测量过程中,可通过判断测量力和力矩是否正交,作为选择使用两种算法的依据,以确保测量结果和控制系统稳定可靠.通过实验对上述方法进行了验证.      

Area-oriented coordinated trajectory planning of dual-arm space robot for capturing a tumbling target

The growing amount of space debris poses a threat to operational spacecraft and the long-term sustainability of activities in outer space. According to the orbital mechanics, an uncontrolled space object will be tumbling, bringing great challenge to capture and remove it. In this paper, a dual-arm coordinated ‘‘Area-Oriented Capture"(AOC) method is proposed to capture a non-cooperative tumbling target. Firstly, the motion equation of the tumbling target is established, based on which, the dynamic properties are analyzed. Then, the ‘‘Area-Oriented Capture"concept is presented to deal with the problem of large pose(position and attitude) deviation and tumbling motion. An area rather than fixed points/devices is taken as the object to be tracked and captured. As long as the manipulators' end-effectors move to a specified range of the objective areas(not fixed points on the target, but areas), the target satellite will be hugged by the two arms.At last, the proposed method and the traditional method(i.e. fixed-point oriented capture method)are compared and analyzed through simulation. The results show that the proposed method has larger pose tolerance and takes shorter time for capturing a tumbling target.     

在轨服务中空间系绳的应用及发展

针对空间系绳在空间在轨服务中的应用与发展问题，在简述空间系绳传统应用及试验情况的基础上，综述了空间绳系机器人、空间绳网机器人两类新在轨应用方式的研究和发展，主要包括动力学建模、结构设计、相对状态测量、逼近/抓捕控制、拖曳变轨等。最后进一步讨论了空间系绳未来的研究方向。     

Kinematics-based four-state trajectory tracking control of a spherical mobile robot driven by a 2-DOF pendulum

Spherical mobile robot has compact structure, remarkable stability, and flexible motion,which make it have many advantages over traditional mobile robots when applied in those unmanned environments, such as outer planets. However, spherical mobile robot is a special highly under-actuated nonholonomic system, which cannot be transformed to the classic chained form. At present, there has not been a kinematics-based trajectory tracking controller which could track both the position states and the attitude states of a spherical mobile robot. In this paper, the four-state(two position states and two attitude states) trajectory tracking control of a type of spherical mobile robot driven by a 2-DOF pendulum was studied. A controller based on the shunting model of neurodynamics and the kinematic model was deduced, and its stability was demonstrated with Lyapunov's direct method. The control priorities of the four states were allocated according to the magnification of each state tracking error in order to firstly ensure the correct tracking of the position states. The outputs(motor speeds) of the controller were regulated according to the maximum speeds and the maximum accelerations of the actuation motors in order to solve the speed jump problem caused by initial state errors, and continuous and bounded outputs were obtained. The effectiveness including the anti-interference ability of the proposed trajectory tracking controller was verified through MATLAB simulations.     

基于地形匹配的高超声速飞行器初始定向偏差在线辨识方法

针对高超声速飞行器机动发射条件下自瞄准时间短、初始定向偏差大的问题，提出一种基于地形匹配的初始定向偏差在线辨识方法。以综合初始对准和姿态控制的惯性系统导航误差模型为分析基础，融合相邻两个地形匹配区两次地形匹配定位结果，建立了初始定向偏差在线辨识模型，并从惯性系统工具误差系数偏差和地形匹配定位误差两方面讨论了辨识模型的适应性。以美国高超声速飞行器CAV H为研究对象建立仿真环境，采用蒙特卡洛法检验初始定向偏差辨识效果。仿真结果表明，在地形匹配定位误差为173.88 m (3σ)情况下，初始定向剩余偏差平均值为8.42″，最大值为34.65″，能够有效提高惯性系统导航精度，并且有助于缩短发射准备时间、提高武器系统生存能力。     

Detumbling strategy based on friction control of dual-arm space robot for capturing tumbling target

The rotational motion of a tumbling target brings great challenges to space robot on successfully capturing the tumbling target. Therefore, it is necessary to reduce the target’s rotation to a rate at which capture can be accomplished by the space robot. In this paper, a detumbling strategy based on friction control of dual-arm space robot for capturing tumbling target is proposed. This strategy can reduce the target’s rotational velocity while maintaining base attitude stability through the establishment of the rotation attenuation controller and base attitude adjustment controller. The rotation attenuation controller adopts the multi-space hybrid impedance control method to control the friction precisely. The base attitude adjustment controller applies the dual-arm extended Jacobian matrix to stabilize the base attitude. The main contributions of this paper are as follows: (1) The compliant control method is adopted to achieve a precise friction control, which can reduce the target angular velocity steadily; (2) The dual-arm extended Jacobian matrix is applied to stabilize the base attitude without affecting the target capture task; (3) The detumbling strategy of dual-arm space robot is designed considering base attitude stabilization, realizing coordinated planning of the base attitude and the arms. The strategy is verified by a dual-arm space robot with two 7-DOF (degrees of freedom) arms. Simulation results show that, target with a rotation velocity of 20 (°)/s can be effectively controlled to stop within 30 s, and the final deflection of the base attitude is less than 0.15° without affecting the target capture task, verifying the correctness and effectiveness of the strategy. Except to the tumbling target capture task, the control strategy can also be applied to other typical on-orbit operation tasks such as space debris removal and spacecraft maintenance.     

弹跳机器人动力学分析

弹跳机器人可以跃过数倍于自身尺寸的障碍物或沟渠,对地形有较强的适应力。但是相对于已经实用化的其他类型机器人,弹跳机器人目前还处于研究阶段。本文把驱动机构和弹跳机构一起加以考虑,综合各种阻尼的影响,构建了两类弹跳机器人模型。然后采用二阶拉格朗日方程,对其进行相应的动力学分析和比较,求出模型的动力学标准状态方程和弹跳高度的计算公式;并通过计算机模拟结果和试验结果的对比说明了分析的正确性。本文的分析结果同时适用于连续型和间隙型弹跳机器人的动力学分析。     

基于自组织与LVQ神经网络的足球机器人协作策略学习

足球机器人系统目前已成为人工智能应用技术研究的重要实验平台，系统的核心部分就是决策子系统。本文主要研究机器人足球比赛中协作策略的学习问题，采用了自组织与学习向量量化（LVQ）神经网络算法实现了两个足球机器人的传球学习。提出了一种协作策略学习的改进算法。该算法通过对网络权值矩阵进行改进，可以显著提高网络的训练质量。仿真和实验结果表明了该方法的可行性与有效性。     

一种基于遗传算法的机器人补偿学习控制方法

机器人建模的不精确性以及一些扰动的存在给机器人控制增加了相当大的难度。针时这一问题，本文以PUMA560机器人为被控对象，给出了一种PUMA560机器人动力学模型的简化形式，采用PD控制的计算力矩法，得到了机器人的闭环动态误差方程，在此基础上设计了机器人的控制器结构，提出了一种新的基于遗传算法(Genetic algorithm，GA)的机器人补偿学习控制方法。将GA与计算力矩法相结合，利用进化学习来消除机器人中不确定因素的影响，实现时机器人轨迹跟踪的良好控制。最后给出了这种控制的仿真结果，验证了该方法的有效性。     

柔性机械臂的模糊自适应控制研究

提出了一种用于柔性机器人操作臂控制的模糊自适应控制策略。由于柔性臂的结构特点，在进行其操作臂的控制研究时，必须考虑系统的振动特性才能达到末端的高精度控制。本将拉格朗日法和假设模态法相结合，建立了单自由度柔性臂系统的动力学方程，提出了一种模糊自适应控制策略。在柔性操作臂末端带有不同载荷的情况下，分两种类型进行了控制仿真实验：定位控制和轨迹跟踪控制。仿真结果与传统的PD控制仿真实验结果相比较，表明模糊自适应控制器在轨迹跟踪和系统的振动抑制方面反映出更好的稳定性和鲁棒性，适用于柔性机械臂系统的动力学控制。