Multi-objective path planning in GPS denied environments under localization constraints

Shaunak D. Bopardikar, Brendan Englot, Alberto Speranzon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

The main contribution of this paper is a novel planning algorithm that, starting from a probabilistic roadmap, efficiently constructs an expanded graph used to search for the optimal solution of a multi-objective problem. The primary cost is the shortest path from start to goal and the secondary cost is related to the state estimation error covariance. This needs to be optimized as we assume the navigation to be in a GPS denied environment. The proposed algorithm is efficient as it relies on a scalar metric, related to the largest eigenvalue of the error covariance, and adaptively quantizes the secondary cost, yielding a graph whose number of vertices and edges provides a good tradeoff between optimality and computational complexity. Numerical examples show the advantage of the proposed approach compared to methods where the expanded graph is built by quantizing the secondary cost uniformly.

Original languageEnglish
Title of host publication2014 American Control Conference, ACC 2014
Pages1872-1879
Number of pages8
DOIs
StatePublished - 2014
Event2014 American Control Conference, ACC 2014 - Portland, OR, United States
Duration: 4 Jun 20146 Jun 2014

Publication series

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

Conference

Conference2014 American Control Conference, ACC 2014
Country/TerritoryUnited States
CityPortland, OR
Period4/06/146/06/14

Keywords

  • Autonomous Systems
  • GPS-denied Localization
  • Motion Planning
  • Multi-objective Optimization

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