Abstract
The present paper proposes the approach of locomotion in mammals to be applied in articulated robotics. This is achieved using Central Pattern Generators by amplitude modulation of oscillatory signals to communicate the angle of rotation of each of the joints that are involved in a specific type of locomotion. Performing simulations to determine viability by frequency and amplitude, a better response was found in the amplitude modulation. A series of locomotion data with dogs were compiled and used as a reference for the amplitude modulation of the differential equation systems that replicate the Central Pattern Generators of the articulations of the quadrupedal robot. Recurrent neural networks in continuous time were used to represent the CPG. The angle was modulated as a function of the amplitude of the cyclic signal produced by the Central Pattern Generators allowing to manage the setpoints (angles) for a given articulation, between 0° and 90°. Other works, although related to Central Pattern Generators, and some focused on reproducing the model, none of them deals with the construction of modulated signals that represent joint angles based on data obtained from biomechanical studies of locomotion by quadrupeds. A distributed autonomous control architecture based on modular and hierarchical Central Pattern Generators, organized in two layers, that simultaneously synchronizes and executes the movement of each joint from each leg, and for the total movement production is proposed.
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References
Eve, M., Dirk, B.: Central pattern generators and the control of rhythmic movements. Curr. Biol. 11(23), 986–996 (2001)
Cohen, A.H., Rossignol, S., Grillner, S.: Neural Control of Rhythmic Movements in Vertebrate, pp 1–500. Wiley (1988)
Grillner, S., Wallen, P.: Central pattern generators for locomotion, with special reference to vertebrates. Ann. rev. Neurosci. 8, 233–261 (1985)
Buchli, J., Ijspeert, A.J.: Distributed central pattern generator model for robotics application based on phase sensitivity analysis. In: Ijspeert, A.J., Murata, M., Wakamiya, N. (eds.) BioADIT 2004. LNCS, vol. 3141, pp. 333–349. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-27835-1_25
Protopapas, A., Bower, J.M.: Dynamics of cerebral cortical networks. In: The Book of GENESIS, pp. 149–168. Springer, New York (1998). https://doi.org/10.1007/978-1-4612-1634-6_9
Abe, M., Iwama, K., Takato, M., Saito, K., Uchikoba, F.: Hardware neural network models of CPG and PWM for controlling servomotor system in quadruped robot. Artif. Life Robot. 22(3), 391–397 (2017)
Gonzalez-Luchena, I., Gonzalez-Rodriguez, A.G., Gonzalez-Rodriguez, A., Adame-Sanchez, C., Castillo-Garcia, F.J.: A new algorithm to maintain lateral stabilization during the running gait of a quadruped robot. Robot. Auton. Syst. 83, 57–72 (2016)
Li, X., Wang, W., Yi, J., Gait transition based on CPG modulation for quadruped locomotion. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM, 2015 August, art. no. 7222583, pp. 500–505 (2015)
Harischandra, N.R., Krause, A.F., Dürr, V.: Stable phase-shift despite quasi-rhythmic movements: a CPG-driven dynamic model of active tactile exploration in an insect. Front. Comput. Neurosci. 9, 107, 16 p (2015)
Tran, D.T., et al.: Central pattern generator based reflexive control of quadruped walking robots using a recurrent neural network. Robot. Auton. Syst. 62(10), 1497–1516 (2014)
Shahbazi, H., Parandeh, R., Jamshidi, K.: Implementation of imitation learning using natural learner central pattern generator neural networks. Neural Netw. 83, 94–108 (2016)
Gutierrez, Gabrielle J., Marder, E.: Modulation of a single neuron has state- dependent actions on circuit dynamics. eNeuro 1, 1–12 (2014)
Nachstedt, T., Tetzlaff, C., Manoonpong, P.: Fast dynamical coupling enhances frequency adaptation of oscillators for robotic locomotion control. Front. Neurorobot. 11, 1–14 (2017)
Fukuoka, Y., Habu, Y., Fukui, T.: Analysis of the gait generation principle by a simulated quadruped model with a CPG incorporating vestibular modulation. Biol. Cybern. 107, 695–710 (2013)
Ralev, D., Cappelletto, J., Grieco, J.C., Certad, N., Cabrera, M. E.: Analysis of oscillators for the generation of rhythmic patterns in legged robot locomotion. In: IEEE Latin American Robotics Symposium, pp 125–128 (2013)
Reyes, M.B., Carelli, P.V., Sartorelli, J.C., Pinto, R.D.: A modeling approach on why simple central pattern generators are built of irregular neurons. Plus One 10, 1–22 (2015)
Rojas, R.: Neural Networks, p. 509. Springer, Berlin (1996). https://doi.org/10.1007/978-3-642-61068-4
Rico, E.M., Hernandez, J.A.: Analysis and application of a displacement CPG-based method on articulated frames. In: Solano, A., Ordoñez, H. (eds.) CCC 2017. CCIS, vol. 735, pp. 495–510. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66562-7_36
Cappelletto, J., Estévez, P., Grieco, J.C., Medina-Meléndez, W., Fernández-López, G.: Gait synthesis in legged robot locomotion using a CPG-based model. In: Journa Bioinspiration and Robotics: Walking and Climbing Robots, Vienna, pp 227–246 (2007)
Goslow, G.E., Seeherman, H.J., Taylor, C.R., McCutchln, M.N., Heglund, N.C.: Electrical activity and relative length changes of dog limb muscles as a function of speed and gait. Exp. Biol. 94, 15–42 (1981)
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Rico Mesa, E.M., Hernández-Riveros, JA. (2018). Modulation of Central Pattern Generators (CPG) for the Locomotion Planning of an Articulated Robot. In: Florez, H., Diaz, C., Chavarriaga, J. (eds) Applied Informatics. ICAI 2018. Communications in Computer and Information Science, vol 942. Springer, Cham. https://doi.org/10.1007/978-3-030-01535-0_24
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