O ce delivery robots have to perform many tasks. They have to determine the order in which to visit ofces, plan paths to those o ces, follow paths reliably, and avoid static and dynamic obstacles in the process. Reliability and e ciency are key issues in the design of such autonomous robot systems. They must deal reliably with noisy sensors and actuators and with incomplete knowledge of the environment. They must also act e ciently, in real time, to deal with dynamic situations. Our architecture is composed of four abstraction layers: obstacle avoidance, navigation, path planning, and task scheduling. The layers are independent, communicating processes that are always active, processing sensory data and status information to update their decisions and actions. A version of our robot architecture has been in nearly daily use in our building since December 1995. As of July 1996, the robot has traveled more than 75 kilometers in service of over 1800 navigation requests that were speci ed using our World Wide Web interface.

We continue our study of the inverse scattering problem for diffuse light. In contrast to our earlier work, in which we considered the linear inverse problem, we now consider the nonlinear problem. We obtain a solution to this problem in the form of a functional series expansion. The first term in this expansion is the pseudoinverse of the linearized forward-scattering operator and leads to the linear inversion formulas that we have reported previously. The higher-order terms represent nonlinear corrections to this result. We illustrate our results with computer simulations in model systems.

The results of this simulation study imply that penalized AM has the potential to reconstruct images with similar noise and resolution using a fraction (10%-70%) of the FBP dose. However, this dose-reduction potential depends strongly on the AM penalty parameter and the contrast magnitude of the structures of interest. In addition, the authors' results imply that the advantage of AM can be maximized by optimizing the nonquadratic penalty function to the specific imaging task of interest. Future work will extend the methods used here to quantify noise and resolution in images reconstructed from real CT data.

Radar targets often have both specular and diffuse scatterers. A conditionally Rician model for the amplitudes of pixels in Synthetic Aperture Radar (SAR) images quantitatively accounts for both types of scatterers. Conditionally Rician models generalize conditionally Gaussian models by including means with uniformly distributed phases in the complex imagery. Qualitatively, the values of the two parameters in the Rician model bring out different aspects of the images. For automatic target recognition (ATR), log-likelihoods are computed using parameters estimated from training data. Using MSTAR data, the resulting performance for a number of four class ATR problems representing both standard and extended operating conditions is studied and compared to the performance of corresponding conditionally Gaussian models. Performance is measured quantitatively using the Hubert-Schmidt squared error for orientation estimation and the probability of error for recognition. For the MSTAR dataset used, the results indicate that algorithms based on conditionally Rician and conditionally Gaussian models yield similar results when a rich set of training data is available, but the performance under the Rician model suffers with smaller training sets. Due to the smaller number of distribution parameters, the conditionally Gaussian approach is able to yield a better performance for any fixed complexity.

PET provides an in vivo molecular and functional imaging capability that could be valuable for studying the interaction of plants in changing environments at the whole-plant level. We have developed a dedicated plant PET imager housed in a plant growth chamber (PGC), which provides a fully controlled environment. The system currently contains two types of scintillation detector modules from commercial small animal PET scanners: 84 microPET® detectors, which are made with scintillation crystal arrays of 2.2 mm(3) × 2.2 mm(3) × 10 mm(3) crystals to provide a large detection area; and 32 Inveon™ detectors, which are made with scintillation crystal arrays of 1.5 mm(3) × 1.5 mm(3) × 10 mm(3) crystals to provide higher spatial resolution. The detector modules are configured to form two half-rings, which provide a 15 cm-diameter trans-axial field of view (FOV) for dynamic tomographic imaging of small plants. Alternatively, the Inveon detectors can be reconfigured to form quarter-rings, which provide a 25 cm FOV using step-and-shoot motion. The imager contains two linear stages that move detectors vertically at different heights for multisection scanning, and two rotation stages to collect coincidence events from all angles when using the step-and-shoot acquisition. The detector modules and mechanical components of the imager are housed inside a PGC that regulates the environmental parameters. The system has a typical energy resolution of 15% for the Inveon detectors and 24% for the microPET detectors, timing resolution of 1.8 ns, and sensitivity of 1.3%, 1.4% and 3.0% measured at the center of the FOV, 5 cm off to the larger half-ring and 5 cm off to the smaller half-ring, respectively (with a 350-650 keV energy window and 3.1 ns timing window). The system's spatial resolution is capable of resolving rod sources of 1.25 mm diameter spaced 2.5 mm apart (center to center) using the ML-EM reconstruction algorithm. Preliminary imaging experiments using soybean and wild type and mutant maize labeled with (11)CO2 produced high-quality dynamic PET images that reveal the translocation and distribution patterns of photoassimilates. This system can be used to provide an in vivo molecular and functional imaging capability for plant research.