Most robotic grasping tasks assume a stationary or fixed object. In this paper, we explore the requirements for tracking and grasping a moving object. The focus of our work is to achieve a high level of interaction between a real-time vision system capable of tracking moving objects in 3-D and a robot arm equipped with a dexterous hand that can be used pick up a moving object. We are interested in exploring the interplay of hand-eye coordination for dynamic grasping tasks such as grasping of parts on a moving conveyor system, assembly of articulated parts or for grasping from a mobile robotic system. Coordination between an organism's sensing modalities and motor control system is a hallmark of intelligent behavior, and we are pursuing the goal of building an integrated sensing and actuation system that can operate in dynamic as opposed to static environments. The system we have built addresses three distinct problems in robotic hand-eye coordination for grasping moving objects: fast computation of 3-d motion parameters from vision, predictive control of moving robotic arm to track a moving oblect, and grasp planning. The system is able to operate at approximately human arm movement rates, and we present experimenatl result in which a moving model train is tracked, stably grasped, and picked up by the system. The algorithms we have developed that relate sensing to actuation are quite general and applicable to a variety of complex robotic tasks that require visual feedback for arm and hand control.

Currently two major limitations to applying vision in real tasks are robustness in realworld, uncontrolled environments, and the computational resources required for real-time operation. In particular, many current robotic visual motion detection algorithms (optical flow) are not suited for practical applications such as segmentation and structure-frommotion because they either require highly specialized hardware or up to several minutes on a scientific workstation. In addition, many such algorithms depend on the computation of first and in some cases higher numerical derivatives, which are notoriously sensitive to noise. In fact the current trend in optical flow research is to stress accuracy under ideal conditions and not to consider computational resource requirements or resistance to noise, which are essential for real-time robotics. As a result robotic vision researchers are frustrated by an inability to obtain reliable optical flow estimates in real-world conditions, and practica...

This paper describes a software system that we have developed to simplify the task of programming the DataCube MV20 image processor [2]. The system presents an abstract view of the hardware's capabilities, allowing the programmer to focus on the computation to be performed rather than the manipulations needed to map the computation onto the hardware. Because it is based on an abstract model of the hardware, the system could be supported on other architectures as well. The core of the programming system is VEIL

Currently two major limitations to applying vision in real tasks are robustness in realworld, uncontrolled environments, and the computational resources required for real-time operation. In particular, many current robotic visual motion detection algorithms (optical flow) are not suited for practical applications such as segmentation and structure-frommotion because they either require highly specialized hardware or up to several minutes on a scientific workstation. In addition, many such algorithms depend on the computation of first and in some cases higher numerical derivatives, which are notoriously sensitive to noise. In fact the current trend in optical flow research is to stress accuracy under ideal conditions and not to consider computational resource requirements or resistance to noise, which are essential for real-time robotics. As a result robotic vision researchers are frustrated by an inability to obtain reliable optical flow estimates in real-world conditions, and practica...