Combining Stochastic Automata and Classication Techniques for Supervision and Safe Orchard Navigation

Fabio Caponetti (Invited author), Mogens Blanke (Invited author)

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Cost drivers in commercial orchards are time-consuming tasks as the drive through rows for spraying, cutting grass or collecting fruit. An automated tractor can be an answer to enhance production efficiency. For this to be acceptable by public and authorities, safety and reliability are crucial, hence information redundancy is needed to achieve a fault tolerant system. This paper addresses ways to extract information from laser scanner data. A Gaussian Mixture model is used to classify laser data into obstacles, while through diagnosis, a stochastic automaton model gives a semantic position estimate relying only on laser perception. Results demonstrate the feasibility of implementation in an autonomous tractor that use diagnosis and active fault-tolerant control to enhance availability and safety
Original languageEnglish
Title of host publication7. IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes
Publication date2009
Pages205-210
ISBN (Print)978-3-902661-46-3
DOIs
Publication statusPublished - 2009
Event7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes - Barcelona, Spain
Duration: 30 Jun 20093 Jul 2009
Conference number: 7
http://safeprocess09.upc.es/

Conference

Conference7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes
Number7
CountrySpain
CityBarcelona
Period30/06/200903/07/2009
Internet address

Keywords

  • Statistical methods
  • Discrete-event and hybrid systems
  • Dependability

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