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      Synchronization of Non-linear Oscillators for Neurobiologically Inspired Control on a Bionic Parallel Waist of Legged Robot

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          Abstract

          Synchronization of coupled non-linear oscillators inspired by a central pattern generator (CPG) can control the bionic robot and promote the coordination and diversity of locomotion. However, for a robot with a strong mutual coupled structure, such neurobiological control is still missing. In this contribution, we present a σ-Hopf harmonic oscillator with decoupled parameters to expand the solution space of the locomotion of the robot. Unlike the synchronization of original Hopf oscillators, which has been fully discussed, the asymmetric factor of σ-Hopf oscillator causes a deformation in oscillation waveform. Using the non-linear synchronization theory, we construct the transition state model of the synchronization process to analyze the asymmetrical distortion, period change and duty ratio inconsistency. Then a variable coupling strength is introduced to eliminate the waveform deformation and maintain the fast convergence rate. Finally, the approach is used for the locomotion control of a bionic parallel waist of legged robot, which is a highly coupled system. The effectiveness of the approach in both independent and synthesis behavior of four typical motion patterns are validated. The result proves the importance of controllability of the oscillation waveform and the instantaneous state of the synchronization, which benefits the transition and transformation of the locomotion and makes the coupling motion more flexible.

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          Most cited references88

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          Central pattern generators for locomotion control in animals and robots: a review.

          The problem of controlling locomotion is an area in which neuroscience and robotics can fruitfully interact. In this article, I will review research carried out on locomotor central pattern generators (CPGs), i.e. neural circuits capable of producing coordinated patterns of high-dimensional rhythmic output signals while receiving only simple, low-dimensional, input signals. The review will first cover neurobiological observations concerning locomotor CPGs and their numerical modelling, with a special focus on vertebrates. It will then cover how CPG models implemented as neural networks or systems of coupled oscillators can be used in robotics for controlling the locomotion of articulated robots. The review also presents how robots can be used as scientific tools to obtain a better understanding of the functioning of biological CPGs. Finally, various methods for designing CPGs to control specific modes of locomotion will be briefly reviewed. In this process, I will discuss different types of CPG models, the pros and cons of using CPGs with robots, and the pros and cons of using robots as scientific tools. Open research topics both in biology and in robotics will also be discussed.
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            Learning agile and dynamic motor skills for legged robots

            Legged robots pose one of the greatest challenges in robotics. Dynamic and agile maneuvers of animals cannot be imitated by existing methods that are crafted by humans. A compelling alternative is reinforcement learning, which requires minimal craftsmanship and promotes the natural evolution of a control policy. However, so far, reinforcement learning research for legged robots is mainly limited to simulation, and only few and comparably simple examples have been deployed on real systems. The primary reason is that training with real robots, particularly with dynamically balancing systems, is complicated and expensive. In the present work, we introduce a method for training a neural network policy in simulation and transferring it to a state-of-the-art legged system, thereby leveraging fast, automated, and cost-effective data generation schemes. The approach is applied to the ANYmal robot, a sophisticated medium-dog–sized quadrupedal system. Using policies trained in simulation, the quadrupedal machine achieves locomotion skills that go beyond what had been achieved with prior methods: ANYmal is capable of precisely and energy-efficiently following high-level body velocity commands, running faster than before, and recovering from falling even in complex configurations.
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              Neural bases of goal-directed locomotion in vertebrates--an overview.

              The different neural control systems involved in goal-directed vertebrate locomotion are reviewed. They include not only the central pattern generator networks in the spinal cord that generate the basic locomotor synergy and the brainstem command systems for locomotion but also the control systems for steering and control of body orientation (posture) and finally the neural structures responsible for determining which motor programs should be turned on in a given instant. The role of the basal ganglia is considered in this context. The review summarizes the available information from a general vertebrate perspective, but specific examples are often derived from the lamprey, which provides the most detailed information when considering cellular and network perspectives.
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                Author and article information

                Contributors
                Journal
                Front Neurorobot
                Front Neurorobot
                Front. Neurorobot.
                Frontiers in Neurorobotics
                Frontiers Media S.A.
                1662-5218
                02 August 2019
                2019
                : 13
                : 59
                Affiliations
                Key Laboratory of Road Construction Technology and Equipment of MOE, Chang'an University , Xi'an, China
                Author notes

                Edited by: Poramate Manoonpong, University of Southern Denmark, Denmark

                Reviewed by: Arash Ahmadi, University of Windsor, Canada; Chengju Liu, Yangpu Hospital, Tongji University, China; Horacio Rostro Gonzalez, University of Guanajuato, Mexico

                *Correspondence: Yaguang Zhu zhuyaguang@ 123456chd.edu.cn
                Article
                10.3389/fnbot.2019.00059
                6687854
                38edb11a-0cf7-460f-b3a9-5b95bdd4e41f
                Copyright © 2019 Zhu, Zhou, Gao and Liu.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 27 April 2019
                : 11 July 2019
                Page count
                Figures: 10, Tables: 0, Equations: 29, References: 97, Pages: 16, Words: 10443
                Funding
                Funded by: National Natural Science Foundation of China 10.13039/501100001809
                Funded by: China Postdoctoral Science Foundation 10.13039/501100002858
                Award ID: 2018T111005
                Categories
                Neuroscience
                Original Research

                Robotics
                bionic parallel waist,locomotion control,synchronization,transition state,σ-hopf oscillator

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