面向实时自避碰的双臂机器人力矩控制策略

为解决双臂机器人运动过程中的自碰撞问题，提出了一种面向实时自避碰的双臂机器人力矩控制策略。利用机械臂正向运动学构建双臂机器人动态骨架包围盒，用于简化计算关节间最小距离；根据机械臂关节空间阻抗控制规则，设计了基于关节间距离的力矩控制算法，将运动中各关节间距离转换为避碰力矩，并将避碰力矩和其他任务的期望力矩相加，从而得到控制双臂机器人运动的实际力矩。实验表明：当双臂机器人的设定杆件相互靠近且达到避碰距离时，本研究所提出的自避碰策略能够及时对机器人的运动关节产生一组平滑的避碰力矩，避免了机器人自身发生碰撞，验证了所提算法的有效性及实用性。     

被动冗余度空间机器人运动学综合

分析了“被动冗余度”空间机器人主,被动关节之间的运动学耦合,得到了可用于运动 学规划的耦合指标;分析了“被动冗余度”空间机器人的运动学奇异问题,以及与对应全主 动关节冗余度空间机器人的运动学奇异的区别,得到的新的可操作性指标同样可用于机器人 的运动规划;推导出了“被动冗余度”空间机器人的最佳最小二乘运动学优化方程,通过“ 准自运动”实现被动冗余度空间机器人优化控制;通过对平面3DOF空间站机器人的仿真证实 了分析得到的结论．     

自由漂浮空间机器人避奇异规划—跟踪算法

针对路径相关空间内自由漂浮空间机器人无法进行有效跟踪控制的问题,设计了一种避奇异轨迹规划—跟踪算法,用于完成路径相关空间机械臂末端轨迹跟踪控制的任务．首先,分析奇异条件并设定安全边界曲线,求解回避奇异的基座姿态角阈值,从而得到避奇异参考轨迹及初始状态值．接着,利用自由漂浮空间机器人非线性动力学模型具有状态依赖参数的类线性结构特点,基于状态依赖Riccati方程设计跟踪控制器对末端速度进行跟踪,保证闭环系统的局部渐近稳定性．所提方法克服了传统方法将工作空间约束在路径无关空间的缺点．仿真结果表明,该算法具有比比例微分（proportional derivative,PD）控制更高的跟踪精度．同时,在存在输入干扰的情况下仍然能够实现有效跟踪．     

基于一致性理论的多臂航天器协同控制方法

针对空间中自由漂浮多臂航天器的多臂协同问题,提出一种基于一致性理论的协同控制方法,采用有向通信拓扑与广义雅克比矩阵结合的方式,实现自由漂浮航天器多机械臂间的协同.首先,建立多机械臂间的通信关系有向图,确定“领导-跟随”体系下的主臂与从臂;其次,基于有向通信拓扑,进行主从臂末端运动规划,实现主臂运动向从臂的传递;再次,利用广义雅克比矩阵在动量守恒条件下进行末端运动向关节运动的映射,并基于一致性理论设计关节空间内的多臂协同运动控制器;最后,基于李雅普诺夫稳定性理论证明控制器的稳定性,并分析位置控制误差.仿真结果表明,所提出的控制方法可以实现多臂航天器系统空间操控任务中各机械臂的聚集、跟踪与位置协同.     

双臂空间机器人的避自碰轨迹规划研究*

本文针对自由漂浮的双臂空间机器人系统研究了一种基于危险域的避自碰轨迹规划方案。首先,引入危险域的概念,用来评估两个机械臂之间发生碰撞的危险程度。其次,在路径规划的基础之上,利用危险域的反馈信息,设计了一种安全避自碰的轨迹规划方案,用以保证两个机械臂可以运动在安全位型,从而避免发生自碰。最后,针对一个双臂冗余空间机器人系统进行运动仿真,仿真结果验证了本文方法的有效性。     

基于伪逆的导轨机械臂关节速度纠偏运动规划方案

针对导轨机械臂在任务执行过程中出现的关节速度偏离期望值的问题,提出了一种基于伪逆算法的导轨机械臂关节速度纠偏运动规划方案。首先,根据机械臂的关节角状态和末端执行器的运动状态,运用伪逆算法对导轨机械臂在速度层上进行冗余度解析。然后,设计时变函数对关节速度进行约束调整,使偏离后的关节速度收敛于期望值。接着,针对末端执行器出现的位置误差设计了误差修正方法以保证轨迹跟踪任务的顺利执行。最后,将运动规划方案在Matlab软件上以基座直线移动和弧形移动的四连杆冗余度机械臂为例进行了仿真实验。仿真结果表明了该方案能纠正导轨机械臂在任务执行过程中偏离期望值的关节速度,且能使末端执行器的轨迹跟踪达到较高的精度。     

自由飘浮空间机器人系统基座姿态调整

规划机械臂的运动以调整作为其基座的卫星的姿态，既节约姿控燃料，又可作为常规姿控系统的备份手段．首先，建立自由飘浮空间机器人系统的状态方程，其状态变量为关节角和卫星姿态角，输入变量为关节角速度．基于系统能控性理论，规划连接系统初始状态和期望状态的路径，实现了仅通过机械臂关节的运动同时控制基座姿态和机械臂关节角的目的．从理论上分析了机械臂的能量消耗，给出了使能量指标最小的近似最优算法．仿真结果表明了该方法的有效性．     

基于伪逆的导轨机械臂关节速度纠偏运动规划方案

针对导轨机械臂在任务执行过程中出现的关节速度偏离期望值的问题，提出了一种基于伪逆算法的导轨机械臂关节速度纠偏运动规划方案。首先，根据机械臂的关节角状态和末端执行器的运动状态，运用伪逆算法对导轨机械臂在速度层上进行冗余度解析。然后，设计时变函数对关节速度进行约束调整，使偏离后的关节速度收敛于期望值。接着，针对末端执行器出现的位置误差设计了误差修正方法以保证轨迹跟踪任务的顺利执行。最后，将运动规划方案在Matlab软件上以基座直线移动和弧形移动的四连杆冗余度机械臂为例进行了仿真实验。仿真结果表明了该方案能纠正导轨机械臂在任务执行过程中偏离期望值的关节速度，且能使末端执行器的轨迹跟踪达到较高的精度。     

高精度解耦六自由度机械臂逆运动学解法

根据6自由度机械臂正交解耦的结构特点,采用位姿分解方式,将6自由度逆运动学降为3自由度位置逆运动学、3自由度方向逆运动学;利用欧儿里德范数导出机械臂定位、定向的逆运动学解析解,使机械臂高速、准确运动.在定向控制方面,提出一种以单位四元数为目标输入的控制形式,只需计算两个角度逆解,既简化计算,又利于实际操作;利用逆运动学计算机械臂的工作空间和奇异点空间,借助移动机器人车体自由度弥补因计算以及关节长度不够引起的奇异位形,极大扩展了机械手臂的有效运动区域.     

蒸汽发生器检修机械臂监控软件设计

为实现对蒸汽发生器检修机械臂的远程控制和操作模拟与训练,设计了一套上位机监控软件.在VC 平台下,利用OpenGL和3DMAX建立了机械臂、蒸汽发生器水室和检修工具模型;设计了监控软件与六个关节控制器CAN通讯算法,实现了六个关节的协调控制;设计了轨迹规划算法和避碰监测算法,实现了机械臂的无碰撞运动;设计了机械臂单关节的重力矩补偿,提高了关节的控制精度.实际工作验证了监控软件的有效性,利用软件可实现对机械臂的远程控制,完成蒸汽发生器的检修任务.并可用于对操作人员的模拟训练.     

A real-time impedance-based singularity and joint-limits avoidance approach for manual guidance of industrial robots

This paper studies real-time manual guidance considering singularity and joint-limits avoidance using impedance control in an industrial scenario. The operator is responsible for keeping the end-effector (EE) away from the robot’s singularity and joint-limits. The proposed approach detects the singularity and joint-limits in real-time. Then, virtual stiffness and damping are added to target stiffness and damping as the robot is getting close to the singularity or joint-limit. A criterion is presented for detection of singularity by combining manipulability ellipsoid and condition number. Also a new joint to Cartesian space transformation is formulated in order to convert joint stiffness and damping to Cartesian stiffness and damping for joint-limits avoidance method. The presented approach is applied on a SCARA robot. An experiment is performed in this paper to investigate singularity and joint-limits avoidance separately as well as together. Increase in stiffness and damping warn the operator of the possibility of singularity or joint-limits allowing the operator to changes the EE path. The proposed approach, eliminates the need for a robotics expert by allowing any operator with no knowledge about robot singularity and joint-limits to interact and teach the robot in a safe, real-time and time-saving manner.     

On Multiple Secondary Task Execution of Redundant Nonholonomic Mobile Manipulators

This paper investigates self-motion control of redundant nonholonomic mobile manipulators, to execute multiple secondary tasks including tip-over prevention, singularity removal, obstacle avoidance and physical limits escape. An extended gradient projection method (EGPM) is proposed to determine self-motion directions, and a real-time fuzzy logic self-motion planner (FLSMP) is devised to generate the corresponding self-motion magnitudes. Unlike the task-priority allocation method and the extended Jacobian method, the proposed scheme is simple to implement and is free from algorithm singularities. The proposed dynamic model is established with consideration of nonholonomic constraints of the mobile platform, interactive motions between the mobile platform and the onboard manipulator, as well as self-motions allowed by redundancy of the entire robot. Furthermore, a robust adaptive neural-network controller (RANNC) is developed to accomplish multiple secondary tasks without affecting the primary one in the workspace. The RANNC does not rely on precise prior knowledge of dynamic parameters and can suppress bounded external disturbance effectively. In addition, the RANNC does not require any off-line training and can ensure the control performance by online adjusting the neural-network parameters through adaptation laws. The effectiveness of the proposed algorithm is verified via simulations on a three-wheeled redundant nonholonomic mobile manipulator.     

基于二次型规划的平面冗余机械臂的自运动

提出一种基于二次型性能指标的方法,用于规划平面冗余机械臂的自运动轨迹．鉴于实际的机械臂都存在关节物理约束,该自运动规划方案考虑了关节极限和关节速度极限的躲避．提出了基于线性变分不等式的原对偶神经网络,并将其作为所对应的二次型规划方案的实时求解器．仿真结果证实了该基于神经网络的自运动规划方案的有效性．     

Adaptive control of redundant multiple robots in cooperative motion

A redundant robot has more degrees of freedom than what is needed to uniquely position the robot end-effector. In practical applications the extra degrees of freedom increase the orientation and reach of the robot. Also the load carrying capacity of a single robot can be increased by cooperative manipulation of the load by two or more robots. In this paper, we develop an adaptive control scheme for kinematically redundant multiple robots in cooperative motion.In a usual robotic task, only the end-effector position trajectory is specified. The joint position trajectory will therefore be unknown for a redundant multi-robot system and it must be selected from a self-motion manifold for a specified end-effector or load motion. In this paper, it is shown that the adaptive control of cooperative multiple redundant robots can be addressed as a reference velocity tracking problem in the joint space. A stable adaptive velocity control law is derived. This controller ensures the bounded estimation of the unknown dynamic parameters of the robots and the load, the exponential convergence to zero of the velocity tracking errors, and the boundedness of the internal forces. The individual robot joint motions are shown to be stable by decomposing the joint coordinates into two variables, one which is homeomorphic to the load coordinates, the other to the coordinates of the self-motion manifold. The dynamics on the self-motion manifold are directly shown to be related to the concept of zero-dynamics. It is shown that if the reference joint trajectory is selected to optimize a certain type of objective functions, then stable dynamics on the self-motion manifold result. The overall stability of the joint positions is established from the stability of two cascaded dynamic systems involving the two decomposed coordinates.     

Nonlinear generalised dynamic inversion for aircraft manoeuvring control

The nonlinear control problem of aircraft trajectory tracking is tackled in the framework of multiple linear time-varying constrained control using the newly developed paradigm of generalised dynamic inversion. The time differential forms of the multiple constraints encapsulate the control objectives, and are inverted to obtain the reference trajectory-realising control law. The inversion process utilises the Moore–Penrose generalised inverse and the associated nullspace projection, and it predictably involves the problematic generalised inversion singularity. Thus, a singularity avoidance scheme based on a new type of dynamically scaled generalised inverses is introduced that guarantees both asymptotically stable tracking and singularity avoidance. The steady-state closed-loop system allows for two inherently noninterfering control actions working towards a unified goal to exploit the aircraft's control authority over the entire state space. One control action is performed by the particular part of the control law on the range space of the transposed constraint matrix, and it works to impose the prescribed aircraft constrained dynamics. The other control action is performed by the auxiliary part of the control law on the complementary orthogonal nullspace of the constraint matrix, and it provides aircraft's global inner stability using the concept of perturbed feedback linearisation. Numerical simulations of an aggressive multiaxial aircraft coordinated manoeuvre verify the efficacy of designing nonlinear flight control systems via this methodology.     

Canonical parameterization of excess motor degrees of freedom withself-organizing maps

The problem of sensorimotor control is underdetermined due to excess (or "redundant") degrees of freedom when there are more joint variables than the minimum needed for positioning an end-effector. A method is presented for solving the nonlinear inverse kinematics problem for a redundant manipulator by learning a natural parameterization of the inverse solution manifolds with self-organizing maps. The parameterization approximates the topological structure of the joint space, which is that of a fiber bundle. The fibers represent the "self-motion manifolds" along which the manipulator can change configuration while keeping the end-effector at a fixed location. The method is demonstrated for the case of the redundant planar manipulator. Data samples along the self-motion manifolds are selected from a large set of measured input-output data. This is done by taking points in the joint space corresponding to end-effector locations near "query points", which define small neighborhoods in the end-effector work space. Self-organizing maps are used to construct an approximate parameterization of each manifold which is consistent for all of the query points. The resulting parameterization is used to augment the overall kinematics map so that it is locally invertible. Joint-angle and end-effector position data, along with the learned parameterizations, are used to train neural networks to approximate direct inverse functions.     

自由漂浮空间机器人回避动力学奇异的轨迹规划

针对非完整约束导致的自由漂浮空间机器人广义雅可比矩阵动力学奇异性问题,提出一种间接求解机器人逆运动学的方案.该方案通过运动方程分解并构造迭代函数,将动力学奇异转换为运动学奇异问题,避免直接求解逆广义雅可比矩阵,解决广义雅可比矩阵动力学参数相关特性问题,仿真结果表明该方法能够有效地实现自由漂浮空间机器人回避动力学奇异的非完整轨迹规划.     

A Fast Approach for Robot Motion Planning

This paper describes a new approach to robot motion planning that combines the end-point motion planning with joint trajectory planning for collision avoidance of the links. Local and global methods are proposed for end-point motion planning. The joint trajectory planning is achieved through a pseudoinverse kinematic formulation of the problem. This approach enables collision avoidance of the links by a fast null-space vector computation. The power of the proposed planner derives from: its speed; the good properties of the potential function for end-point motion planning; and from the simultaneous avoidance of the links collision, kinematic singularities, and local minima of the potential function. The planner is not defined over computationally expensive configuration space and can be applied for real-time applications. The planner shows to be faster than many previous planners and can be applied to robots with many degrees of freedom. The effectiveness of the proposed local and global planning methods as well as the general robot motion planning approach have been experimented using the computer-simulated robots. Some of the simulation results are included in this paper.