Robotics: control, sensing, vision, and intelligence
Robotics: control, sensing, vision, and intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machines That Walk: The Adaptive Suspension Vehicle
Machines That Walk: The Adaptive Suspension Vehicle
Optimal turning gait of a six-legged robot using a GA-fuzzy approach
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Numerical Methods for Engineers
Numerical Methods for Engineers
Soft Computing
Estimation of optimal feet forces and joint torques for on-line control of six-legged robot
Robotics and Computer-Integrated Manufacturing
Torque Distribution in a Six-Legged Robot
IEEE Transactions on Robotics
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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.