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Task-oriented parameter tuning based on priority condition for biologically inspired robot application. (English) Zbl 1394.93212
Summary: This work gives a biologically inspired control scheme for controlling a robotic system. Novel adaptive behaviors are observed from humans or animals even in unexpected disturbances or environment changes. This is why they have neural oscillator networks in the spinal cord to yield rhythmic-motor primitives robustly under a changing task. Hence, this work focuses on rhythmic arm movements that can be accomplished in terms of employing a control approach based on an artificial neural oscillator model. The main challenge is to determine various parameters for applying a neural feedback to robotic systems with performing a desired behavior and self-maintaining the entrainment effect. Hence, this work proposes a task-oriented parameter tuning algorithm based on the simulated annealing (SA). This work also illustrates how to technically implement the proposed control scheme exploiting a virtual force and neural feedback. With parameters tuned, it is verified in simulations that a 3-DOF planar robotic arm traces a given trajectory precisely, adapting to uneven external disturbances.
93C85 Automated systems (robots, etc.) in control theory
92B99 Mathematical biology in general
34C15 Nonlinear oscillations and coupled oscillators for ordinary differential equations
Full Text: DOI
[1] Grillner, S., Neurobiological bases of rhythmic motor acts in vertebrates, Science, 228, 4696, 143-149, (1985)
[2] Guertin, P. A., The mammalian central pattern generator for locomotion, Brain Research Reviews, 62, 1, 45-56, (2009)
[3] Verdaasdonk, B. W.; Koopman, H. F. J. M.; Van Der Helm, F. C. T., Energy efficient and robust rhythmic limb movement by central pattern generators, Neural Networks, 19, 4, 388-400, (2006) · Zbl 1098.92018
[4] Matsuoka, K., Sustained oscillations generated by mutually inhibiting neurons with adaptation, Biological Cybernetics, 52, 6, 367-376, (1985) · Zbl 0574.92013
[5] Matsuoka, K., Mechanisms of frequency and pattern control in the neural rhythm generators, Biological Cybernetics, 56, 5-6, 345-353, (1987)
[6] Taga, G., A model of the neuro-musculo-skeletal system for anticipatory adjustment of human locomotion during obstacle avoidance, Biological Cybernetics, 78, 1, 9-17, (1998) · Zbl 0884.92008
[7] Morimoto, J.; Endo, G.; Nakanishi, J.; Cheng, G., A biologically inspired biped locomotion strategy for humanoid robots: modulation of sinusoidal patterns by a coupled oscillator model, IEEE Transactions on Robotics, 24, 1, 185-191, (2008)
[8] Fukuoka, Y.; Kimura, H., Dynamic locomotion of a biomorphic quadruped ‘Tekken’ robot using various gaits: walk, trot, free-gait and bound, Applied Bionics and Biomechanics, 6, 1, 63-71, (2009)
[9] Liu, C.; Chen, Q.; Wang, D., CPG-inspired workspace trajectory generation and adaptive locomotion control for quadruped robots, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 41, 3, 867-880, (2011)
[10] Crespi, A.; Ijspeert, A. J., Online optimization of swimming and crawling in an amphibious snake robot, IEEE Transactions on Robotics, 24, 1, 75-87, (2008)
[11] Seo, K.; Chung, S.-J.; Slotine, J.-J. E., CPG-based control of a turtle-like underwater vehicle, Autonomous Robots, 28, 3, 247-269, (2010)
[12] Chung, S.-J.; Dorothy, M., Neurobiologically inspired control of engineered flapping flight, Journal of Guidance, Control, and Dynamics, 33, 2, 440-453, (2010)
[13] Ijspeert, A. J., Central pattern generators for locomotion control in animals and robots: a review, Neural Networks, 21, 4, 642-653, (2008)
[14] Williamson, M. M., Rhythmic robot arm control using oscillators, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems
[15] Esposti, R.; Cavallari, P.; Baldissera, F., Feedback control of the limbs position during voluntary rhythmic oscillation, Biological Cybernetics, 97, 2, 123-136, (2007) · Zbl 1125.92010
[16] Yang, W.; Kwon, J.; Chong, N. Y.; Oh, Y., Biologically inspired robotic arm control using an artificial neural oscillator, Mathematical Problems in Engineering, 2010, (2010) · Zbl 1191.93111
[17] Yang, W.; Kim, H.; You, B. J., Biologically inspired self-stabilizing control for bipedal robots, International Journal of Advanced Robotic Systems, 10, article 144, (2013)
[18] Gomes, M. A.; Siqueira, A. A. G.; Gobbo, R. G., Parameter optimization for neural oscillators applied to trajectory generation of an exoskeleton for lower limbs, ABCM Symposium Series in Mechatronics, 5, 1167-1174, (2012)
[19] Hattori, Y.; Suzuki, M.; Soh, Z.; Kobayashi, Y.; Tsuji, T., Theoretical and evolutionary parameter tuning of neural oscillators with a double-chain structure for generating rhythmic signals, Neural Computation, 24, 3, 635-675, (2012)
[20] Yang, W.; Chong, N. Y.; Kwon, J.; You, B. J., Self-sustaining rhythmic arm motions using neural oscillators, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS ’08)
[21] Yang, W.; Bae, J.-H.; Kim, H., VFI-based robotic arm control for natural adaptive motion, International Journal of Advanced Robotic Systems, 11, 1-11, (2014)
[22] de Lasa, M.; Hertzmann, A., Prioritized optimization for task-space control, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS ’09)
[23] Kirkpatrick, S.; Gelatt, C. D.; Vecchi, M. P., Optimization by simulated annealing, American Association for the Advancement of Science, 220, 4598, 671-680, (1983) · Zbl 1225.90162
[24] Geman, S.; Geman, D., Stochastic relaxation, gibbs distributions, and the Bayesian restoration of images, IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 6, 721-741, (1984) · Zbl 0573.62030
[25] Berret, B.; Chiovetto, E.; Nori, F.; Pozzo, T., Evidence for composite cost functions in arm movement planning: an inverse optimal control approach, PLoS Computational Biology, 7, 10, (2011)
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