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The navigation system of the JPL robot

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TLDR
The control structure of the JPL research robot and the operations of the navigation subsystem are discussed and the results of scene analysis are used to create a segmented terrain model in which surface regions are classified by traversibility.
Abstract
The control structure of the JPL research robot and the operations of the navigation subsystem are discussed The robot functions as a network of interacting concurrent processes distributed among several computers and coordinated by a central executive The results of scene analysis are used to create a segmented terrain model in which surface regions are classified by traversibility The model is used by a path-planning algorithm, PATH, which uses tree search methods to find the optimal path to a goal In PATH, the search space is defined dynamically as a consequence of node testing Maze-solving and the use of an associative data base for context-dependent node generation are also discussed Execution of a planned path is accomplished by a feedback guidance process with automatic error recovery

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Spatial Planning: A Configuration Space Approach

TL;DR: In this article, the authors propose an approach based on characterizing the position and orientation of an object as a single point in a configuration space, in which each coordinate represents a degree of freedom in the position or orientation of the object.
Book

Spatial planning: a configuration space approach

TL;DR: Algorithms for computing constraints on the position of an object due to the presence of ther objects, which arises in applications that require choosing how to arrange or how to move objects without collisions are presented.
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Automatic Planning of Manipulator Transfer Movements

TL;DR: The class of problems that involve finding where to place or how to move a solid object in the presence of obstacles is discussed and a method of computing an explicit representation of the manipulator configurations that would bring about a collision is discussed.
Journal ArticleDOI

Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape

TL;DR: A worst-case lower bound on the length of paths generated by any algorithm operating within the framework of the accepted model is developed; the bound is expressed in terms of the perimeters of the obstacles met by the automaton in the scene.
Journal ArticleDOI

Multiresolution path planning for mobile robots

TL;DR: Hierarchical path-searching methods are introduced, which make use of this multiresolution representation, to speed up the path planning process considerably.
References
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Journal ArticleDOI

A Formal Basis for the Heuristic Determination of Minimum Cost Paths

TL;DR: How heuristic information from the problem domain can be incorporated into a formal mathematical theory of graph searching is described and an optimality property of a class of search strategies is demonstrated.
Journal ArticleDOI

Computer Processing of Line-Drawing Images

TL;DR: Various forms of line drawing representation are described, different schemes of quantization are compared, and the manner in which a line drawing can be extracted from a tracing or a photographic image is reviewed.

A system for extracting 3-dimensional measurements from a stereo pair of TV cameras

TL;DR: A system that models and calibrates the cameras and pairs the two images of a real-world point in two pictures, either manually or automatically, was implemented at JPL and provides three-dimensional measurement resolution of ± 5 mm at distances of about 2 m.
Proceedings Article

System support for the Stanford hand-eye system

TL;DR: The Stanford hand-eye system is implemented as several separate tasks, each executing under a timesharing executive, and the SAIL language provides facilities for "associative processing," and is extended to serve the data sharing and communication needs of the hand- eyes.

Research and applications: Artificial intelligence

TL;DR: A scenario that demonstrates the approximate capabilities of the current version of the entire robot system is presented and the computer implementation of low-level and intermediate-level actions; routines for automated vision; and the planning, generalization, and execution mechanisms are reported.
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