The University of Southampton
University of Southampton Institutional Repository

Genetic programming, logic design and case-based reasoning for obstacle avoidance

Genetic programming, logic design and case-based reasoning for obstacle avoidance
Genetic programming, logic design and case-based reasoning for obstacle avoidance
This paper draws on three different sets of ideas from computer science to develop a self-learning system capable of delivering an obstacle avoidance decision tree for simple mobile robots. All three topic areas have received considerable attention in the literature but their combination in the fashion reported here is new. This work is part of a wider initiative on problems where human reasoning is currently the most commonly used form of control. Typical examples are in sense and avoid studies for vehicles – for example the current lack of regulator approved sense and avoid systems is a key road-block to the wider deployment of uninhabited aerial vehicles (UAVs) in civil airspaces.
The paper shows that by using well established ideas from logic circuit design (the “espresso” algorithm) to influence genetic programming (GP), it is possible to evolve well-structured case-based reasoning (CBR) decision trees that can be used to control a mobile robot. The enhanced search works faster than a standard GP search while also providing improvements in best and average results. The resulting programs are non-intuitive yet solve difficult obstacle avoidance and exploration tasks using a parsimonious and unambiguous set of rules. They are based on studying sensor inputs to decide on simple robot movement control over a set of random maze navigation problems.
decision tree, data mining, feature engineering, classification, algorithm construction
0302-9743
104-118
Keane, Andy J.
26d7fa33-5415-4910-89d8-fb3620413def
Keane, Andy J.
26d7fa33-5415-4910-89d8-fb3620413def

Keane, Andy J. (2015) Genetic programming, logic design and case-based reasoning for obstacle avoidance. Lecture Notes in Computer Science, 8994, 104-118. (doi:10.1007/978-3-319-19084-6_9).

Record type: Article

Abstract

This paper draws on three different sets of ideas from computer science to develop a self-learning system capable of delivering an obstacle avoidance decision tree for simple mobile robots. All three topic areas have received considerable attention in the literature but their combination in the fashion reported here is new. This work is part of a wider initiative on problems where human reasoning is currently the most commonly used form of control. Typical examples are in sense and avoid studies for vehicles – for example the current lack of regulator approved sense and avoid systems is a key road-block to the wider deployment of uninhabited aerial vehicles (UAVs) in civil airspaces.
The paper shows that by using well established ideas from logic circuit design (the “espresso” algorithm) to influence genetic programming (GP), it is possible to evolve well-structured case-based reasoning (CBR) decision trees that can be used to control a mobile robot. The enhanced search works faster than a standard GP search while also providing improvements in best and average results. The resulting programs are non-intuitive yet solve difficult obstacle avoidance and exploration tasks using a parsimonious and unambiguous set of rules. They are based on studying sensor inputs to decide on simple robot movement control over a set of random maze navigation problems.

Text
Path_Planning.pdf - Other
Restricted to Repository staff only
Request a copy

More information

Accepted/In Press date: 28 March 2015
e-pub ahead of print date: 29 May 2015
Keywords: decision tree, data mining, feature engineering, classification, algorithm construction
Organisations: Computational Engineering & Design Group

Identifiers

Local EPrints ID: 378237
URI: http://eprints.soton.ac.uk/id/eprint/378237
ISSN: 0302-9743
PURE UUID: 475a4d79-2a4d-4ab4-8177-1d15b8503fc8
ORCID for Andy J. Keane: ORCID iD orcid.org/0000-0001-7993-1569

Catalogue record

Date deposited: 29 Jun 2015 12:55
Last modified: 26 Jul 2022 01:35

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×