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Intelligent Approaches in Locomotion - A Review

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Abstract

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.

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  1. School of Computing, University of Portsmouth, Lion Terrace, Portsmouth, PO1 3HE, UK

    Joe Wright & Ivan Jordanov

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Wright, J., Jordanov, I. Intelligent Approaches in Locomotion - A Review. J Intell Robot Syst 80, 255–277 (2015). https://doi.org/10.1007/s10846-014-0149-z

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