abstract

• This work presents two computationally low-cost optimization algorithms to determine parameters for the Dual-SLIP model producing stable walking gaits. While previous approaches use exhaustive or random searches and techniques requiring long simulations typically running until a fall is detected or an end condition is met, the approaches presented here only require the simulation of a single step thus reducing the computation time. The proposed optimization algorithms are based on Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). Also, an Artificial Neural Network (ANN) and a Deep Neural Network (DNN) are presented to predict the stability and the number of steps a given set of parameters will be able to produce. After training, these algorithms do not require any simulation of the walking model to yield their result. Simulation results show the efficacy of the proposed algo-rithms. © 2022 IEEE.

authors

• González de Alba Alejandro
• Maya Méndez Mauro Eduardo

publication date

• 2022-01-01

funding provided via

• Consejo Nacional de Ciencia y Tecnología, CONACYT: 713202  Grant

published in

• International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022  Proceedings

keywords

• Artificial Neural Networks; Dual SLIP; Genetic Algorithm; Particle Swarm Optimization; Spring Loaded Inverted Pendulum Deep neural networks; Inverted pendulum; Parameter estimation; Particle swarm optimization (PSO); Dual SLIP; Low-costs; Optimization algorithms; Parameter optimization; Parameter stability; Particle swarm; Particle swarm optimization; Spring loaded inverted pendulums; Stability prediction; Swarm optimization; Genetic algorithms

Digital Object Identifier (DOI)

• 10.1109/ICECCME55909.2022.9988248

PubMed ID

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