A varied weights (VW) method is proposed in this paper for the kinematic control of redundant manipulators with multiple constraints. A weight factor rule to reflect the transition of the state of constraint subtask between activeness and inactiveness is presented. Each constraint has a VW factor in such a way that every time only the main task and the active constraint subtasks are considered. A new concept of effective singular value is presented for the design of damping factors to avoid the pseudosingularity arising from the transition of weight factors. The experiments on the seven-degree-of-freedom (7-DOF) Ping-Pong manipulator illustrate the efficacy of the proposed VW method.

This paper proposed a task function method for the point-to-point (PTP) control problem of non-redundant robot manipulators. A task function to compress the conventional task space to a new one with lower dimensions increases the redundancy degree so that the original manipulator becomes a redundant one, such that we can apply redundant control laws to achieve online subtasks, such as obstacle avoidance without trajectory replanning. The four basic properties of the task function method which are necessary to ensure the good performance of the PTP control problem are presented. The experiment results verify the effectiveness of the presented method.

We describe a new tracking and predicting scheme applied to a lab-made ping pong robot. The robot has a monocular vision system comprised of a camera and a light. We propose an optimized strategy to calibrate the light center using the least square method. An ellipse fitting method is used to precisely locate the center of ball and shadow on the captured image. After the triangulation of the ball position in the world coordinates, a tracking algorithm based on a Kalman filter outputs an accurate estimation of the flight states including the ball position and velocity. Furthermore, a neural network model is constructed and trained to predict the following flight path. Experimental results show that this scheme can achieve a good predicting precision and success rate of striking an incoming ball. The robot can achieve a success rate of about 80% to return a flight ball of 5 m/s to the opposite court.