We consider systems whose parameters satisfy certain easily computable physical laws. Each parameter is directly measured by a number of sensors, or estimated using measurements, or both. The measurement process may introduce both systematic and random errors which may then propagate into the estimates. Furthermore, the actual parameter values are not known since every parameter is measured or estimated, which makes the existing sample-based fusion methods inapplicable. We propose a fusion method for combining the measurements and estimators based on the least violation of physical laws that relate the parameters. Under fairly general smoothness and nonsmoothness conditions on the physical laws, we show the asymptotic convergence of our method and also derive distribution-free performance bounds based on finite samples. For suitable choices of the fuser classes, we show that for each parameter the fused estimate is probabilistically at least as good as its best measurement as well as best estimate. We illustrate the effectiveness of this method for a practical problem of fusing well-log data in methane hydrate exploration.

A feedforward multilayer neural net is trained to learn the correspondence between seismic data and well logs. The introduction of a virtual input layer, connected to the nominal input layer through a special nonlinear transfer function, enables ultrafast (single iteration), near-optimal training of the net using numerical algebraic techniques. A unique computer code, named DeepNet, has been developed, that has achieved, in actual field demonstrations, results unattainable to date with industry standard tools.

An important characteristic of mobile manipulators is their particular kinematic redundancy created by the addition of the degrees of freedom of the platform and those of the manipulator. This kinematic redundancy is very desirable since it allows mobile manipulators to operate under many modes of motion and to perform a wide variety of tasks. On the other hand, it also significantly complicates the problem of planning a series of sequential tasks, in particular for the critical times at which the system needs to "switch" from one task to the other (task commutation), with changes in mode of motion, task requirement, and task constraints. This paper focuses on the problem of planning the positions and configurations in which the system needs to be at task commutation in order to assure that it can properly initiate the next task to be performed. The concept of and need for "commutation configurations" in sequences of mobile manipulator tasks is introduced, and an optimization approach is proposed for their calculation during the task sequence planning phase. A variety of optimization criteria were previously investigated to optimize the task commutation configurations of the system when task requirements involve obstacle avoidance, reach, maneuverability, and optimization of strength. In this paper, the authors show that a "minimax" approach is particularly adapted for most of these requirements. The authors develop the corresponding criteria and discuss solution algorithms to solve the "minimax" optimization problems. An implementation of the algorithms for the authors' HERMIES-III mobile manipulator is then described and sample results are presented and discussed.>

Since a vehicle with two or more steerable drive wheels is always traveling in a circle about an instantaneous center of rotation, the motion of the wheels is constrained. The wheel translational velocity divided by the radius to the center of rotation must be the same for all wheels. When the drive wheels are controlled independently using position control, the motion of the wheels may violate the constraints and the wheels may slip. Consequently, substantial errors can occur in the position and orientation of the vehicle. A vehicle with N steerable drive wheels has N holonomic constraints on the steering angles, (N-1) nonholonomic constraints on the wheel velocities, and one degree of freedom. The authors have developed a new approach to the control of a vehicle with N steerable drive wheels. The novel aspect of their approach is the introduction of variables to control the constraint forces. To control the vehicle, the authors have one variable to control motion and (N-1) variables that can control the constraint forces to reduce errors. Kankaanranta and Koivo (1988) developed a control architecture that allows the control variables for force and position to be decoupled. In the work of Kankaaranta and Koivo the control variables for force are an exogenous input. The authors have made the central variables for force endogenous by defining them in terms of the errors in satisfying the nonholonomic constraints. The authors have applied the control architecture to the HERMIES-III robot and have measured a dramatic reduction in error (more than a factor of 20) compared to motions without constraint force control.>

A new wheel control system for the HERMIES-III robot has been designed, built, and tested. HERMIES-III is a large mobile robot with omnidirectional steering that is designed for human-scale experiments. During each cycle (at 20 Hz), the wheel control system moves the robot toward a goal and calculates the current position of the robot. The system has seven modes for moving to a goal and the goal may be changed during the motion of the robot. The architecture, wheel driver and reckoner, and wheel controller are discussed in detail, and the current status of the system is given.>

HERMIES-III is a large mobile robot designed for human-scale experiments. The initial experiment with the robot (DEMO 89) was the cleanup of a simulated chemical spill. To perform the experiment, the robot was required to plan a path through an already known world, navigate along the path (avoiding unexpected obstacles), and locate and remove debris from a target area. A description is given of the software system that was developed to perform the experiment. The software system consisted of 19 processes that operated on a distributed set of heterogeneous computers.>