具有滚动接触的多指手操作运动学及其算法

研究多指手滚动操作的运动学及其算法.简要介绍了滚动接触运动方程,根据接触的 运动学约束,建立了描述物体与关节速度关系的关节--物体运动方程,并给出物体与手指表 面间相对角速度的表达式.得到的关节--物体运动方程、相对角速度表达式和接触运动方程 构成了形式简洁的滚动操作运动学方程.结合对方程的分析,进一步给出了多指手滚动操作物 体跟踪期望的运动轨迹时,关节运动轨迹的生成算法.     

基于非完整运动规划的多指手灵巧操作

灵巧操作是多指手操作的一个重要方面,探讨实现这种操作的纯滚动方法.首先利 用纯滚动的非完整特性,进行非完整运动的最优路径规划,然后根据规划结果应用多指手操 作运动学方程确定手指的运动.为多指手灵巧操作的实现提供了一条途径.     

灵巧手运动学分析

本文研究了灵巧手操作物体的运动学问题,在分析手指与物体接触条件的基础上,建立了手指与物体之间单点接触作纯滚动和作可控制滑动的运动学方程,给出了3R,4R 和5R 手指的运动约束条件,并对5-4-5型灵巧手进行了速度仿真和图形仿真.     

多指手操作:运动学算法和实验

本文探讨了多指手的运动学操作问题，即给出被抓物体所要运动的轨迹，如何用多指手进行操作以实现物体的运动，也即怎样根据物体的运动来确定各手指的运动．我们提出和考察了三种运动学操作算法：广义逆算法、接触轨迹控制算法和抓持优化算法．每种算法各有其特点，可根据具体的操作任务加以采用，甚至可综合用于复杂操作任务的不同阶段．在实验系统ＨＫＵＳＴＨＡＮＤ上以两指手操作蓝球的实验实现了这些算法，考察了其可行性和有效性．     

机器人多指操作的递阶控制

为机器人多指协调操作建立一递阶控制系统.给定一操作任务,任务规划器首先生 成一系列物体的运动速度;然后,协调运动规划器根据期望的物体运动速度生成期望的手指 运动速度和期望的抓取姿态变化;同时,抓取力规划器为平衡作用在物体上的外力,根据当前 的抓取姿态,生成各手指所需的抓取力;最后,系统将手指的期望运动速度与为实现期望抓取 力而生成的顺应速度合并,并通过手指的逆雅可比转化为手指关节运动速度后,由手指的关 节级运动控制器实现手指的运动和抓取力的控制.该控制方法已成功应用于香港科技大学 (HKUST)灵巧手控制系统的开发.实验证明该方法不仅能完成物体轨迹的跟踪控制任务, 而且能完成物体对环境的力控制和力与速度的混合控制.     

欠驱动多指节机器人手的仿真实现

欠驱动多指节机器人手具有较复杂的非线性运动方程组以及运动过程的阶段性,公用Ｓｉｍｕｌｉｎｋ仿真系统一般很难实现这种机器人手的动态仿真为此提出用Ｍａｔｌａｂ语言编写的Ｍ－文件与Ｓｉｍｕｌｉｎｋ的仿真系统之间的合理组合,实现多指节手的动态仿真过程。文中用于欠驱动多指节机器人手的动态仿真模型能够发映手指操作的起初过程,并能简化仿真系统的复杂性,这为多指节手的运动、受力分析以及机构、控制设计等提供了有效工     

基于物体目标阻抗的多指手协调混合阻抗控制的研究

本文在多指手协调控制的基础上,提出了协调混合阻抗控制方法。在不同方向引入位置或力控制的物体目标阻抗,根据多指手协调控制的动力学方程设计计算力矩控制器,并对ＢＨ－１手抓持物体在自由空间和受限空间运动进行了仿真研究。结果表明,采用协调混合阻抗控制可使物体在被抓持过程中按期望的位置和力轨迹运动,且具有较好的动态性能。     

三指机器人手的运动学研究

本文研究了指端与物体间为纯滚动接触时三指9关节机器人手操作物体的运动学问题,建立了手指关节运动与物体运动之间的位置关系、速度关系和加速度关系,给出了9个节间的运动约束条件。     

机器人灵活手的运动分析和力分析

机器人灵活手可以稳定地抓持任意形状物体,或利用手指的运动操纵物体相对于机器人末杆(或手掌)的运动.它的运动学和力传递关系比一般开链机器人复杂得多.本文分析了在被抓持物体与手指指尖,手指指尖与手指关节之间力和虚位移的关系.利用线性变换的理论揭示了过约束、欠约束和奇异状态的形成条件.本文还分析了手指机构冗余自由度、亏缺自由度和奇异位形对抓持的影响.这些结果为机器人灵活手的设计和控制方案的规划提供了理论依据.     

灵巧手操作的重构规划

根据人类利用滑动或滚动方式进行灵巧操作的几种模式 ,在多指灵巧手—物体的抓持系统中 ,把实现这些灵巧操作模式的规划问题作为一个位形空间的重构问题进行全局和局部级的操作规划 ,开发了实现灵巧操作的规划算法 .仿真结果表明了方法的有效性和正确性 .     

gripper

Grasping of objects has been a challenging task for robots. The complex grasping task can be defined as object contact control and manipulation subtasks. In this paper, object contact control subtask is defined as the ability to follow a trajectory accurately by the fingers of a gripper. The object manipulation subtask is defined in terms of maintaining a predefined applied force by the fingers on the object. A sophisticated controller is necessary since the process of grasping an object without a priori knowledge of the object's size, texture, softness, gripper, and contact dynamics is rather difficult. Moreover, the object has to be secured accurately and considerably fast without damaging it. Since the gripper, contact dynamics, and the object properties are not typically known beforehand, an adaptive critic neural network (NN)-based hybrid position/force control scheme is introduced. The feedforward action generating NN in the adaptive critic NN controller compensates the nonlinear gripper and contact dynamics. The learning of the action generating NN is performed on-line based on a critic NN output signal. The controller ensures that a three-finger gripper tracks a desired trajectory while applying desired forces on the object for manipulation. Novel NN weight tuning updates are derived for the action generating and critic NNs so that Lyapunov-based stability analysis can be shown. Simulation results demonstrate that the proposed scheme successfully allows fingers of a gripper to secure objects without the knowledge of the underlying gripper and contact dynamics of the object compared to conventional schemes.     

Learning Motion of Dexterous Manipulation For A Pair of Multi‐DOF Fingers With Soft‐TIPS

This paper shows that a pair of dual multi‐DOF fingers with soft‐tips can learn iteratively a desired periodic motion of manipulation of an object if sensory feedback signals are designed adequately. It is shown that dynamics of the overall fingers and object system satisfy passivity but residual error dynamics for a given periodic posture of the object and a fixed value of contact force satisfy output‐dissipativity only in an approximate sense. Numerical simulation results are presented which show that the pair of fingers manipulating an object is capable of learning iteratively a variety of dexterous motions with a good performance.     

Control of twisting manipulation using a multi-fingered robotic hand with a common rotational axis

The main purpose of the present study is to prove the usability of a mechanism with a common rotational axis during twisting manipulation using a multi-fingered robotic hand where two fingers and two other fingers can independently rotate in inner and outer circles with a dual turning mechanism. Although various types of conventional multi-fingered hands have potential capability to achieve twisting manipulations such as opening a bottle cap from within a hand, it is well-known that such tasks are difficult to execute quickly due to limited working space of the fingers and complexity of control. The proposed hand with a common rotational axis is effective in rotational manipulation around a particular axis, where each joint role assignment is completely decoupled into internal force control for grasping an object and velocity control around the axis for rotating the object. We prove the usability of this mechanism with a common rotational axis through the use of a control scheme, and show experimental results involving manipulation tasks where twisting manipulation is dominant.

     

Intelligent control of multi-fingered hands

This paper is concerned with intelligent control for grasping and manipulation of an object by multi-fingered robot hands with rigid or soft hemispheric finger ends that induce rolling contacts with the object. Even in the case of 2D motion like pinching by means of a pair of multi-degrees of freedom robot fingers, there arises an interesting family of Lagrange’s equations of motion with many geometric constraints, which are under-actuated, redundant, and non-holonomic in some sense. Regardless of underactuation of dynamics, it is possible to find a class of sensory feedback signals that realize secure grasp of an object together with control of object orientation. In regard to the secure grasping, a problem of force/torque closure for 2D objects in a dynamic sense plays a crucial role. It is shown that proposed sensory feedback signals satisfying the dynamic force/torque closure can be constructed without knowing object kinematic parameters and location of the mass center. To prove the convergence of motion of the overall fingers–object system under the circumstance of redundancy of joints, new concepts called “stability on a manifold” and “asymptotic stability on a manifold” are introduced. Based on the results found for intelligent control of robotic hands, the last two sections attempt to discuss why human multi-fingered hands can become so dexterous at grasping and object manipulation.     

Whole-finger manipulation with a two-fingered robot hand

This paper describes a method for whole-finger rolling manipulation using a two-fingered robot hand. 'Whole-finger' refers to the use of the complete phalangeal surface during the manipulation. An example of whole-finger manipulation by the human hand is the rolling of a pen between two fingers. The proposed method is based on a two-dimensional model for modelling an object manipulation and is derived from a study of the movement of the contact line between both fingers. Also, the method uses tactile sensor information to estimate the contact point position together with the local curvature of the object. This whole-finger dexterous manipulation is demonstrated on a prototype two-fingered hand. This 5 d.o.f. hand consists of a tendon driven index and thumb, and is equipped with force and tactile sensors. The dimensions and performance of this device are 'human-sized'. A hybrid force-position control scheme is used. The hierarchical control structure is implemented on a dual transputer system. This paper first describes the kinematic model used for whole-finger manipulation. In the second part, the main emphasis is put on the mechanical design and on the transputer-based control system.     

In-hand recognition and manipulation of elastic objects using a servo-tactile control strategy

Grasping and manipulating objects with robotic hands depend largely on the features of the object to be used. Especially, features such as softness and deformability are crucial to take into account during the manipulation tasks. Indeed, positions of the fingers and forces to be applied by the robot hand when manipulating an object must be adapted to the caused deformation. For unknown objects, a previous recognition stage is usually needed to get the features of the object, and the manipulation strategies must be adapted depending on that recognition stage. To obtain a precise control in the manipulation task, a complex object model is usually needed and performed, for example using the Finite Element Method. However, these models require a complete discretization of the object and they are time-consuming for the performance of the manipulation tasks. For that reason, in this paper a new control strategy, based on a minimal spring model of the objects, is presented and used for the control of the robot hand. This paper also presents an adaptable tactile-servo control scheme that can be used in in-hand manipulation tasks of deformable objects. Tactile control is based on achieving and maintaining a force value at the contact points which changes according to the object softness, a feature estimated in an initial recognition stage.     

A real-time strategy for dexterous manipulation: Fingertips motion planning,force sensing and grasp stability

This paper presents a global strategy for object manipulation with the fingertips with an anthropomorphic dexterous hand: the LMS Hand of the ROBIOSS team from PPRIME Institute in Poitiers (France). Fine manipulation with the fingertips requires to compute on one hand, finger motions able to produce the desired object motion and on the other hand, it is necessary to ensure object stability with a real time scheme for the fingertip force computation. In the literature, lot of works propose to solve the stability problem, but most of these works are grasp oriented; it means that the use of the proposed methods are not easy to implement for online computation while the grasped object is moving inside the hand. Also simple real time schemes and experimental results with full-actuated mechanical hands using three fingers were not proposed or are extremely rare. Thus we wish to propose in a same strategy, a robust and simple way to solve the fingertip path planning and the fingertip force computation. First, finger path planning is based on a geometric approach, and on a contact modelling between the grasped object and the finger. And as force sensing is required for force control, a new original approach based on neural networks and on the use of tendon-driven joints is also used to evaluate the normal force acting on the finger distal phalanx. And an efficient algorithm that computes fingertip forces involved is presented in the case of three dimensional object grasps. Based on previous works, those forces are computed by using a robust optimization scheme.In order to validate this strategy, different grasps and different manipulation tasks are presented and detailed with a simulation software, SMAR, developed by the PPRIME Institute. And finally experimental results with the real hand illustrate the efficiency of the whole approach.     

A General Approach for Dexterous Manipulation Planning Based on Configuration Space Structuring

In this paper, we propose a new method for the dexterous manipulation planning problem, under quasi-static movement assumption. This method computes both object and finger trajectories as well as finger relocation sequence and applies to every object shape and hand geometry. It relies on the exploration of the particular subspaces GS k that are the subspaces of all the grasps that can be achieved for a given set of k grasping fingers. The originality is to use continuous paths in these subspaces to directly link two configurations. The proposed approach captures the GS k connectivity in a graph structure. The answer of the manipulation planning query is then given by searching a path in the computed graph. Another specificity of our technique is that it considers manipulated object and hand as an only system, unlike most existing methods that first compute object trajectory then fingers trajectories and thus can not find a solution in all situations. Simulation experiments were conducted for different dexterous manipulation task examples to validate the proposed method.     

机器人多指操作的递阶控制1)

This paper presents a hierarchical control system for robot multifingered coordinate manipulation. Given a manipulation,the task planner generates a sequence of object's motion velocities at first,and then generates for coordinate motion the desired velocities of finger's motion and desired orientation change of the grasped object according to the desired velocities of object's motion.At the same time,the force planner generates the grasp forces on the fingers in order to resist the external forces on the object,according to the grasp posture.Finally,the system generates a result compliance velocity from both the desired finger's velocities and desired grasp forces,and transfers it into joint velocites through the finger's inverse Jacobian.Then the controller of joint motion implements the control of both forces and velocities for the fingers.The approach has been applied to the development of control system HKUST dexterous hand successfully.Experiment results show that it is not only possible to trail and control the object's track,but also possible to realize force control and the hybrid control of both forces and velocities through this method.