Many computer vision applications require synchronization between image acquisition and external trigger events. Hardware and software triggering are widely used but have several limitations. Soft synchronization is investigated, which operates by time tagging both trigger events and images in a video stream and selecting the image corresponding to each trigger event. A stochastic model is developed for soft synchronization; and, based on the model, the uncertainty interval and confidence for correct image selection are determined, and an efficient calibration method is derived. Soft synchronization is experimentally demonstrated on a Linux image processing computer with a camera connected by the IEEE-1394 serial bus. To minimize timing variability, time tags are associated with images and events in the corresponding interrupt service routines within the operating system of the image processing computer. The model-based analysis applied to the experimental hardware shows that image/event synchronization within inter-frame interval can be achieved with 99% confidence. Experiments confirm this result.

Nonlinear proportional-integral-derivative (NPID) is realized by modulating the control gains of a conventional linear controller in response to system state or error. Performance benefits such as increased damping or reduced tracking error have been experimentally demonstrated. Previous results (2000) have established Lyapunov stability of NPID control for full-state knowledge and for special cases of partial-state knowledge; but the general problem of stability with arbitrary state knowledge remained unsolved. In the present work, design methods for NPID control are extended to the case of arbitrary partial-state knowledge. Given any combination of a stabilizable linear system and NPID control law, an upper bound on control effort can be determined for which global asymptotic stability is assured. The result is based on a Lyapunov stability proof and is applied to establish the stability of NPID control for the Sarcos dexterous manipulator.

To provide COSMOS, a dynamic model based manipulator control system, with an improved dynamic model, a PUMA 560 arm was disassembled; the inertial properties of the individual links were measured; and an explicit model incorporating all of the non-zero measured parameters was derived. The explicit model of the PUMA arm has been obtained with a derivation procedure comprised of several heuristic rules for simplification. A simplified model, abbreviated from the full explicit model with a 1% significance criterion, can be evaluated with 805 calculations, one fifth the number required by the recursive Newton-Euler method. The procedure used to derive the model is laid out; the measured inertial parameters are presented, and the model is included in an appendix.

The robotics group of the Stanford Artificial Intelligence Laboratory is currently developing a new computational system for robotics applications. Stanford's NYMPH system uses multiple NSC 32016 processors and one MC68010 based processor, sharing a common Intel Multibus. The 32K processors provide the raw computational power needed for advanced robotics applications, and the 68K provides a pleasant interface with the rest of the world. Software has been developed to provide useful communications and synchronization primitives, without consuming excessive processor resources or bus bandwidth. NYMPH provides both large amounts of computing power and a good programming environment, making it an effective research tool.