Reflex Navigation in Sensor space

Keeratipranon, Narongdech, Maire, Frederic D., & Huang, Henry (2007) Reflex Navigation in Sensor space. In Ruckert, Ulrich, Sitte, Joaquin, & Witkowski, Ulf (Eds.) Autonomous Minirobots for Research and Edutainment, 2 - 5 October, Buenos Aires, Argentina.


Most existing navigation systems require a ground frame (2D Cartesian coordinate system) in order to navigate from a ground point A to a ground point B. However, it is more natural to navigate by reflex actions between the points in sensor space corresponding to A and B. In this paper, we show that it is possible to navigate by reflex action in sensor space when the components of the sensor vector are bearings of landmarks, and the reflex action is a gradient descent on the distance in sensor space between the current position and the target position. Our main result is a proof that except for pathological cases, any point is reachable from any other point by reflex action in the bearing sensor space provided the environment is free of obstacles. Although the robot’s trajectory generated by this method does not necessarily correspond to the shortest path on the ground, this approach constitutes a practical solution to a basic navigation problem for autonomous mobile robots.

Impact and interest:

0 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

129 since deposited on 02 Nov 2007
2 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 10575
Item Type: Conference Paper
Refereed: Yes
Additional Information: For more information, please refer to the conference website (see hypertext link) or contact the authors.
Additional URLs:
ISBN: 978-3-939350-35-4
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Adaptive Agents and Intelligent Robotics (080101)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2007 (please consult authors)
Deposited On: 02 Nov 2007 00:00
Last Modified: 29 Feb 2012 13:35

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page