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
Free gait generation with reinforcement learning for a six-legged robot
Robotics and Autonomous Systems
Soft Computing
Robotics and Autonomous Systems
Estimation of optimal feet forces and joint torques for on-line control of six-legged robot
Robotics and Computer-Integrated Manufacturing
Effects of turning gait parameters on energy consumption and stability of a six-legged walking robot
Robotics and Autonomous Systems
Expert Systems with Applications: An International Journal
Generating continuous free crab gaits for quadruped robots on irregular terrain
IEEE Transactions on Robotics
Torque Distribution in a Six-Legged Robot
IEEE Transactions on Robotics
Special issue recent advances in soft computing: Theories and applications
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Recent Advances in Soft Computing: Theories and Applications
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In this paper, adaptive neuro-fuzzy expert systems have been designed to predict specific energy consumption and normalized energy stability margin for crab walking of a six-legged robot. The application of this technique for crab gait generation of the six-legged robot is new, to the best of the authors' knowledge. Three approaches based on adaptive neuro-fuzzy inference system have been developed and their performances are compared with each other. Genetic algorithm-tuned multiple adaptive neuro-fuzzy inference systems are found to perform better than other approaches. This could be due to a more exhaustive search carried out by the genetic algorithm compared to back-propagation algorithm and the use of two separate adaptive neuro-fuzzy inference systems for two different outputs.