Abstract
Humans can perform natural and robust walking behavior. They can even quickly adapt to different situations, like changing their walking speed to synchronize with the speed of a treadmill. Reproducing such complex abilities with artificial bipedal systems is still a difficult problem. To tackle this problem, we present here an adaptive combinatorial neural control circuit consisting of reflex-based and central pattern generator (CPG)-based mechanisms. The reflex-based control mechanism basically generates energy-efficient bipedal locomotion while the CPG-based mechanism with synaptic plasticity ensures robustness against loss of global sensory feedback (e.g., foot contact sensors) as well as allows for adaptation within a few steps to deal with environmental changes. We have successfully applied our control approach to the biomechanical bipedal robot DACBOT. As a result, the robot can robustly walk with energy efficiency and quickly adapt to different speeds of a treadmill.
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Acknowledgments
This research was supported partly by Bernstein Center for Computational Neuroscience II Goettingen (BCCN grant 01GQ1005A, project D1) and Center for BioRobotics (CBR) at the University of Southern Denmark (SDU).
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Di Canio, G., Stoyanov, S., Balmori, I.T., Larsen, J.C., Manoonpong, P. (2016). Adaptive Combinatorial Neural Control for Robust Locomotion of a Biped Robot. In: Tuci, E., Giagkos, A., Wilson, M., Hallam, J. (eds) From Animals to Animats 14. SAB 2016. Lecture Notes in Computer Science(), vol 9825. Springer, Cham. https://doi.org/10.1007/978-3-319-43488-9_28
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DOI: https://doi.org/10.1007/978-3-319-43488-9_28
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