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Dürr V, Arena PP, Cruse H, Dallmann CJ, Drimus A, Hoinville T, Krause T, Mátéfi-Tempfli S, Paskarbeit J, Patanè L, Schäffersmann M, Schilling M, Schmitz J, Strauss R, Theunissen L, Vitanza A, Schneider A. Integrative Biomimetics of Autonomous Hexapedal Locomotion. Front Neurorobot 2019; 13:88. [PMID: 31708765 PMCID: PMC6819508 DOI: 10.3389/fnbot.2019.00088] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Accepted: 10/07/2019] [Indexed: 01/31/2023] Open
Abstract
Despite substantial advances in many different fields of neurorobotics in general, and biomimetic robots in particular, a key challenge is the integration of concepts: to collate and combine research on disparate and conceptually disjunct research areas in the neurosciences and engineering sciences. We claim that the development of suitable robotic integration platforms is of particular relevance to make such integration of concepts work in practice. Here, we provide an example for a hexapod robotic integration platform for autonomous locomotion. In a sequence of six focus sections dealing with aspects of intelligent, embodied motor control in insects and multipedal robots-ranging from compliant actuation, distributed proprioception and control of multiple legs, the formation of internal representations to the use of an internal body model-we introduce the walking robot HECTOR as a research platform for integrative biomimetics of hexapedal locomotion. Owing to its 18 highly sensorized, compliant actuators, light-weight exoskeleton, distributed and expandable hardware architecture, and an appropriate dynamic simulation framework, HECTOR offers many opportunities to integrate research effort across biomimetics research on actuation, sensory-motor feedback, inter-leg coordination, and cognitive abilities such as motion planning and learning of its own body size.
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Affiliation(s)
- Volker Dürr
- Department of Biological Cybernetics, Faculty of Biology, Bielefeld University, Bielefeld, Germany
- Cognitive Interaction Technology: Center of Excellence, Bielefeld University, Bielefeld, Germany
| | - Paolo P. Arena
- DIEEI: Dipartimento di Ingegneria Elettrica Elettronica e Informatica, Università degli Studi di Catania, Catania, Italy
| | - Holk Cruse
- Cognitive Interaction Technology: Center of Excellence, Bielefeld University, Bielefeld, Germany
| | - Chris J. Dallmann
- Department of Biological Cybernetics, Faculty of Biology, Bielefeld University, Bielefeld, Germany
- Cognitive Interaction Technology: Center of Excellence, Bielefeld University, Bielefeld, Germany
| | - Alin Drimus
- Mads Clausen Institute, University of Southern Denmark, Sønderborg, Denmark
| | - Thierry Hoinville
- Department of Biological Cybernetics, Faculty of Biology, Bielefeld University, Bielefeld, Germany
- Cognitive Interaction Technology: Center of Excellence, Bielefeld University, Bielefeld, Germany
| | - Tammo Krause
- Institut für Entwicklungsbiologie und Neurobiologie, Johannes Gutenberg-Universität, Mainz, Germany
| | | | - Jan Paskarbeit
- Cognitive Interaction Technology: Center of Excellence, Bielefeld University, Bielefeld, Germany
| | - Luca Patanè
- DIEEI: Dipartimento di Ingegneria Elettrica Elettronica e Informatica, Università degli Studi di Catania, Catania, Italy
| | - Mattias Schäffersmann
- Cognitive Interaction Technology: Center of Excellence, Bielefeld University, Bielefeld, Germany
| | - Malte Schilling
- Cognitive Interaction Technology: Center of Excellence, Bielefeld University, Bielefeld, Germany
| | - Josef Schmitz
- Department of Biological Cybernetics, Faculty of Biology, Bielefeld University, Bielefeld, Germany
- Cognitive Interaction Technology: Center of Excellence, Bielefeld University, Bielefeld, Germany
| | - Roland Strauss
- Institut für Entwicklungsbiologie und Neurobiologie, Johannes Gutenberg-Universität, Mainz, Germany
| | - Leslie Theunissen
- Department of Biological Cybernetics, Faculty of Biology, Bielefeld University, Bielefeld, Germany
- Cognitive Interaction Technology: Center of Excellence, Bielefeld University, Bielefeld, Germany
| | - Alessandra Vitanza
- DIEEI: Dipartimento di Ingegneria Elettrica Elettronica e Informatica, Università degli Studi di Catania, Catania, Italy
| | - Axel Schneider
- Cognitive Interaction Technology: Center of Excellence, Bielefeld University, Bielefeld, Germany
- Institute of System Dynamics and Mechatronics, Bielefeld University of Applied Sciences, Bielefeld, Germany
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Harischandra N, Clare AJ, Zakotnik J, Blackburn LML, Matheson T, Dürr V. Evaluation of linear and non-linear activation dynamics models for insect muscle. PLoS Comput Biol 2019; 15:e1007437. [PMID: 31609992 PMCID: PMC6812852 DOI: 10.1371/journal.pcbi.1007437] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Revised: 10/24/2019] [Accepted: 09/25/2019] [Indexed: 11/18/2022] Open
Abstract
In computational modelling of sensory-motor control, the dynamics of muscle contraction is an important determinant of movement timing and joint stiffness. This is particularly so in animals with many slow muscles, as is the case in insects-many of which are important models for sensory-motor control. A muscle model is generally used to transform motoneuronal input into muscle force. Although standard models exist for vertebrate muscle innervated by many motoneurons, there is no agreement on a parametric model for single motoneuron stimulation of invertebrate muscle. Although several different models have been proposed, they have never been evaluated using a common experimental data set. We evaluate five models for isometric force production of a well-studied model system: the locust hind leg tibial extensor muscle. The response of this muscle to motoneuron spikes is best modelled as a non-linear low-pass system. Linear first-order models can approximate isometric force time courses well at high spike rates, but they cannot account for appropriate force time courses at low spike rates. A linear third-order model performs better, but only non-linear models can account for frequency-dependent change of decay time and force potentiation at intermediate stimulus frequencies. Some of the differences among published models are due to differences among experimental data sets. We developed a comprehensive toolbox for modelling muscle activation dynamics, and optimised model parameters using one data set. The "Hatze-Zakotnik model" that emphasizes an accurate single-twitch time course and uses frequency-dependent modulation of the twitch for force potentiation performs best for the slow motoneuron. Frequency-dependent modulation of a single twitch works less well for the fast motoneuron. The non-linear "Wilson" model that optimises parameters to all data set parts simultaneously performs better here. Our open-access toolbox provides powerful tools for researchers to fit appropriate models to a range of insect muscles.
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Affiliation(s)
- Nalin Harischandra
- Biological Cybernetics, Faculty of Biology, Bielefeld University, Bielefeld, Germany
- Cognitive Interaction Technology—Center of Excellence (CITEC), Bielefeld University, Bielefeld, Germany
| | - Anthony J. Clare
- University of Leicester, Department of Neuroscience, Psychology and Behaviour, Leicester, United Kingdom
| | - Jure Zakotnik
- Biological Cybernetics, Faculty of Biology, Bielefeld University, Bielefeld, Germany
| | | | - Tom Matheson
- University of Leicester, Department of Neuroscience, Psychology and Behaviour, Leicester, United Kingdom
| | - Volker Dürr
- Biological Cybernetics, Faculty of Biology, Bielefeld University, Bielefeld, Germany
- Cognitive Interaction Technology—Center of Excellence (CITEC), Bielefeld University, Bielefeld, Germany
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Toth TI, Schmidt J, Büschges A, Daun-Gruhn S. A neuro-mechanical model of a single leg joint highlighting the basic physiological role of fast and slow muscle fibres of an insect muscle system. PLoS One 2013; 8:e78247. [PMID: 24244298 PMCID: PMC3823925 DOI: 10.1371/journal.pone.0078247] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2013] [Accepted: 09/10/2013] [Indexed: 11/18/2022] Open
Abstract
In legged animals, the muscle system has a dual function: to produce forces and torques necessary to move the limbs in a systematic way, and to maintain the body in a static position. These two functions are performed by the contribution of specialized motor units, i.e. motoneurons driving sets of specialized muscle fibres. With reference to their overall contraction and metabolic properties they are called fast and slow muscle fibres and can be found ubiquitously in skeletal muscles. Both fibre types are active during stepping, but only the slow ones maintain the posture of the body. From these findings, the general hypothesis on a functional segregation between both fibre types and their neuronal control has arisen. Earlier muscle models did not fully take this aspect into account. They either focused on certain aspects of muscular function or were developed to describe specific behaviours only. By contrast, our neuro-mechanical model is more general as it allows functionally to differentiate between static and dynamic aspects of movement control. It does so by including both muscle fibre types and separate motoneuron drives. Our model helps to gain a deeper insight into how the nervous system might combine neuronal control of locomotion and posture. It predicts that (1) positioning the leg at a specific retraction angle in steady state is most likely due to the extent of recruitment of slow muscle fibres and not to the force developed in the individual fibres of the antagonistic muscles; (2) the fast muscle fibres of antagonistic muscles contract alternately during stepping, while co-contraction of the slow muscle fibres takes place during steady state; (3) there are several possible ways of transition between movement and steady state of the leg achieved by varying the time course of recruitment of the fibres in the participating muscles.
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Affiliation(s)
- Tibor Istvan Toth
- Emmy Noether Research Group of Computational Biology, Department of Animal Physiology, University of Cologne, Cologne, Germany
| | - Joachim Schmidt
- Department of Animal Physiology, University of Cologne, Cologne, Germany
| | - Ansgar Büschges
- Department of Animal Physiology, University of Cologne, Cologne, Germany
| | - Silvia Daun-Gruhn
- Emmy Noether Research Group of Computational Biology, Department of Animal Physiology, University of Cologne, Cologne, Germany
- * E-mail:
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