Xin-Zhi Zheng

Display of Feel for the Manipulation of Dynamic Virtual Objects

A system for displaying feel information, while manipulating virtual objects, which takes their dynamic behavior into account is introduced. The display is realized using multiple link mechanisms. New concept of impedance display and two ways of its realization, “measuring force and displaying motion” and “measuring motion and displaying force,” are proposed. Calculation algorithms for the driving input to the display device for the operation of both single-body and multi-body virtual objects are established. The validity of the proposed method is verified experimentally using a newly developed two-finger display device.

On dynamic control of finger sliding and object motion in manipulation with multifingered hands

Dynamic control of a three-fingered robot hand manipulating an object in 3D space, while allowing one of the three fingers to slide in order to change its grasp location on the object surface, is formulated. The static and dynamic friction between the sliding finger and the object surface are explicitly considered and their effects regarding the behavior of the finger and object are discussed. Motion equations of the whole system are derived and a dynamic control law for realizing the desired object motion, as well as the desired finger sliding and the desired grasping force, is proposed. The realizability of the given trajectories under the constraints of maximal static friction and dynamic friction at the grasp points, and of joint driving torque, are also discussed. A simulation example to demonstrate the use of the proposed control law is given. The results of this work will have useful application to a multifingered robot hand in performing certain tasks involving the re-grasping and re-orientation of an object without interrupting the grasp.

Prosthetic hand control based on torque estimation from EMG signals

In this paper, we propose a direct torque control method for the prosthetic hand. In order to estimate the joint torque from EMG signals, an artificial neural network by the feedback error learning schema is used. 2-DOF motions, i.e. hand grasping/opening and arm flexion/extension, are picked up. In the experiments, two measurement conditions of EMG signal are prepared: the forearm from which the EMG signal is measured is free or fixed. Then it is verified that the neural network can learn the relation between the EMG signal and the joint torque under these two measurement conditions.

A learning and dynamic pattern generating architecture for skilful robotic baseball batting system

A learning and dynamic pattern generating system for acquiring the skills in dynamic manipulation of objects using robotic manipulators is to be established, where the desired space trajectories for the manipulators are not specified explicitly. Robotic batting is taken as a task example. The problem is approached so as to result in an iterative learning of the joint driving torque patterns of the manipulator that are considered as the task skills and learned against several typically given desired ball velocities. A multi-layered artificial neural network is used to learn and generalize the joint driving torque against various desired ball velocities, and an iterative optimal control algorithm is adopted to generate the supervisory joint driving torque signals for the neural network. Computer simulation examples of a three-degree-of-freedom manipulator are outlined, the results are depicted to explain the idea and verify the proposed approach, and the robustness issues are discussed qualitatively.

Experimental study of decentralized PVFC for cooperative mobile robots

Traditionally, tasks for a robotic system are specified by a desired timed trajectory. There, however, are many tasks in which the desired motions are specified by the state of system rather than time, such as contour following tasks. For such tasks, passive velocity field control (PVFC) has been proposed and the geometry of the controlled systems was analyzed. In this paper, a method to apply a decentralized PVFC to a cooperative multiple mobile robotic system whose sub-system is under nonholonomic constraints, which conveys a common rigid object in a horizontal plain, is proposed, and the effectiveness of the control method is demonstrated by experiments.

Task skill formation via motion repetition in robotic dynamic manipulation

A system structure for acquiring the task skills in dynamic manipulation of objects using robotic manipulators is established, where the desired space trajectories do not need to be specified explicitly. A robotic batting is taken as a task example. The joint driving torque patterns of the manipulator are considered as the task skills and are learned against several typically given desired ball velocities. A multi-layered artificial neural network is used to learn and generalize the joint driving torque against various desired ball velocities, and an iterative optimal control algorithm is adapted to generate the supervisory joint driving torque signals for the neural network. Computer simulation and a three-degree-of-freedom manipulator is outlined and the results are depicted to explain the idea and verify the proposed approach.

Human control characteristics in bilateral micro-teleoperation system

The micromanipulator is an indispensable device used to assist high precision tasks required in microsurgery and microassembly. However, in order to manipulate micro objects it is inevitable to consider the scaling effect problem between worlds with different physical characteristics. Some scaling methods have been proposed, but how they influence human operator performance is still a remaining problem. In this paper, the performance of the human operator is analyzed based on his/her dynamic characteristics.

Task-oriented impedance adjustments of human arm movements

This paper discusses task-oriented control strategies and their dynamic formation in movements of human arm, with the impedance adjustment as the fundamental control method in its redundant degree-of-freedom (DOF) muscular-skeletal structure. Impedance adjustment mechanisms and dynamic characteristics for the muscular-skeletal and spinal reflection systems are described. It is also shown that the impedance adjustment at the joint and muscular levels plays an important role in the manipulation of objects. Dynamic impedance properties (stiffness and viscosity) of the actuating structure, together with the equilibrium trajectory profile of the structure, is defined to be the expression of the motion skill. Then, a task-oriented iterative impedance adjusting algorithm based on Variant Calculus is proposed, and simulation examples on a three-DOF planar arm are presented to show that the task-oriented motion skills can be formed under naturally specified simple objective functions.

Self-organization of a task-oriented visuo-motor map for a redundant arm

In this paper, we propose a new method that solves the inverse kinematics problem by self-organizing a task-oriented visuo-motor map for a redundant manipulator. The method makes good use of the redundancy of the manipulator with excess degrees of freedom, although conventional methods using a self-organization algorithm of a visuo-motor map do not utilize the redundancy, and eliminates the redundancy by maximizing a dexterity measure for task execution. In this paper, two types of the map can be teamed up by applying an extended manipulability measure and a force-controllability measure as dexterity measure. The efficiency of the proposed method is ascertained by computer simulations for learning the task-oriented visuo-motor maps of a three-link manipulator and a four-link manipulator in horizontal plane. The values of dexterity measures became higher than those of conventional methods that did not consider the characteristics of tasks and did not make use of the redundant degrees of freedom of the manipulator.