Intelligent control for avoiding the joint limits of redundant planar manipulators

This article presents an intelligent control system for a redundant manipulator to avoid physical limits such as joint angle limits and joint velocity limits. In this method, a back-propagation neural network (NN) is introduced for the kinematic inversion of the manipulator. Since this inverse kinematics has an infinite number of joint angle vectors, a fuzzy-neuro system is constructed to provide an approximate value for that vector. This vector is fed into the NN as a hint input vector in order to guide the output of the NN within the self-motion. Simulations and a comparative study are made based on a four-link redundant manipulator to prove the efficacy of the proposed control system.     

A projected back-tracking line-search for constrained interactive inverse kinematics

Inverse kinematics is the problem of manipulating the pose of an articulated figure in order to achieve a desired goal disregarding inertia and forces. One can approach the problem as a non-linear optimization problem or as non-linear equation solving. The former approach is superior in its generality and ability to generate realistic poses, whereas the latter approach is recognized for its low iteration cost. Therefore, many prefer equation solving over optimization for interactive applications. In this paper we present a projected-gradient method for solving an inverse kinematics problem interactively, which exhibit good performance and precision. The method is compared to existing work in terms of visual quality and accuracy. Our method shows good convergence properties and deals with joint constraints in a simple and elegant manner. Our main contribution lies in an explicit incorporation of joint limits in an interactive solver. This makes it possible to compute the pose in each frame without the discontinuities exhibited by existing key frame animation techniques.     

A geometric approach for inverse kinematics of a 4-link redundant In-Vivo robot for biopsy

The presence of a large number of degrees of freedom enables redundant manipulators to have some desirable features like reaching difficult areas and avoiding obstacles. These manipulators in the form of In-Vivo robots will enhance the dexterity and capacity of a surgeon to explore the internal cavity when inserted in the existing tool channel of the endoscope to take a biopsy from the stomach. This paper presents a simple geometric approach, to solve the problem of multiple inverse kinematic solutions of redundant manipulators, to find a single optimum solution and to easily switch from one solution to another depending upon the path and the environment. A simulation model of this approach has been developed and experiments have been conducted on the In-Vivo robot to judge its effectiveness.     

Collision-avoidance for redundant robots through control of the self-motion of the manipulator

A new method to on-line collision-avoidance of the links of redundant robots with obstacles is presented. The method allows the use of redundant degrees of freedom such that a manipulator can avoid obstacles while tracking the desired end-effector trajectory. It is supposed that the obstacles in the workspace of the manipulator are presented by convex polygons. The recognition of collisions of the links of the manipulator with obstacles results on-line through a nonsensory method. For every link of the redundant manipulator and every obstacle a boundary ellipse is defined in workspace such that there is no collision if the robot joints are outside these ellipses. In case a collision is imminent, the collision-avoidance algorithm compute the self-motion movements necessary to avoid the collision. The method is based on coordinate transformation and inverse kinematics and leads to the favorable use of the abilities of redundant robots to avoid the collisions with obstacles while tracking the end-effector trajectory. This method has the advantage that the configuration of the manipulator after collision-avoidance can be influenced by further requirements such as avoidance of singularities, joint limits, etc. The effectiveness of the proposed method is discussed by theoretical considerations and illustrated by simulation of the motion of three-and four-link planar manipulators between obstacles.     

A method to learn the inverse kinematics of multi-link robots by evolving neuro-controllers

A general method to learn the inverse kinematic of multi-link robots by means of neuro-controllers is presented. We can find analytical solutions for the most used and well-known robots in the literature. However, these solutions are specific to a particular robot configuration and are not generally applicable to other robot morphologies. The proposed method is general in the sense that it is independent of the robot morphology. The method is based on the evolutionary computation paradigm and works obtaining incrementally better neuro-controllers. Furthermore, the proposed method solves some specific issues in robotic neuro-controller learning: it avoids any neural network learning algorithm which relies on the classical supervised input-target learning scheme and hence it lets to obtain neuro-controllers without providing targets. It can converge beyond local optimal solutions, which is one of the main drawbacks of some neural network training algorithms based on gradient descent when applied to highly redundant robot morphologies. Furthermore, using learning algorithms such as the neuro-evolution of augmenting topologies it is also possible to learn the neural network topology which is a common source of empirical testing in neuro-controllers design. Finally, experimental results are provided when applying the method to two multi-link robot learning tasks and a comparison between structural and parametric evolutionary strategies on neuro-controllers is shown.     

面向冗余机器人实时控制的逆运动学求解有效方法

针对7自由度冗余机器人实时运动控制,对机器人逆运动学提出了一种新的求解方法.采用位姿分解方式,使7自由度冗余机器人逆运动学简化为4自由度位置逆运动学求解.在梯度投影法得到位置优化解的基础上,利用机器人封闭解公式求得一组优化解.通过对7自由度机器人仿真分析,表明了该方法的有效性.     

Inverse kinematics and geometrically bounded singularities prevention of redundant manipulators: An Artificial Neural Network approach

This article presents an Artificial Neural Network (ANN) approach for fast inverse kinematics computation and effective geometrically bounded singularities prevention of redundant manipulators. Here, some bounded geometrical concepts are properly utilized to establish some characterizing matrices, to yield a simple performance index, and a null space vector for singularities avoidance/prevention and safe path generation. Then, a properly trained ANN is used in a novel scheme for the computation of inverse kinematics. This scheme includes the proposed null space vector being also computed by another properly trained ANN. The efficiency of the proposed ANN approach is demonstrated in the successful singularities prevention of a planar redundant manipulator performing a benchmark test.     

基于解析法和遗传算法的机械手运动学逆解

研究优化机械手轨迹规划问题,机械手运动时要具有稳定性避障性能。针对平面3自由度冗余机械手优化控制问题,建立机械手的结构模型。提出用解析法和遗传算法相结合满足具有计算量小和适应性强的特点。在给定机械手末端执行器的运动轨迹,按着机械手冗余自由度,运动轨迹上每个点对应的关节角有无穷多个解。而通过算法可以找到一组最优的关节角,可得到优化机械手运动过程中柔顺性和避障点。仿真结果表明,该算法可以快速收敛到全局最优解,可用于计算冗余机械手运动学逆解,并可实现机器人的轨迹规划和避障优化控制。     

A real-time planning algorithm for obstacle avoidance of redundant robots

A computationally efficient, obstacle avoidance algorithm for redundant robots is presented in this paper. This algorithm incorporates the neural networks and pseudodistance function D _p in the framework of resolved motion rate control. Thus, it is well suited for real-time implementation. Robot arm kinematic control is carried out by the Hopfield network. The connection weights of the network can be determined from the current value of Jacobian matrix at each sampling time, and joint velocity commands can be generated from the outputs of the network. The obstacle avoidance task is achieved by formulating the performance criterion as D _p>d _min (d _min represents the minimal distance between the redundant robot and obstacles). Its calculation is only related to some vertices which are used to model the robot and obstacles, and the computational times are nearly linear in the total number of vertices. Several simulation cases for a four-link planar manipulator are given to prove that the proposed collision-free trajectory planning scheme is efficient and practical.     

冗余度分解对冗余度机器人关节弹性变形的影响

当机器人高速运动时,由于关节弹性而产生的末端变形将会影响机器人的跟踪精度．本文以具有弹性关节的冗余度机器人为对象,就冗余度分解对机器人关节弹性变形的影响进行了研究.首先,以关节弹性变形为性能指标,提出了冗余度分解的常量权系数法．然后,在此基础上又进一步提出了两种改进方法,即变量权系数法和切换法．前者是利用变量权系数自动地调整角加速度对总目标函数的影响程度;而后者是利用门槛函数来实现不同的冗余度分解法间的相互切换．同时,还给出了利用这两种改进方法获得关节弹性变形最优解的必要条件．最后,本文以平面3R机器人为例进行了仿真研究,并将不同方法做了比较．研究结果表明,这两种改进方法具有良好的全局特性．     

Smooth continuous transition between tasks on a kinematic control level: Obstacle avoidance as a control problem

Kinematically redundant robots allow simultaneous execution of several tasks with different priorities. Beside the main task, obstacle avoidance is one commonly used subtask. The ability to avoid obstacles is especially important when the robot is working in a human environment. In this paper, we propose a novel control method for kinematically redundant robots, where we focus on a smooth, continuous transition between different tasks. The method is based on a new and very simple null-space formulation. Sufficient conditions for the tasks design are given using the Lyapunov-based stability discussion. The effectiveness of the proposed control method is demonstrated by simulation and on a real robot. Pros and cons of the proposed method and the comparison with other control methods are also discussed.     

排爆机械臂的运动学仿真

在机械臂性能优化设计的研究中,为了使排爆机械臂能够灵活、有效的处理爆炸物,需对其进行运动学仿真,针对所设计的排爆机械臂的机械结构,通过D-H方法建立相应的运动学模型,运用矩阵逆乘的方法分离变量,求得了运动学正解和逆解.用MATLAB平台中机器人工具箱编程并建立ADAMS虚拟样机,对机械臂的末端位移、速度和加速度做了运动学仿真,通过仿真验证了机构设计的合理性和仿真方法的正确性.结果为排爆机器人的结构设计和优化,为排爆机械臂的电机选择提供了依据.     

冗余度机器人全局优化的一种新方法

提出一种冗余度机器人全局优化的新方法．通过引入自运动变量,为冗余度机器人全局优化问题建立了一个简单泛函极值模型,并由此导出了响应的全局优化算法．这种方法建模简单,计算量小,能实现冗余度机器人全局优化的实时控制．     

两栖机器人结构设计及运动学建模

两栖仿生机器人的研究是当前新兴的一个热点,它是军用机器人研究的一个重要分支。提出一种两栖仿生机器人的设计思路,确定了一些关键参数,如电机、游动机构等。并对两栖机器人仿鱼游动的运动学进行建模,最终通过实体仿真验证方案的可行性。     

A study on optimal motion of a biped locomotion machine

In this paper we propose a calculation method for the optimal trajectory of a biped locomotion machine which is based on inverse kinematics and inverse dynamics. First, the trajectory of the waist is expressed by a Fourier series, where the bases are selected appropriately so that the periodic boundary conditions are strictly satisfied. A biped locomotion machine establishes optimal walking by using kicking forces to the ground at the moment of switching legs. In order to include the effecs of the kicking forces, additional terms that indicate the impulsive forces at the moment of switching legs are included in the formulation. Then the angles of each joint are determined by inverse kinematics, and using inverse dynamics, the input torques of each joint are expressed in terms of Fourier coefficients. By defining the performance index as a quadratic form of the input torques, the motion planning problem is formulated as an optimization problem of the trajectory of the waist, whose paramaters are Fourier coefficients of the trajactory of the waist. Using the successive quadratic programming (SQP) method, the optimal trajectory of a biped locomotion machine is obtained. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–21, 1998.     

冗余驱动并联机械手的混合位置/力自适应控制

针对冗余驱动并联机构研究一种自适应的混合位置/力控制算法.基于并联机构中约束子流形的几何性质,将冗余驱动并联机构的逆动力学自然投影到位形空间和约束力空间.基于投影方程,提出一种统一的具有渐进稳定性的自适应混合位置/力控制算法.采用最小二范数准则求解冗余解问题,实现了实际驱动关节力矩的优化.仿真结果验证了控制方法的有效性.