Manoonpong, Poramate

[["dc.bibliographiccitation.artnumber","39455"],["dc.bibliographiccitation.journal","Scientific Reports"],["dc.bibliographiccitation.volume","6"],["dc.contributor.author","Manoonpong, Poramate"],["dc.contributor.author","Petersen, Dennis"],["dc.contributor.author","Kovalev, Alexander"],["dc.contributor.author","Woergoetter, Florentin"],["dc.contributor.author","Gorb, Stanislav N."],["dc.contributor.author","Spinner, Marlene"],["dc.contributor.author","Heepe, Lars"],["dc.date.accessioned","2018-11-07T10:04:23Z"],["dc.date.available","2018-11-07T10:04:23Z"],["dc.date.issued","2016"],["dc.description.abstract","Based on the principles of morphological computation, we propose a novel approach that exploits the interaction between a passive anisotropic scale-like material (e.g., shark skin) and a non-smooth substrate to enhance locomotion efficiency of a robot walking on inclines. Real robot experiments show that passive tribologically-enhanced surfaces of the robot belly or foot allow the robot to grip on specific surfaces and move effectively with reduced energy consumption. Supplementing the robot experiments, we investigated tribological properties of the shark skin as well as its mechanical stability. It shows high frictional anisotropy due to an array of sloped denticles. The orientation of the denticles to the underlying collagenous material also strongly influences their mechanical interlocking with the substrate. This study not only opens up a new way of achieving energy-efficient legged robot locomotion but also provides a better understanding of the functionalities and mechanical properties of anisotropic surfaces. That understanding will assist developing new types of material for other real-world applications."],["dc.identifier.doi","10.1038/srep39455"],["dc.identifier.isi","000390523500001"],["dc.identifier.pmid","28008936"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14233"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/38687"],["dc.notes.intern","Merged from goescholar"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Nature Publishing Group"],["dc.relation.issn","2045-2322"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Enhanced Locomotion Efficiency of a Bio-inspired Walking Robot using Contact Surfaces with Frictional Anisotropy"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1777"],["dc.bibliographiccitation.issue","12"],["dc.bibliographiccitation.journal","Robotics and Autonomous Systems"],["dc.bibliographiccitation.lastpage","1789"],["dc.bibliographiccitation.volume","62"],["dc.contributor.author","Xiong, Xiaofeng"],["dc.contributor.author","Woergoetter, Florentin"],["dc.contributor.author","Manoonpong, Poramate"],["dc.date.accessioned","2018-11-07T09:32:21Z"],["dc.date.available","2018-11-07T09:32:21Z"],["dc.date.issued","2014"],["dc.description.abstract","The neuromechanical control principles of animal locomotion provide good insights for the development of bio-inspired legged robots for walking on challenging surfaces. Based on such principles, we developed a neuromechanical controller consisting of a modular neural network (MNN) and of virtual agonist-antagonist muscle mechanisms (VAAMs). The controller allows for variable compliant leg motions of a hexapod robot, thereby leading to energy-efficient walking on different surfaces. Without any passive mechanisms or torque and position feedback at each joint, the variable compliant leg motions are achieved by only changing the stiffness parameters of the VAAMs. In addition, six surfaces can be also classified by observing the motor signals generated by the controller. The performance of the controller is tested on a physical hexapod robot. Experimental results show that it can effectively walk on six different surfaces with the specific resistances between 9.1 and 25.0, and also classify them with high accuracy. (C) 2014 Elsevier B.V. All rights reserved."],["dc.identifier.doi","10.1016/j.robot.2014.07.008"],["dc.identifier.isi","000344131000009"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/31740"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Elsevier Science Bv"],["dc.relation.issn","1872-793X"],["dc.relation.issn","0921-8890"],["dc.title","Neuromechanical control for hexapedal robot walking on challenging surfaces and surface classification"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","340"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Industrial Robot An International Journal"],["dc.bibliographiccitation.lastpage","346"],["dc.bibliographiccitation.volume","41"],["dc.contributor.author","Xiong, Xiaofeng"],["dc.contributor.author","Woergoetter, Florentin"],["dc.contributor.author","Manoonpong, Poramate"],["dc.date.accessioned","2018-11-07T09:45:50Z"],["dc.date.available","2018-11-07T09:45:50Z"],["dc.date.issued","2014"],["dc.description.abstract","Purpose - The purpose of this paper is to apply virtual agonist-antagonist mechanisms (VAAMs) to robot joint control allowing for muscle-like functions and variably compliant joint motions. Biological muscles of animals have a surprising variety of functions, i.e. struts, springs and brakes. Design/methodology/approach - Each joint is driven by a pair of VAAMs (i.e. passive components). The muscle-like functions as well as the variable joint compliance are simply achieved by tuning the damping coefficient of the VAAM. Findings - With the VAAM, variably compliant joint motions can be produced without mechanically bulky and complex mechanisms or complex force/toque sensing at each joint. Moreover, through tuning the damping coefficient of the VAAM, the functions of the VAAM are comparable to biological muscles. Originality/value - The model (i. e. VAAM) provides a way forward to emulate muscle-like functions that are comparable to those found in physiological experiments of biological muscles. Based on these muscle-like functions, the robotic joints can easily achieve variable compliance that does not require complex physical components or torque sensing systems, thereby capable of implementing the model on small-legged robots driven by, for example, standard servo motors. Thus, the VAAM minimizes hardware and reduces system complexity. From this point of view, the model opens up another way of simulating muscle behaviors on artificial machines. Executive summary - The VAAM can be applied to produce variable compliant motions of a high degree-of-freedom robot. Only relying on force sensing at the end effector, this application is easily achieved by changing coefficients of the VAAM. Therefore, the VAAM can reduce economic cost on mechanical and sensing components of the robot, compared to traditional methods (e. g. artificial muscles)."],["dc.identifier.doi","10.1108/IR-11-2013-421"],["dc.identifier.isi","000341784300003"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/34720"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Emerald Group Publishing Limited"],["dc.relation.issn","1758-5791"],["dc.relation.issn","0143-991X"],["dc.title","Virtual agonist-antagonist mechanisms produce biological muscle-like functions An application for robot joint control"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","10"],["dc.bibliographiccitation.journal","Frontiers in Neurorobotics"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Dasgupta, Sakyasingha"],["dc.contributor.author","Goldschmidt, Dennis"],["dc.contributor.author","Woergoetter, Florentin"],["dc.contributor.author","Manoonpong, Poramate"],["dc.date.accessioned","2018-11-07T09:51:28Z"],["dc.date.available","2018-11-07T09:51:28Z"],["dc.date.issued","2015"],["dc.description.abstract","Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated movements, (2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and (3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex locomotive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps, leg damage adaptations, as well as climbing over high obstacles. Furthermore, we demonstrate that the newly developed recurrent network based approach to online forward models outperforms the adaptive neuron forward models, which have hitherto been the state of the art, to model a subset of similar walking behaviors in walking robots."],["dc.identifier.doi","10.3389/fnbot.2015.00010"],["dc.identifier.isi","000370403400001"],["dc.identifier.pmid","26441629"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13198"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/35924"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Frontiers Media Sa"],["dc.relation.issn","1662-5218"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Distributed recurrent neural forward models with synaptic adaptation and CPG-based control for complex behaviors of walking robots"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","389"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","Applied Physics A"],["dc.bibliographiccitation.volume","127"],["dc.contributor.author","Tramsen, Halvor T."],["dc.contributor.author","Heepe, Lars"],["dc.contributor.author","Homchanthanakul, Jettanan"],["dc.contributor.author","Wörgötter, Florentin"],["dc.contributor.author","Gorb, Stanislav N."],["dc.contributor.author","Manoonpong, Poramate"],["dc.date.accessioned","2022-02-01T10:31:48Z"],["dc.date.available","2022-02-01T10:31:48Z"],["dc.date.issued","2021"],["dc.description.abstract","Abstract Legged locomotion of robots can be greatly improved by bioinspired tribological structures and by applying the principles of computational morphology to achieve fast and energy-efficient walking. In a previous research, we mounted shark skin on the belly of a hexapod robot to show that the passive anisotropic friction properties of this structure enhance locomotion efficiency, resulting in a stronger grip on varying walking surfaces. This study builds upon these results by using a previously investigated sawtooth structure as a model surface on a legged robot to systematically examine the influences of different material and surface properties on the resulting friction coefficients and the walking behavior of the robot. By employing different surfaces and by varying the stiffness and orientation of the anisotropic structures, we conclude that with having prior knowledge about the walking environment in combination with the tribological properties of these structures, we can greatly improve the robot’s locomotion efficiency."],["dc.description.abstract","Abstract Legged locomotion of robots can be greatly improved by bioinspired tribological structures and by applying the principles of computational morphology to achieve fast and energy-efficient walking. In a previous research, we mounted shark skin on the belly of a hexapod robot to show that the passive anisotropic friction properties of this structure enhance locomotion efficiency, resulting in a stronger grip on varying walking surfaces. This study builds upon these results by using a previously investigated sawtooth structure as a model surface on a legged robot to systematically examine the influences of different material and surface properties on the resulting friction coefficients and the walking behavior of the robot. By employing different surfaces and by varying the stiffness and orientation of the anisotropic structures, we conclude that with having prior knowledge about the walking environment in combination with the tribological properties of these structures, we can greatly improve the robot’s locomotion efficiency."],["dc.identifier.doi","10.1007/s00339-021-04443-7"],["dc.identifier.pii","4443"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/98950"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-517"],["dc.relation.eissn","1432-0630"],["dc.relation.issn","0947-8396"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Getting grip in changing environments: the effect of friction anisotropy inversion on robot locomotion"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Frontiers in Neurorobotics"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Manoonpong, Poramate"],["dc.contributor.author","Tetzlaff, Christian"],["dc.date.accessioned","2020-12-10T18:44:30Z"],["dc.date.available","2020-12-10T18:44:30Z"],["dc.date.issued","2018"],["dc.identifier.doi","10.3389/fnbot.2018.00053"],["dc.identifier.eissn","1662-5218"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/78482"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Editorial: Neural Computation in Embodied Closed-Loop Systems for the Generation of Complex Behavior: From Biology to Technology"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","editorial_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","301"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","The International Journal of Robotics Research"],["dc.bibliographiccitation.lastpage","331"],["dc.bibliographiccitation.volume","26"],["dc.contributor.author","Manoonpong, Poramate"],["dc.contributor.author","Pasemann, Frank"],["dc.contributor.author","Roth, Hubert"],["dc.date.accessioned","2018-11-07T11:04:30Z"],["dc.date.available","2018-11-07T11:04:30Z"],["dc.date.issued","2007"],["dc.description.abstract","A neurocontroller is described which generates the basic locomotion and controls the sensor-driven behavior of a four-legged and a six-legged walking machine. The controller utilizes discrete-time neuro-dynamics, and is of modular structure. One module is for processing sensor signals, one is a neural oscillator network serving as a central pattern generator and the third one is a so-called velocity regulating network. These modules are small and their structures and their functionalities are analyzable. In combination, they enable the machines to autonomously explore an unknown environment, to avoid obstacles, and to escape from corners or deadlock situations. The neurocontroller was developed and tested first using a physical simulation environment, and then it was successfully transferred to the physical walking machines. Locomotion is based on a gait where the diagonal legs are paired and move together e.g. trot gait for the four-legged walking machine and tripod gait for the six-legged walking machine. The controller developed is universal in the sense that it can easily be adapted to different types of even-legged walking machines without changing the internal structure and its parameters."],["dc.identifier.doi","10.1177/0278364906076263"],["dc.identifier.isi","000245177500005"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13000"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/51858"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Sage Publications Ltd"],["dc.relation.issn","1741-3176"],["dc.relation.issn","0278-3649"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Modular reactive neurocontrol for biologically inspired walking machines"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","265"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Robotics and Autonomous Systems"],["dc.bibliographiccitation.lastpage","288"],["dc.bibliographiccitation.volume","56"],["dc.contributor.author","Manoonpong, Poramate"],["dc.contributor.author","Pasemann, Frank"],["dc.contributor.author","Woergoetter, Florentin"],["dc.date.accessioned","2018-11-07T11:17:02Z"],["dc.date.available","2018-11-07T11:17:02Z"],["dc.date.issued","2008"],["dc.description.abstract","This article describes modular neural control structures for different walking machines utilizing discrete-time neurodynamics. A simple neural oscillator network serves as a central pattern generator producing the basic rhythmic leg movements. Other modules, like the velocity regulating and the phase switching networks, enable the machines to perform onmidirectional walking as well as reactive behaviors, like obstacle avoidance and different types of tropisms. These behaviors are generated in a sensori-motor loop with respect to appropriate sensor inputs, to which a neural preprocessing is applied. The neuromodules presented are small so that their structure-function relationship can be analysed. The complete controller is general in the sense that it can be easily adapted to different types of even-legged walking machines without changing its internal structure and parameters. (c) 2007 Elsevier B.V. All rights reserved."],["dc.identifier.doi","10.1016/j.robot.2007.07.004"],["dc.identifier.isi","000254444100005"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/54720"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Elsevier Science Bv"],["dc.relation.issn","0921-8890"],["dc.title","Sensor-driven neural control for omnidirectional locomotion and versatile reactive behaviors of walking machines"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","20"],["dc.bibliographiccitation.journal","Frontiers in neurorobotics"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Goldschmidt, Dennis"],["dc.contributor.author","Manoonpong, Poramate"],["dc.contributor.author","Dasgupta, Sakyasingha"],["dc.date.accessioned","2019-07-09T11:44:54Z"],["dc.date.available","2019-07-09T11:44:54Z"],["dc.date.issued","2017"],["dc.description.abstract","Despite their small size, insect brains are able to produce robust and efficient navigation in complex environments. Specifically in social insects, such as ants and bees, these navigational capabilities are guided by orientation directing vectors generated by a process called path integration. During this process, they integrate compass and odometric cues to estimate their current location as a vector, called the home vector for guiding them back home on a straight path. They further acquire and retrieve path integration-based vector memories globally to the nest or based on visual landmarks. Although existing computational models reproduced similar behaviors, a neurocomputational model of vector navigation including the acquisition of vector representations has not been described before. Here we present a model of neural mechanisms in a modular closed-loop control-enabling vector navigation in artificial agents. The model consists of a path integration mechanism, reward-modulated global learning, random search, and action selection. The path integration mechanism integrates compass and odometric cues to compute a vectorial representation of the agent's current location as neural activity patterns in circular arrays. A reward-modulated learning rule enables the acquisition of vector memories by associating the local food reward with the path integration state. A motor output is computed based on the combination of vector memories and random exploration. In simulation, we show that the neural mechanisms enable robust homing and localization, even in the presence of external sensory noise. The proposed learning rules lead to goal-directed navigation and route formation performed under realistic conditions. Consequently, we provide a novel approach for vector learning and navigation in a simulated, situated agent linking behavioral observations to their possible underlying neural substrates."],["dc.identifier.doi","10.3389/fnbot.2017.00020"],["dc.identifier.pmid","28446872"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14958"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59123"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.publisher","Frontiers Media S.A."],["dc.relation","info:eu-repo/grantAgreement/EC/H2020/732266/EU//Plan4Act"],["dc.relation.eissn","1662-5218"],["dc.relation.issn","1662-5218"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","006"],["dc.subject.ddc","573"],["dc.subject.ddc","612"],["dc.title","A Neurocomputational Model of Goal-Directed Navigation in Insect-Inspired Artificial Agents."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","12"],["dc.bibliographiccitation.journal","Frontiers in Neural Circuits"],["dc.bibliographiccitation.volume","7"],["dc.contributor.author","Manoonpong, Poramate"],["dc.contributor.author","Parlitz, Ulrich"],["dc.contributor.author","Woergoetter, Florentin"],["dc.date.accessioned","2018-11-07T09:28:08Z"],["dc.date.available","2018-11-07T09:28:08Z"],["dc.date.issued","2013"],["dc.description.abstract","Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs) and sensory feedback (afferent-based control) but also on internal forward models (efference copies). They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines."],["dc.identifier.doi","10.3389/fncir.2013.00012"],["dc.identifier.fs","604443"],["dc.identifier.isi","000314839800001"],["dc.identifier.pmid","23408775"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10221"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/30705"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Frontiers Research Foundation"],["dc.relation","info:eu-repo/grantAgreement/EC/FP7/270273/EU//Xperience"],["dc.relation.issn","1662-5110"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 3.0"],["dc.title","Neural control and adaptive neural forward models for insect-like, energy-efficient, and adaptable locomotion of walking machines"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]