Paper
10 March 1989 A Methodology Of Map-Guided Autonomous Navigation With Range Sensor In Dynamic Environment
Alex C.C. Meng, Marty Wand, Vincent S.S. Hwang
Author Affiliations +
Proceedings Volume 1007, Mobile Robots III; (1989) https://doi.org/10.1117/12.949111
Event: 1988 Cambridge Symposium on Advances in Intelligent Robotics Systems, 1988, Boston, MA, United States
Abstract
This paper describes the work for a map-guided robot (AGV) maneuvering in dynamic factory floor environment. The robot is equipped with a narrow-beam sonar to detect obstacles. In factory applications, the location and orientation of the robot will be determined by external sensors. A path from start to target location will be digitized into a sequence of waypoints and the robot navigates locally by dead-reckoning between two adjacent waypoints. Autonomous navigation, in this setup, is viewed in terms of three components: automatic path plannihg for the given floor layout, the start and target locations of the robot, automatic path replanning after detecting obstacles, and local navigation which should always lead the robot out of a trap (cul-de-sac) if one occurs. For any given floor layout, we model the passage ways between obstacles (processing machines) as a connected graph knowns as the Voronoi graph. From the robot's point of view, the graph is the map to plan paths and navigate. Path planning is based on the retraction method. After detecting an obstacle, path replanning will be invoked. Algorithms for path planning, path replanning, and local navigation are given.
© (1989) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Alex C.C. Meng, Marty Wand, and Vincent S.S. Hwang "A Methodology Of Map-Guided Autonomous Navigation With Range Sensor In Dynamic Environment", Proc. SPIE 1007, Mobile Robots III, (10 March 1989); https://doi.org/10.1117/12.949111
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CITATIONS
Cited by 3 scholarly publications.
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KEYWORDS
Sensors

Free space

Computer simulations

Mobile robots

Detection and tracking algorithms

Environmental sensing

Target detection

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