- author = "Andy Keane",
- title = "Genetic Programming, Logic Design and Case-Based Reasoning for Obstacle Avoidance",
- booktitle = "9th International Conference Learning and Intelligent Optimization, LION 2015",
- year = "2015",
- editor = "Clarisse Dhaenens and Laetitia Jourdan and Marie-Eleonore Marmion",
- volume = "8994",
- series = "Lecture Notes in Computer Science",
- pages = "104--118",
- address = "Lille, France",
- month = jan # " 12-15",
- publisher = "Springer",
- note = "Revised Selected Papers",
- keywords = "genetic algorithms, genetic programming, decision tree, data miningfication, algorithm construction, robot",
- isbn13 = "978-3-319-19083-9",
- bibsource = "OAI-PMH server at eprints.soton.ac.uk",
- oai = "oai:eprints.soton.ac.uk:378237",
- type = "PeerReviewed",
-
URL = "
http://eprints.soton.ac.uk/378237/1/Path_Planning.pdf",
-
URL = "
http://eprints.soton.ac.uk/378237/",
- isbn13 = "978-3-319-19084-6",
-
DOI = "
doi:10.1007/978-3-319-19084-6_9",
- size = "15 pages",
-
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.",
- notes = "VLSI, RPN",
