L*: An intelligent path planning algorithm based on renormalized measure of probabilistic regular languages

Ishanu Chattopadhyay, Goutham Mallapragada, Asok Ray

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A novel path planning algorithm L* is introduced that reduces the problem to optimization of a probabilistic finite state machine and applies the rigorous theory of language-measure-theoretic optimal control to compute ν-optimal paths to the specified goal. It is shown that although the underlying navigation model is probabilistic, the proposed algorithm computes plans that can be executed in a deterministic sense with automated optimal trade-off between path length and robustness under dynamic uncertainty. The algorithm has been validated on mobile robotic platforms in a laboratory environment.

Original languageEnglish (US)
Title of host publication2008 American Control Conference, ACC
Pages1249-1254
Number of pages6
DOIs
StatePublished - Sep 30 2008
Event2008 American Control Conference, ACC - Seattle, WA, United States
Duration: Jun 11 2008Jun 13 2008

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Other

Other2008 American Control Conference, ACC
Country/TerritoryUnited States
CitySeattle, WA
Period6/11/086/13/08

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'L*: An intelligent path planning algorithm based on renormalized measure of probabilistic regular languages'. Together they form a unique fingerprint.

Cite this