Xiong, Xiaofeng

[["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.firstpage","2521"],["dc.bibliographiccitation.issue","11"],["dc.bibliographiccitation.journal","IEEE Transactions on Cybernetics"],["dc.bibliographiccitation.lastpage","2534"],["dc.bibliographiccitation.volume","46"],["dc.contributor.author","Xiong, Xiaofeng"],["dc.contributor.author","Manoonpong, Poramate"],["dc.contributor.author","Wörgötter, Florentin"],["dc.date.accessioned","2020-12-10T18:26:17Z"],["dc.date.available","2020-12-10T18:26:17Z"],["dc.date.issued","2016"],["dc.description.abstract","The control of multilegged animal walking is a neuromechanical process, and to achieve this in an adaptive and energy efficient way is a difficult and challenging problem. This is due to the fact that this process needs in real time: 1) to coordinate very many degrees of freedom of jointed legs; 2) to generate the proper leg stiffness (i.e., compliance); and 3) to determine joint angles that give rise to particular positions at the endpoints of the legs. To tackle this problem for a robotic application, here we present a neuromechanical controller coupled with sensorimotor learning. The controller consists of a modular neural network for coordinating 18 joints and several virtual agonist-antagonist muscle mechanisms (VAAMs) for variable compliant joint motions. In addition, sensorimotor learning, including forward models and dual-rate learning processes, is introduced for predicting foot force feedback and for online tuning the VAAMs' stiffness parameters. The control and learning mechanisms enable the hexapod robot advanced mobility sensor driven-walking device (AMOS) to achieve variable compliant walking that accommodates different gaits and surfaces. As a consequence, AMOS can perform more energy efficient walking, compared to other small legged robots. In addition, this paper also shows that the tight combination of neural control with tunable muscle-like functions, guided by sensory feedback and coupled with sensorimotor learning, is a way forward to better understand and solve adaptive coordination problems in multilegged locomotion."],["dc.identifier.doi","10.1109/TCYB.2015.2479237"],["dc.identifier.eissn","2168-2275"],["dc.identifier.fs","622745"],["dc.identifier.issn","2168-2267"],["dc.identifier.pmid","26441437"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/76023"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.status","final"],["dc.relation.eissn","2168-2275"],["dc.relation.issn","2168-2267"],["dc.title","Adaptive and Energy Efficient Walking in a Hexapod Robot Under Neuromechanical Control and Sensorimotor Learning"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","unknown"],["dspace.entity.type","Publication"]]