The goal of this project is to segment an image, or an image sequence, into its constituent regions such that each region is characterized by homogeneity of a three-dimensional property. Currently, we are developing segmentation algorithms that use uniformity of three-dimensional surface texture and three-dimensional object motion as homogeneity criteria.

This project is aimed at the design of a six-legged robot that is able to traverse irregular terrain mimicking the locomotion capabilities of insects. The completed robot will have a structure similar to that of an insect. In the model, each leg has three degrees of freedom, and the orientations and separations of joints are made to parallel the anatomy of the insect's legs. The robot leg movements are powered pneumatically (using compressed air) in an attempt to achieve the strength and compliance of muscle. Various robot design parameters are being obtained through experimental studies of insect locomotion. The planned controller of the robot is based on central pattern generators thought to coordinate the leg movements in insects.

The objectives of this research are analysis, development, real-time implementation, and real-world application of a new image transform. The transform is aimed at multiscale, low-level image segmentation, i.e., extraction and representation of image structure at all geometric and photometric scales present in an image. Specifically, the transform detects contours and skeletons of image regions, and identifies the cross-scale relationships among these. The scales present are a priori unknown and must be identified automatically. Application of the transform to a range of problems is investigated.

This project is aimed at the interpretation of moving, nonrigid surfaces carrying little or limited detail, with applications to virtual space teleconferencing. The objectives include delineation of moving parts of a scene, active selection of viewpoints and data acquisition, and integration of focus, shading, and silhouette information for functionality under a range of environmental conditions.

This project concerns (1) analysis of images of dynamic scenes, (2) analysis-guided synthesis, and (3) perceptual evaluation of synthesized image sequences with emphasis on computational speed, each aimed at the 3-D motion and structure characteristics relevant to navigation. The first part is concerned with integrated analysis and estimation of 3-D motion and structure parameters from multiple image cues or attributes, including those obtained during active acquisition of image sequences and those extracted from the acquired image sequence. Image synthesis is based on the new notion that the cues that contributed the most to 3-D interpretation also would contribute the most to perceptually realistic synthesis, thus suggesting an approach to analysis-guided synthesis, compression, and visualization. The perceptual evaluation tests the efficacy of analysis-guided synthesis.

This research is aimed at high-performance image representation, manipulation, and analysis. The use of image representation is investigated for three-dimensional scene estimation and communication of multidimensional and multivariate images (e.g., magnetic resonance images and color images). New representations are developed for image texture and perceptual groupings. Finally, multiscale representations are used to develop a toolset for browsing of image databases, image editing, and composition.

The goal of this research project is to design, construct, and evaluate integrated active sensing and motor control systems for legged robots using insights provided by insect neurobiology. In particular, we plan to develop and test neurally inspired robotic control systems that acquire, process, and integrate sensory information from two distinct sensory modalities in order to carry out visually guided target tracking and target approach behavior in environments that may include obstacles and irregular terrain. The two sensory systems we will consider are: (1) tactile, proprioceptive, and stress signals from leg sense organs and (2) visual signals from the eyes, along with relevant proprioceptive signals related to head and body position.

The objective of this research is to improve the quality, reproducibility, and speed of ceramic-based multichip module manufacturing. An interdisciplinary effort will focus on materials and processing issues and on automated visual inspection of tape-cast ceramic layers. This will be addressed by an experimental program that (1) characterizes the rheological properties as a function of suspension composition and time and (2) determines the microstructural variations within the layers. Insights gained from this program will be used to derive statistical models for defect occurrence, which will then drive the inspection process. The result will be improved dimensional control, reproducibility, and automated visual inspection of tape-cast sheets.

This project involves joint work with researchers at CICESE in Ensenada, Mexico. The goal of this collaboration is to expand our own research in the area of visual servo control of robotic manipulators so that the previously neglected aspect of robot dynamics will be taken into consideration. We currently have an active research program in visual servo control at the University of Illinois at Urbana-Champaign (UIUC). We will expand our current research efforts by capitalizing on the dynamics and control expertise of the researchers at CICESE. Our current research in intelligent control and in optimizing the performance of visual servo systems will directly benefit from these efforts.

This project concerns the nonlinear control of underactuated mechanical systems. This class of systems is quite broad and encompasses flexible structures of all kinds including flexible link robots, flexible joint robots, as well as robot models that include actuator dynamics, and many of the classical control problems like the ball-and-beam and cart-pole systems. Techniques such as partial feedback linearization, singular perturbations, and passivity methods are being applied for global and semiglobal stabilization of these systems.

This project concerns the integration of machine learning and sensor-based control in intelligent robotic systems. The research combines techniques of explanation-based control with robust and adaptive nonlinear control, computer vision, and robot motion planning. We wish to go beyond the strict hierarchical control architectures typi- cally used in robotic systems by integrating modeling, dy- namics, and control at all levels of intelligence. Our ultimate goal is to combine analytical techniques of nonlinear dynamics and control with artificial intelligence into a single new paradigm, in which symbolic reasoning holds an equal place with differential equation based modeling and control.

This project fundamental issues in the adaptive control of underactuated mechanical systems. This class of systems encompasses both holonomic and nonholonomic systems such as balancing and walking robots, space robots, flexible link robots, and flexible joint robots, as well as robot models that include actuator dynamics, and many of the classical control problems like the ball-and-beam and cart-pole systems. Techniques such as partial feedback linearization, singular perturbations, and passivity-based methods are being applied for global and semiglobal stabilization of these systems.