Fusion of information from multiple sensors is required for planning and control of robotic systems in complex environments. The minimal representation approach is based on an information measure as a universal yardstick for fusion and provides a framework for integrating information from a variety of sources. In this paper, we describe the principles of minimal representation multisensor fusion and evaluate a differential evolution approach to the search for solutions. Experiments in robot manipulation using both tactile and visual sensing demonstrate that this algorithm is effective in finding useful and practical solutions to this problem for real systems. Comparison of this differential evolution algorithm with more traditional genetic algorithms shows distinct advantages in both accuracy and efficiency.

Multisensor fusion has emerged as a central problem in the development of robotic systems where interaction with the environment is critical to the achievement of a given task. The Anthrobot five-fingered hand grasps an object, and senses the contact points with the surface of the object using tactile sensors. The tactile sensors extract touch position and approximate surface normal in the kinematic reference frame of the hand. In addition, a CCD camera views the position of the same object and extracts vertex/edge features of the object image. Both the tactile features and the visual features are related to the position and orientation of the object, and in practice we wish to combine these two sources of information to improve robot's ability to accurately manipulate the object. The fusion of the tactile and image feature data is used to derive an improved estimate of the object pose which guides the manipulation.

This paper presents an application of feature-based visual servoing to achieve accurate and robust 3D filament alignment. Two orthogonal cameras are used to localize the five degrees of freedom of an axi-symmetric filament. An algorithm for precisely estimating the center and orientation features of a filament in a camera view is described. The use of feature-based servoing overcomes the difficulty of 3D camera calibration in a factory environment. These features drive a PID control loop for each view. The two views are coupled in one degree of freedom, and the multiple-view controller switches between two single-view controllers to achieve 3D servoing. The overall accuracy achieved is in thousandths of an inch. Experimental results on the performance of the control algorithm are discussed. Models explaining the system behavior are presented. The use of visual servoing in this application far exceeds the positioning accuracy and the repeatability of human operators.

Traditional data interpretation methods do not prescribe a well-defined methodology for sensors which are difficult to characterize by well-defined statistical uncertainty models. We describe a minimal representation approach that characterizes the "information" in observed data by its coding complexity; this "information" is well-defined for sensors with known and unknown statistics. For statistical data, this information-based approach subsumes classical approaches, while for sensors with unknown statistics it provides a new paradigm for uncertainty modeling based on accuracy and precision (AP), and allows fusion with statistical data. An abstract multisensor data interpretation problem is described and formulated using the minimal representation approach. Monte-Carlo simulations comparing the use of an AP-coding uncertainty model with a Gaussian uncertainty model for a two-dimensional pose estimation problem are presented.

A general approach to model-based multisensor data fusion using a minimal representation size criterion is described. Each sensor is modeled by a general constraint equation which defines a data constraint manifold (DCM), and observed sensor data populate the measurement space according to these constraints. The choice of multisensor interpretations is based on a minimal representation size criterion which evaluates the complexity through correspondence and encoded errors weighted by relative sensor accuracy and precision. This general framework automatically selects subsets of data features called constraining data feature sets (CDFS) and chooses the CDFS corresponding to a minimal representation interpretation of the observed data. The resulting procedure fuses heterogeneous sensor readings into a single estimation method. The method is illustrated for a visual and tactile data fusion example. The approach generalizes to problems with non-geometric models, and can be used for multisensor system identification in other domains such as process control.>

Most high-level synthesis systems act as "black boxes", with the user allowed minimal interaction during the synthesis process.To allow the user to take a more active role, using his or her creativity where appropriate, advances must be made in three key areas.Better metrics must be developed, to allow the user to quickly evaluate the quality of the design at various points in the synthesis process, and to identify developing problem areas in the design.Better interaction techniques must be devised to allow the user to observe, control, and interact with the synthesis tools.Finally, as a foundation for these metrics and synthesis tools, a well-designed graphical user interface must be developed, this interface must be able to display both small and large designs, and must support mechanisms to control the display complexity.These are three important issues to address in making high-level synthesis a practical design tool.