This paper describes a novel memory architecture to minimize truncation error for implementing N-Dimension decomposable transformation using consecutive one dimension (1D) transformation approach. The memory architecture utilizes the effective bit representation not only achieving minimal truncation error with a constrained memory size, but also minimizing memory size to fulfill a given accuracy requirement. A 8/spl times/8 2-D IDCT macro that used the proposed architecture is implemented. The cell count for the macro is 43 K cells and occupies 6 mm/sup 2/ using 0.5 /spl mu/m CMOS VLSI technology.

A parallel-jaw type multiple-d.o.f. robot hand is designed and implemented to manipulate workpieces within the hand by rotation and translation. An efficient manipulator control algorithm and part mating application is developed. A simulation result is also presented to show the changes of force during part insertion to verify the models developed.>

The authors address the problem of robot multisensor fusion and integration with special emphasis on optimal estimation of fused sensor data. The investigation is based on a Unimation PUMA 560 robot and various external sensors. These include overhead vision, eye-in-hand vision, proximity, tactile array, position, force/torque, crossfire, overload, and slip sensing devices. The efficient fusion of data from different sources will enable the machine to respond promptly in dealing with the real world. Towards this goal, the general paradigm of a sensor data fusion system has been developed, and some simulation results as well as results from the actual implementation of certain concepts of sensor data fusion have been demonstrated.>

The objective of this paper is to develop an intelligent robot system through the integration of multiple sensors into robot tasks. The investigation is based on a Unimation PUMA 560 robot employing various external sensors. These sensors include overhead vision, eye-in-hand vision, proximity, tactile array, position, force/torque, cross-fire, overload and slip. The efficient machine representation of acquired sensory knowledge will enable robots to deal with the "real world". The general paradigm of a sensor data fusion system has been developed. This system will allows the robot to handle uncertainty in sensory data as well as to verify and recover sensor errors.