In field environments it is not usually possible to provide robotic systems with valid/complete geometric models of the task and environment. The robot or robot teams will need to create these models by performing appropriate sensor actions. Additionally, the robot(s) will need to position their sensors in a task directed optimum way. Here, an algorithm based on iterative sensor planning and sensor redundancy is proposed to enable them to efficiently position their cameras with respect to the task/target. The method assumes stationary robotic vehicles with cameras carried by articulated mounts. The algorithm uses the measured scene information to find new camera mount poses. Simulations and experiments show the effectiveness of this algorithm.

To meet the objectives of many future missions, robots will need to be adaptable and reconfigurable. A concept for such a robotic system has been proposed previously based on using a large number of simple binary actuators. Previous researchers have addressed some of the issues brought up by robots with a few binary actuators. This paper examines the computational feasibility of controlling and planning such binary robotic systems with a large number of actuators, including computation of their workspace, forward kinematics, inverse kinematics and trajectory following. Methods are proposed and evaluated by simulation. A detailed error analysis and computational requirements are presented. An example of the planning for a binary walking robot is presented.

In field environments it is not usually possible to provide robotic systems with valid geometric models of the task and environment. The robot or robot teams will need to create these models by performing appropriate sensor actions. Here, an algorithm based on iterative sensor planning and sensor redundancy is proposed to enable them to efficiently build 3D models of the environment and task. The method assumes stationary robotic vehicles with cameras carried by articulated mounts. The algorithm uses the measured scene information to find new camera mount poses based on information content. Issues addressed include model-based multiple sensor data fusion, and uncertainty and vehicle suspension motion compensation. Simulations show the effectiveness of this algorithm.

An optical modeless image identification algorithm is presented. The system uses the HAusdorff-Voronoi NETwork (HAVNET), an artificial neural network designed for two-dimensional binary pattern recognition. A detailed review of the architecture, the learning equations, and the recognition equations for the HAVNET network are presented. Competitive learning has been implemented in training the network using a nearest-neighbor technique. The image identification system presented in this paper is applied to two tasks: the optical recognition of a set of American sign language signals and identification of grayscale fingerprints. Image preprocessing includes edge enhancement by histogram equalization, application of a Laplacian filter and thresholding. A segmented Hankel and Fourier transformation in polar coordinates is applied to the binary image giving a rotationally and translationally invariant image structure. This preprocessed image employs the HAVNET neural network for successful image identification.

A robotic system has been developed to automate the crucible packing process in the CZ semiconductor wafer production. It requires the delicate manipulation and packing of highly irregular shaped polycrystalline silicon nuggets, into a fragile glass crucible. An online algorithm is presented to plan the packing. It uses a method called virtual trial and error. The online algorithm handles large numbers of highly irregularly shaped object of different sizes without requiring the object models. Working with the 3D range maps of objects, it is computationally fast enough to be applied in real-time to practical industrial applications, such as the CZ wafer manufacture. Simulation results show that it compares well with the human performance. The integrated system is shown to achieve high production rates, required precision and cost effectiveness.

A robotic system is developed to automate the packing of polycrystalline silicon nuggets into a fragile fused silica crucible in CZ semiconductor wafer production. The highly irregular shapes of the nuggets make this a difficult and challenging task. In this system, nugget grasping is done with a three-cup suction gripper and nugget manipulation is attained with a 7-DOF SCARA manipulator. An optical 3D vision system, based on active laser triangulation, measures the nugget and crucible profiles. A model-free virtual trial and error packing algorithm determines optimal nugget placement in real time. A hybrid position-force control scheme has been implemented and tested for physical nugget placement. The integrated system achieves high production rates, required precision and cost effectiveness.

A robotic system is being developed to automate the crucible packing process in CZ semiconductor wafer production. It requires the delicate manipulation and packing of highly irregular polycrystalline silicon nuggets, into a fragile fused silica crucible. For this application, a dual optical 3D surface geometry measuring system that uses active laser triangulation has been developed and successfully tested. One part of the system measures the geometry profile of a nugget being packed and the other the profile of the nuggets already in the crucible. A resolution of 1 mm with 15 KHz sampling frequency is achieved. Data from the system is used by a packing algorithm to determine optimal nugget placement. The system is shown to achieve high production rates, required precision and cost effectiveness.