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1.
Kinematics of grasping and manipulation by a multifingered robotic hand where multifinger surfaces are in contact with an object is solved. The surface of the object was represented by B‐spline surfaces to model objects of various shapes. The fingers were modeled by cylindrical links and a half ellipsoid fingertip. Geometric contact equations have been solved for all possible contact combinations between the finger surface and the object. The simulation system calculated joint displacements and contact locations for a given trajectory of the object. Since there are no closed form solutions for contact or intersection between these surfaces, kinematics of grasping was solved by recursive numerical calculation. The initial estimate of the contact point was obtained by approximating the B‐spline surface by a polyhedron. As for the simulation of manipulation, exact contact locations were updated by solving the contact equations according to the given contact conditions such as pure rolling, twist‐rolling, or slide‐twist‐rolling. Several examples of simulation of grasping and manipulation are presented. ©1999 John Wiley & Sons, Inc. 相似文献
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Diego R. Faria Ricardo Martins Jorge Lobo Jorge Dias 《Robotics and Autonomous Systems》2012,60(3):396-410
Humans excel in manipulation tasks, a basic skill for our survival and a key feature in our manmade world of artefacts and devices. In this work, we study how humans manipulate simple daily objects, and construct a probabilistic representation model for the tasks and objects useful for autonomous grasping and manipulation by robotic hands. Human demonstrations of predefined object manipulation tasks are recorded from both the human hand and object points of view. The multimodal data acquisition system records human gaze, hand and fingers 6D pose, finger flexure, tactile forces distributed on the inside of the hand, colour images and stereo depth map, and also object 6D pose and object tactile forces using instrumented objects. From the acquired data, relevant features are detected concerning motion patterns, tactile forces and hand-object states. This will enable modelling a class of tasks from sets of repeated demonstrations of the same task, so that a generalised probabilistic representation is derived to be used for task planning in artificial systems. An object centred probabilistic volumetric model is proposed to fuse the multimodal data and map contact regions, gaze, and tactile forces during stable grasps. This model is refined by segmenting the volume into components approximated by superquadrics, and overlaying the contact points used taking into account the task context. Results show that the features extracted are sufficient to distinguish key patterns that characterise each stage of the manipulation tasks, ranging from simple object displacement, where the same grasp is employed during manipulation (homogeneous manipulation) to more complex interactions such as object reorientation, fine positioning, and sequential in-hand rotation (dexterous manipulation). The framework presented retains the relevant data from human demonstrations, concerning both the manipulation and object characteristics, to be used by future grasp planning in artificial systems performing autonomous grasping. 相似文献
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《Advanced Robotics》2013,27(1):101-114
This paper proposes a method of acquisition of a page turning skill for a multifingered robotic hand using reinforcement learning. The goal of this paper is generation of the manipulation skill of a flexible object without its explicit model and tactile sensation during its control. In this paper, a page turning task is considered as an example of such tasks, where a sheet of paper, generally used in books, is a flexible object. The fingertip trajectories are obtained by reinforcement learning based on simulation. The reward considering a friction condition is given, so that a page turning skill without slip between the finger and the paper is obtained. The validity is confirmed by an experimental system which consists of a 2-d.o.f. manipulator and two 2-d.o.f. fingers. 相似文献
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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. 相似文献
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Mohamed Zribi Jun Chen Magdi S. Mahmoud 《Journal of Intelligent and Robotic Systems》1999,24(2):125-149
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. 相似文献
7.
机器人多指手协调操作物体时,合理地确定手指对被操作物体的作用力是必要的.本文将手指尖与被操作物体之间接触模拟为具有摩擦的点接触,对由多指手与被操作物体组成的这样一个速度较低的系统作了静力分析,并对多指手操作物体时的操作力作了合理的分配,提出基于力矩最小的内力的最优计算方法,在计算中,充分考虑到手指只能推而不能拉物体这一实事.最后,以4个手指操作一个圆柱形物体为例.对操作过程作了图形仿真. 相似文献
8.
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. 相似文献
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《Robotics, IEEE Transactions on》2009,25(6):1319-1331
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《Advanced Robotics》2013,27(3):245-261
—This paper reviews the current state of the art and predicts the outlook in robotic tactile sensing for real-time control of dextrous manipulation. We begin with an overview of human touch sensing capabilities and draw lessons for robotic manipulation. Next, tactile sensor devices are described, including tactile array sensors, force-torque sensors, and dynamic tactile sensors. The information provided by these devices can be used in manipulation in many ways, such as finding contact locations and object shape, measuring contact forces, and determining contact conditions. Finally, recent progress in experimental use of tactile sensing in manipulation is discussed, and future directions for research in sensing and control are considered. 相似文献
13.
Since sensory feedback is an important part of robot control and the acquisition, manipulation, and recognition of objects, incorporating a sense of touch into a robotic system can greatly enhance the performance of that system. This article describes the evaluation of a recently developed low-resolution tactile array sensor pad system for use in robotic applications. Computer algorithms are developed which acquire data from the sensor pad and display the data on a CRT screen. Vision algorithms are implemented in order to extract the necessary information from the tactile data which will aid in the acquisition, manipulation, and recognition of objects. An object's pose is estimated by calculating its center of gravity (position) and principal axis (orientation). Recognizing an object and distinguishing between different objects is accomplished by implementing algorithms which estimate an object's perimeter (shape) and area (size). This work demonstrates that a low-resolution tactile array sensor is capable of providing the information that is required for many robotic applications in which objects must be acquired, manipulated, and recognized. Such a system provides a low-cost alternative to more conventional vision-based systems. 相似文献
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Cecilia Laschi Gioel Asuni Eugenio Guglielmelli Giancarlo Teti Roland Johansson Hitoshi Konosu Zbigniew Wasik Maria Chiara Carrozza Paolo Dario 《Autonomous Robots》2008,25(1-2):85-101
This paper presents a sensory-motor coordination scheme for a robot hand-arm-head system that provides the robot with the capability to reach an object while pre-shaping the fingers to the required grasp configuration and while predicting the tactile image that will be perceived after grasping. A model for sensory-motor coordination derived from studies in humans inspired the development of this scheme. A peculiar feature of this model is the prediction of the tactile image. The implementation of the proposed scheme is based on a neuro-fuzzy module that, after a learning phase, starting from visual data, calculates the position and orientation of the hand for reaching, selects the best-suited hand configuration, and predicts the tactile feedback. The implementation of the scheme on a humanoid robot allowed experimental validation of its effectiveness in robotics and provided perspectives on applications of sensory predictions in robot motor control. 相似文献
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《自动化学报》1999,25(5):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. 相似文献
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灵巧手操作中的接触状态和接触点检测对应操作尤为重要,传统研究中,多采用分布式触觉压力传感器直接测量接触点和接触力,但其精度受触觉传感器单元分布密度影响较大,而将操作中接触点简化为固定接触点则会引入较大误差.本文分析了固定接触点模型的不足,分析了接触面轮廓曲线,以刚性接触为接触模型,从几何角度提出了一种不同位姿下灵巧手与被操作对象的变接触点的求解算法,实现不借用触觉传感器确定接触点,并通过MATLAB求解出一特定操作中接触点的变化规律. 相似文献
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Enhanced continuous valued Q-learning for real autonomous robots 总被引:1,自引:0,他引:1
《Advanced Robotics》2013,27(5):439-441
A parallel-jaw gripper is a very useful tool for robot manipulation tasks due to its simple mechanism and control. This fact limits the range of successful grasps it can undergo, and also makes it unfeasible under uncertainties. Thus, it is desirable to improve its dexterity and manipulability. In this paper, we propose a new design of a two-fingered parallel gripper that utilizes rolling at the contacts for object repositioning and reorientation, aimed at effective firm grasps. We name it the scrollic gripper, an acronym for synchronously closing with rolling constraints. At first, the background to utilize the rolling constraints is described. Then, grasping and manipulation of the gripper are discussed. In grasp acquisition, we propose a quality function for evaluating grasp stability. The sophisticated hardware and functioning for the scrollic gripper consist, basically, on implementation of an additional degree-of-freedom to the conventional parallel-jaw gripper, leading to grasp acquisition and secure grasping. 相似文献
19.
Jean Philippe Saut Anis Sahbani Véronique Perdereau 《International Journal of Industrial Ergonomics》2007,(1)
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. 相似文献
20.
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. 相似文献