Multiobjective Evolution of Biped Robot Gaits Using Advanced Continuous Ant-Colony Optimized Recurrent Neural Networks

This paper proposes the optimization of a fully connected recurrent neural network (FCRNN) using advanced multiobjective continuous ant colony optimization (AMO-CACO) for the multiobjective gait generation of a biped robot (the NAO). The FCRNN functions as a central pattern generator and is optimize...

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Vydané v:IEEE transactions on cybernetics Ročník 48; číslo 6; s. 1910 - 1922
Hlavní autori: Juang, Chia-Feng, Yeh, Yen-Ting
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.06.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithm design and analysis
Ant colony optimization
Ant colony optimization (ACO)
biped robot gait control
central pattern generators (CPGs)
Computer simulation
Gait
Genetic algorithms
Hip
Knee
Legged locomotion
Mathematical programming
multiobjective optimization algorithms
Multiple objective analysis
Neural networks
Optimization
Pareto optimization
Pareto optimum
Pitch (inclination)
Recurrent neural networks
recurrent neural networks (RNNs)
Robots
Rolling motion
Walking
Yaw
ISSN:2168-2267, 2168-2275, 2168-2275
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Shrnutí:This paper proposes the optimization of a fully connected recurrent neural network (FCRNN) using advanced multiobjective continuous ant colony optimization (AMO-CACO) for the multiobjective gait generation of a biped robot (the NAO). The FCRNN functions as a central pattern generator and is optimized to generate angles of the hip roll and pitch, the knee pitch, and the ankle pitch and roll. The performance of the FCRNN-generated gait is evaluated according to the walking speed, trajectory straightness, oscillations of the body in the pitch and yaw directions, and walking posture, subject to the basic constraints that the robot cannot fall down and must walk forward. This paper formulates this gait generation task as a constrained multiobjective optimization problem and solves this problem through an AMO-CACO-based evolutionary learning approach. The AMO-CACO finds Pareto optimal solutions through ant-path selection and sampling operations by introducing an accumulated rank for the solutions in each single-objective function into solution sorting to improve learning performance. Simulations are conducted to verify the AMO-CACO-based FCRNN gait generation performance through comparisons with different multiobjective optimization algorithms. Selected software-designed Pareto optimal FCRNNs are then applied to control the gait of a real NAO robot.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2017.2718037