Abstract
Soft computing-based approaches have been developed to predict specific energy consumption and stability margin of a six-legged robot ascending and descending some gradient terrains. Three different neuro-fuzzy and one neural network-based approaches have been developed. The performances of these approaches are compared among themselves, through computer simulations. Genetic algorithm-tuned multiple adaptive neuro-fuzzy inference system is found to perform better than other three approaches for predicting both the outputs. This could be due to a more exhaustive search carried out by the genetic algorithm in comparison with back-propagation algorithm and the use of two separate adaptive neuro-fuzzy inference systems for two different outputs. A designer may use the developed soft computing-based approaches in order to predict specific energy consumption and stability margin of the robot for a set of input parameters, beforehand.
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Roy, S.S., Pratihar, D.K. Soft computing-based approaches to predict energy consumption and stability margin of six-legged robots moving on gradient terrains. Appl Intell 37, 31–46 (2012). https://doi.org/10.1007/s10489-011-0311-2
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DOI: https://doi.org/10.1007/s10489-011-0311-2