A differential-geometric approach for 2D and 3D object grasping and manipulation

This article presents an expository work on a differential-geometric treatment of fundamental problems of 2D and 3D object grasping and manipulation by a pair of robot fingers with multi-joints under holonomic or nonholonomic constraints. First, Lagrange’s equation of motion of a fingers-object system whose motion is confined to a vertical plane is derived under holonomic constraints when rolling contacts between finger-ends and object surfaces are permitted. Then, a class of control signals called “blind grasping” and constructed without knowing the object kinematics or using any external sensing like vision or tactile sensation is shown to realize stable object grasping in a dynamic sense. Stability of motion and its convergence to an equibrium manifold are treated on the basis of differential geometry of solution trajectories of the closed-loop dynamics on the constraint manifolds. Second, a mathematical model of 3D object grasping and manipulation by a pair of multi-joint robot fingers is derived under the assumption that spinning motion of rotation around the opposing axis between contact points does no more arise. It is shown that, differently from the 2D case, the instantaneous axis of rotation of the object is time-varying, which induces a nonholonomic constraint expressed as a linear differential equation of rotational motion of the pinched object. It is shown that there is a class of control signals constructed without knowing the object kinematics or using external sensings that can realize “blind grasping” in a dynamic sense. Finally, it is shown that the proposed differential geometric treatment of stability can naturally cope with redundancy resolution problems of surplus degrees-of-freedom (d.f.) of the overall fingers-object system, which is closely related to Bernstein’s d.f. problem.     

Dynamic force/torque balance of 2D polygonal objects by a pair of rolling contacts and sensory‐motor coordination

It is well known that three frictionless fingers suffice to immobilize any 2D object with triangular shape but four fingers are necessary for a parallelepiped. However, it has been recently shown that only two fingers are enough to realize secure grasp of a rigid object with parallel flat surfaces in a dynamic sense if finger ends have a hemispherical shape with appropriate radius and thereby rollings are induced between finger ends and object surfaces. This paper focuses on the two problems: (1) dynamic force/torque balance of 2D polygonal objects under the effect of gravity force by means of a pair of rolling contacts and (2) concurrent realization of dynamically secure grasp and orientation control of 2D polygonal objects by using a pair of multi‐fingered hands with hemispherical ends and sensory feedback signals without knowing object kinematics and mass center. It is shown that the force/torque balance can be attained by controlling both the contact positions and inducing adequate forces in both normal and tangential directions at each of contact points indirectly through finger joints without knowing object mass center and other kinematic parameters. © 2003 Wiley Periodicals, Inc.     

Dynamics and Stability of Blind Grasping of a 3-Dimensional Object under Non-holonomic Constraints

A mathematical model expressing the motion of a pair of multi-DOF robot fingers with hemi-spherical ends, grasping a 3-D rigid object with parallel flat surfaces, is derived, together with non-holonomic constraints. By referring to the fact that humans grasp an object in the form of precision prehension, dynamically and stably by opposable forces, between the thumb and another finger (index or middle finger), a simple control signal constructed from finger-thumb opposition is proposed, and shown to realize stable grasping in a dynamic sense without using object information or external sensing (this is called "blind grasp" in this paper). The stability of grasping with force/torque balance under non-holonomic constraints is analyzed on the basis of a new concept named "stability on a manifold". Preliminary simulation results are shown to verify the validity of the theoretical results.     

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.

     

Dexterous manipulation of an object by means of multi‐DOF robotic fingers with soft tips

This article analyzes the dynamics of motion of various setups of two multiple degree‐of‐freedom (DOF) fingers that have soft tips, in fine manipulation of an object, and shows performances of their motions via computer simulation. A mathematical model of these dynamics is described as a system of nonlinear differential equations expressing motion of the overall fingers‐object system together with algebraic constraints due to tight area contacts between the finger‐tips and surfaces of the object. First, problems of (1) dynamic, stable grasping and (2) regulation of the object rotational angle by means of a setup of dual two‐DOF fingers, are treated. Second, the problem of regulating the position of the object mass center by means of a pair of two‐DOF and three‐DOF fingers is considered. Third, a set of dual three‐DOF fingers is treated, in order to let it perform a sophisticated task, which is specified by a periodic pattern of the object posture and a constant internal force. In any case, there exist sensory‐motor coordinations, which are described by analytic feedback connections from sensing to actions at finger joints. In the cases of setpoint control problems, convergences of motion to secure grasping together with the specified object rotational angle and/or the specified object mass center position, are proved theoretically. A constraint stabilization method (CSM) is used for solving numerically the differential algebraic equations to show performances of the proposed sensory‐feedback schemes. © 2002 Wiley Periodicals, Inc.     

Coordination and Control of Multi-fingered Robot Hands with Rolling and Sliding Contacts

In this paper, the problem of controlling multi-fingered robot hands with rolling and sliding contacts is addressed. Several issues are explored. These issues involve the kinematic analysis and modeling, the dynamic analysis and control, and the coordination of a multi-fingered robot hand system. Based on a hand-object system in which the contacts are allowed to both roll and slide, a kinematic model is derived and analyzed. Also, the dynamic model of the hand-object system with relative motion contacts is studied. A control law is proposed to guarantee the asymptotic tracking of the object trajectory together with the desired rolling and/or sliding motions along the surface of the object. A planning approach is then introduced to minimize the contact forces so that the desired motion of the object and the relative motions between the fingers and the object can be achieved. Simulation results which support the theoretical development are presented.     

基于深度神经网络的多指灵巧手抓取手势优化

基于深度神经网络模型,提出了一种适用于多指灵巧手的抓取手势优化方法。首先,在仿真环境下构建了一个抓取数据集,并在此基础上训练了一个卷积神经网络,依据目标物体单目视觉信息和多指灵巧手抓取位形来预测抓取质量函数,由此可以将多指灵巧手的抓取规划问题转化为使抓取质量最大化的优化问题,进一步,基于深度神经网络中的反向传播和梯度上升算法实现多指灵巧手抓取手势的迭代与优化。在仿真环境中,比较该网络和仿真平台对同一抓取位形的抓取质量评估结果,再利用所提出的优化方法对随机搜索到的初始手势进行优化,比较优化前后手势的力封闭指标。最后,在实际机器人平台上验证本文方法的优化效果,结果表明,本文方法对未知物体的抓取成功率在80%以上,对于失败的抓取,优化后成功的比例达到90%。     

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.     

A human-like real-time grasp synthesis method for humanoid robot hands

This paper addresses a real-time grasp synthesis of multi-fingered robot hands to find grasp configurations which satisfy the force closure condition of arbitrary shaped objects. We propose a fast and efficient grasp synthesis algorithm for planar polygonal objects, which yields the contact locations on a given polygonal object to obtain a force closure grasp by a multi-fingered robot hand. For an optimum grasp and real-time computation, we develop the preference and the hibernation process and assign the physical constraints of a humanoid hand to the motion of each finger. The preferences consist of each sublayer reflecting the primitive preference similar to the conditional behaviors of humans for given objectives and their arrangements are adjusted by the heuristics of human grasping. The proposed method reduces the computational time significantly at the sacrifice of global optimality, and enables grasp posture to be changeable within 2-finger and 3-finger grasp. The performance of the presented algorithm is evaluated via simulation studies to obtain the force-closure grasps of polygonal objects with fingertip grasps. The architecture suggested is verified through experimental implementation to our developed robot hand system by solving 2- or 3-finger grasp synthesis.     

Dexterity and versatility in the pinching motion of robot fingers with multi-degrees of freedom

This paper focuses on dexterity and versatility in pinching a rectangular object by a pair of robot fingers based on sensory feedback. In the pinching motion of humans, it is possible to execute concurrent pinching and orientation control quickly and precisely by using only the thumb and index finger. However, it is not easy for robot fingers to perform such imposed tasks agilely and simultaneously. In the case of robotic grasping, to perform concurrently such plural tasks retards the convergence speed in the execution of the overall task. This means that in order to increase versatility by imposing additional tasks, dexterity in the execution of each task may deteriorate. In this paper it is shown that both dexterity and versatility in the execution of such imposed tasks can be enhanced remarkably, without any deterioration in dexterity in the execution of each task, by using a sensory feedback method based on the idea of role-sharing joint control which comes from observation of the functional role of each human finger joint.     

Multi-fingered robot hand optimal task force distribution: Neural inverse kinematics approach

Grasping and manipulation force distribution optimization of multi-fingered robotic hands can be formulated as a problem for minimizing an objective function subject to form-closure constraints, kinematics, and balance constraints of external force. In this paper we present a novel neural network for dexterous hand-grasping inverse kinematics mapping used in force optimization. The proposed optimization is shown to be globally convergent to the optimal grasping force. The approach followed here is to let an artificial neural network (ANN) learn the nonlinear inverse kinematics functional relating the hand joint positions and displacements to object displacement. This is done by considering the inverse hand Jacobian, in addition to the interaction between hand fingers and the object. The proposed neural-network approach has the advantages that the complexity for implementation is reduced, and the solution accuracy is increased, by avoiding the linearization of quadratic friction constraints. Simulation results show that the proposed neural network can achieve optimal grasping force.     

Grasp stability analysis based on acceleration convex polytopes for multi-fingered robot hands

In this paper, we present the analysis of grasp stability for multi-fingered robot hands that is based on translational and rotational acceleration convex polytopes. The aim of the grasp stability analysis is to find the resistance forces and moments of robot hands that can withstand the external disturbance forces and moments applied on objects. We calculate the resistance forces and moments respectively which are considered the properties of objects and robots. Therefore, the resistance forces and moments depend on the joint driving torque limits, the posture and the mass of robot fingers, the configuration and the mass of objects, the grasp position, the friction coefficients between the object surface and the end-effectors of robot fingers. We produce the critical resistance force and moment which are absolutely stable about external disturbances in all directions, the global resistance force and moment which are whole grasp capability of robot hands, and the weighted resistance forces and moments which can be properly used by controlling two indices according to the importance of robot hands. The effectiveness of this method is verified with simulation examples. Recommended by Editorial Board member Hyoukryeol Choi under the direction of Editor Jae-Bok Song. Myeong Eon Jang received the B.S. and M.S. degrees in Mechanical Engineering from Chonnam National University, Gwangju, Korea in 1987 and 1990, the Ph.D. degree in the Department of Mechatronics Engineering at Chungnam National University, Daejeon, Korea in 2009, respectively. Since 1993, he has been a Researcher in the Agency for Defense Development (ADD), Daejeon, Korea. His research interests include robotics and intelligent control. Jihong Lee received the B.S. degree in Electronics Engineering from Seoul National University, Korea in 1983, and the M.S. and Ph.D. degrees from the Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea, in 1985 and 1991, respectively, all in Electrical and Electronics Engineering. Since 2004, he has been a Professor in the Mechatronics Engineering Department of Chungnam National University, Daejeon, Korea. His research interests include robotics, intelligent control, multi-robot localization and path planning.     

多自由度仿生假手嵌入式控制系统及其抓取策略

为一种能够实现5 指独立动作以及具备人机交互能力的多自由度仿生假手设计了手部嵌入式控制系 统．该系统由传感器系统和运动控制系统构成,集成于假手机体内部,通过通信总线与上层控制器交换信息．传感 器系统包括3 种类型,共12 个传感器,可为假手自主抓取以及人机交互中的感觉反馈提供数据,运动控制系统用于 控制、驱动各手指动作．此外,本文以基于位置的阻抗控制为底层,以动作预构形为上层设计了分层控制策略．实 验表明,该嵌入式控制系统和分层控制策略使假手实现了自主抓取功能,提高了抓取的柔顺性、稳定性和适应性．     

The caging configuration design and optimization for planar moving objects using multi-fingered mechanism

Due to the larger number of fingers, and the relative motion between object and fingers, the moving object caging using multi-fingered mechanism is complicated and difficulty. In this paper, a novel algorithm for planar moving objects caging configuration design and optimization using multi-fingered mechanism is proposed. In the proposed algorithm: (1) the lead finger is chosen and controlled to track the caging point nearby the moving object; (2) possible configurations of following fingers are determined according to interconnections of the multi-fingered mechanism; (3) the caging condition is applied to choose effective caging configurations. Compared to existing methods, advantages of the proposed method lie on the simple task mode – ‘leader-follower mode’ and low calculation burden. In addition, considering caging is a loose closure strategy with a certain margin, the caging margin, which is the space between the multi-fingered mechanism and the moving target, is introduced and quantified. Moreover, the caging configurations are optimized to maximize the caging margin to provide maximum local freedom for the object. For verifying the efficiency of the proposed method, it is applied in the rectangular and regular pentagonal shaped moving objects caging problems, and the simulation results illustrate the validity of the proposed algorithm.     

An overview of 3D object grasp synthesis algorithms

This overview presents computational algorithms for generating 3D object grasps with autonomous multi-fingered robotic hands. Robotic grasping has been an active research subject for decades, and a great deal of effort has been spent on grasp synthesis algorithms. Existing papers focus on reviewing the mechanics of grasping and the finger–object contact interactions Bicchi and Kumar (2000) [12] or robot hand design and their control Al-Gallaf et al. (1993) [70]. Robot grasp synthesis algorithms have been reviewed in Shimoga (1996) [71], but since then an important progress has been made toward applying learning techniques to the grasping problem. This overview focuses on analytical as well as empirical grasp synthesis approaches.     

Towards autonomous robotic servicing: Using an integrated hand-arm-eye system for manipulating unknown objects

Executing complex robotic tasks including dexterous grasping and manipulation requires a combination of dexterous robots, intelligent sensors and adequate object information processing. In this paper, vision has been integrated into a highly redundant robotic system consisting of a tiltable camera and a three-fingered dexterous gripper both mounted on a puma-type robot arm. In order to condense the image data of the robot working space acquired from the mobile camera, contour image processing is used for offline grasp and motion planning as well as for online supervision of manipulation tasks. The performance of the desired robot and object motions is controlled by a visual feedback system coordinating motions of hand, arm and eye according to the specific requirements of the respective situation. Experiences and results based on several experiments in the field of service robotics show the possibilities and limits of integrating vision and tactile sensors into a dexterous hand-arm-eye system being able to assist humans in industrial or servicing environments.     

Stability index and contact configuration planning for multifingered grasp

It is necessary to plan the contact configuration to guarantee a stable grasp. This article discusses the grasping stability of multifingered robot hands. The fingers are assumed to be point contacts with friction. A stability index for evaluating a grasp, which is proportional to the ellipsoidal volume in the grasping task space, is proposed. The invariance of the index is proved under an object linear coordinate transformation and under a change of the torque origin. The similar invariance of the index is also proved under a change of the dimensional unit. The optimal grasping of an object by a multifingered robot hand can be obtained using the stability index to plan the grasp configurations. The index is applicable to plan adaptable fixtures as well. A nonlinear programming method to plan configurations is addressed. Several examples are given using the index to evaluate a grasp, in which the obtained optimal grasping is consistent with what human beings expect. The sensibility of the optimal grasping is analyzed in these examples. © 1998 John Wiley & Sons, Inc.     

Virtual grasp release method and evaluation

We address a “sticking object” problem for the release of whole-hand virtual grasps. The problem occurs when grasping techniques require fingers to be moved outside an object's boundaries after a user's (real) fingers interpenetrate virtual objects due to a lack of physical motion constraints. This may be especially distracting for grasp techniques that introduce mismatches between tracked and visual hand configurations to visually prevent interpenetration. Our method includes heuristic analysis of finger motion and a transient incremental motion metaphor to manage a virtual hand during grasp release. We integrate the method into a spring model for whole-hand virtual grasping to maintain the physically-based pickup and manipulation behavior of such models. We show that the new spring model improves release speed and accuracy based on pick-and-drop, targeted ball-drop, and cube-alignment experiments. In contrast to a standard spring-based grasping method, measured release quality does not depend notably on object size. Users subjectively prefer the new approach and it can be tuned to avoid potential side effects such as increased drops or visual distractions. We further investigated a convergence speed parameter to find the subjectively good range and to better understand tradeoffs in subjective artifacts on the continuum between pure incremental motion and rubber-band-like convergence behavior.