By Topic

Environment learning using a distributed representation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
M. J. Mataric ; MIT Artificial Intelligence Lab., Cambridge, MA, USA

A method for robust mobile robot navigation and environmental learning is presented. It was implemented and tested on a physical robot. The method consists of a collection of simple, incrementally designed robot behaviors. The behaviors receive sonar and compass data which they use to dynamically detect landmarks and construct a distributed map of the environment. The map is represented as a graph in which each node is a collection of augmented finite state machines functioning in parallel. The distributed nature of the map allows for localization in constant time. The method utilizes a modified spreading of activation scheme to accomplish robust linear-time path planning. It is capable of generating both topologically and physically shortest paths to the goal. The method uses local information to achieve the global task without having to replan if the robot becomes lost or strays off the desired path

Published in:

Robotics and Automation, 1990. Proceedings., 1990 IEEE International Conference on

Date of Conference:

13-18 May 1990