This research work focuses on development of a framework for designing a telerobotic system controller. We define telefunctioning as a robotic manipulation method in which the dynamic behaviors of the slave robot and the master robot are functions of each other; these functions are the designer's choice and depend on the application. In a subclass of telefunctioning called telepresence, all of the relationships between the master and the slave are specified as "unity" so that all of the master and slave variables (e.g., position, velocity) are dynamically equal. To create telefunctioning, we arrive at a minimum number of functions relating the robots' variables. We then develop a control architecture which guarantees that the defined functions govern the dynamic behavior of the system. The stability of the closed-loop s) stem (master robot, slave robot, human, and the load being manipulated) is analyzed and sufficient conditions for stability are derived.
Imitating the learning process of a human playing ping-pong is extremely complex. This work proposes a suitable learning strategy. First, an inverse kinematics solution is presented to obtain the smooth joint angles of a redundant anthropomorphic robot arm in order to imitate the paddle motion of a human ping-pong player. As humans instinctively determine which posture is suitable for striking a ball, this work proposes two novel processes: (i) estimating ball states and predicting trajectory using a fuzzy adaptive resonance theory network, and (ii) self-learning the behavior for each strike using a self-organizing map-based reinforcement learning network that imitates human learning behavior. Experimental results demonstrate that the proposed algorithms work effectively when applied to an actual humanoid robot playing ping-pong.
Mobile robots frequently replace humans in handling and transporting wafer carriers in semiconductor production lines. A mobile robot is constructed in this paper. The developed mobile robot is primarily composed of a mobile base, a robot manipulator, and a vision system. Since the guidance control system of the mobile base inevitably causes positioning e n o n of the mobile base, this study employs the eye-in-hand vision system to provide visual information for controlling the manipulator of the mobile robot to grasp accurately stationaty material during pick-and-place operations between a predefined Station and the mobile robot. This work further proposes a position-based look-and-move task encoding control strategy for eye-in-hand vision architecture, that maintains all target features in the camera's field of view throughout the visual guiding. Moreover, the manipulator can quickly approach the material and precisely position the end-effector in the desired pose. Numerous techniques are required for implementing such a task, including image enhancement, edge detection, comer and centroid detection, camera model calibration method, robotic handleye calibration method, using a camera with controlled zoom and focus, and task encoding scheme. Finally, the theoretical results for the proposed control strategy are experimentally verified on the constructed mobile robot. Specific experimental demonstrations include grasping the target object with different locations on the station and grasping the target object tilted by different angles to the station
Abstr8ctThe work prossnted hem i s a nonlinear approach for the stability analysis of robot manipulators in constrained maneuvers. Stability 4 the environment and the manipulator taken as a whole has been investigated, and a bound for staMc manipulation has bcm derived. We &ow that for stability a# the robot, there must be some initial compliancy either in the robot or in the environment. The general stability condition hos &en extended to the particular mse where thc environment is wry rigid in comparison with the robot stiffness. The stability analysis has been investigated using unstructured models for the dynamic behavior of the robot manipulator and the environment. This unified approach of modeling robot dynamics is expressed in terms of sensitivity functions as opposed to the Lagrangian approoeh. It allows us to incorporaie the dynamic behavior of dl tha elements of a robor manipulator (i.e. actuators, 8cnwra and the structural compliance of the links) in addition to the rigid body dynamics. Nomenclature m b r l of the extcrnal force on the robot endpoint inplt trajectory vector environment dynamics vector of the contact foree, [ff, f2, ...., fniT the limiting value of the contact force and robot porition for rigid environment robot dynamics with positioning controller compensator operating on the contact force, f identity metrix environment impedance in the linear domain input-command vector degrees of the freedom of the robot n < 6 robot manipulator sensitivity (l/stifhess) positive scalar the forward loop mapping from e to f in Figure 5. vector of the environment deflection Mctor of the robot endpoint position vector of the environment position before contact vector of the joint angles of the robot positive scalars frequency range of operation (bandwidth) positive scalars IntroductionMost assembly operations and manufacturing tasks require mechanical interactions with the environment or with the object being manipulated, along with "fast" motion in free and unconstrained space. In constrained maneuvers, the interaction force2 must be accommodated rather than resisted.
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