Vision technology/algorithms for space robotics applications

The thrust of automation and robotics for space applications has been proposed for increased productivity, improved reliability, increased flexibility, higher safety, and for the performance of automating time-consuming tasks, increasing productivity/performance of crew-accomplished tasks, and performing tasks beyond the capability of the crew. This paper provides a review of efforts currently in progress in the area of robotic vision. Both systems and algorithms are discussed. The evolution of future vision/sensing is projected to include the fusion of multisensors ranging from microwave to optical with multimode capability to include position, attitude, recognition, and motion parameters. The key feature of the overall system design will be small size and weight, fast signal processing, robust algorithms, and accurate parameter determination. These aspects of vision/sensing are also discussed

Tele-autonomous control involving contacts: The applications of a high precision laser line range sensor

The object localization algorithm based on line-segment matching is presented. The method is very simple and computationally fast. In most cases, closed-form formulas are used to derive the solution. The method is also quite flexible, because only few surfaces (one or two) need to be accessed (sensed) to gather necessary range data. For example, if the line-segments are extracted from boundaries of a planar surface, only parameters of one surface and two of its boundaries need to be extracted, as compared with traditional point-surface matching or line-surface matching algorithms which need to access at least three surfaces in order to locate a planar object. Therefore, this method is especially suitable for applications when an object is surrounded by many other work pieces and most of the object is very difficult, is not impossible, to be measured; or when not all parts of the object can be reached. The theoretical ground on how to use line range sensor to located an object was laid. Much work has to be done in order to be really useful

The Design and Implementation of a Bayesian CAD Modeler for Robotic Applications

We present a Bayesian CAD modeler for robotic applications. We address the problem of taking into account the propagation of geometric uncertainties when solving inverse geometric problems. The proposed method may be seen as a generalization of constraint-based approaches in which we explicitly model geometric uncertainties. Using our methodology, a geometric constraint is expressed as a probability distribution on the system parameters and the sensor measurements, instead of a simple equality or inequality. To solve geometric problems in this framework, we propose an original resolution method able to adapt to problem complexity. Using two examples, we show how to apply our approach by providing simulation results using our modeler

Implementation of a robotic flexible assembly system

As part of the Intelligent Task Automation program, a team developed enabling technologies for programmable, sensory controlled manipulation in unstructured environments. These technologies include 2-D/3-D vision sensing and understanding, force sensing and high speed force control, 2.5-D vision alignment and control, and multiple processor architectures. The subsequent design of a flexible, programmable, sensor controlled robotic assembly system for small electromechanical devices is described using these technologies and ongoing implementation and integration efforts. Using vision, the system picks parts dumped randomly in a tray. Using vision and force control, it performs high speed part mating, in-process monitoring/verification of expected results and autonomous recovery from some errors. It is programmed off line with semiautomatic action planning

The MVP sensor planning system for robotic vision tasks

The MVP (machine vision planner) model-based sensor planning system for robotic vision is presented. MVP automatically synthesizes desirable camera views of a scene based on geometric models of the environment, optical models of the vision sensors, and models of the task to be achieved. The generic task of feature detectability has been chosen since it is applicable to many robot-controlled vision systems. For such a task, features of interest in the environment are required to simultaneously be visible, inside the field of view, in focus, and magnified as required. In this paper, we present a technique that poses the vision sensor planning problem in an optimization setting and determines viewpoints that satisfy all previous requirements simultaneously and with a margin. In addition, we present experimental results of this technique when applied to a robotic vision system that consists of a camera mounted on a robot manipulator in a hand-eye configuration

Methods and strategies of object localization

An important property of an intelligent robot is to be able to determine the location of an object in 3-D space. A general object localization system structure is proposed, some important issues on localization discussed, and an overview given for current available object localization algorithms and systems. The algorithms reviewed are characterized by their feature extracting and matching strategies; the range finding methods; the types of locatable objects; and the mathematical formulating methods