Intelligent approaches in locomotion - a review

Jonathan Wright, Ivan Jordanov

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In this paper we review more than 140 publications and try to not only give a snap shot of the current state of the art research in the area, but also to critically analyse and compare different methodologies used in this research field. Among the investigated intelligent approaches for solving locomotion problems are oscillator based Central Pattern Generators, Neural Networks, Hidden Markov models, Rule Based and Fuzzy Logic systems, as well as Analytical concepts. We try to compare those methods based on the quality of the produced solutions in terms of time, stability, correctness and the expense and cost for achieving them. At the end of each section we list the advantages and disadvantages of the reviewed methods and scrutinize them considering the complexity of the approaches, their applicability to the investigated locomotion tasks and the constraints of the produced solutions. The reviewed publications examine a range of legged and non-legged systems, operating in simple and complex environments, with several different locomotion tasks.
Original languageEnglish
Pages (from-to)255-277
Number of pages23
JournalJournal of Intelligent & Robotic Systems
Issue number2
Early online date26 Oct 2014
Publication statusPublished - 1 Nov 2015


  • legged locomotion
  • central pattern generator
  • neural networks
  • optimisation
  • fuzzy logic
  • hidden Markov models
  • Machine learning


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