Dr. Xiaofeng Xiong

Group(s): Neural Control and Robotics
Email:
Xiaofeng.Xiong@phys.uni-goettingen.de

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    Year
    Title
    Journal / Proceedings / Book
    Xiong, X. and Wörgötter, F. and Manoonpong, P. (2013).
    A Simplified Variable Admittance Controller Based on a Virtual Agonist-Antagonist Mechanism for Robot Joint Control. Proc. Intl Conf. on Climbing and Walking Robots CLAWAR 2013, 281-288.
    BibTeX:
    @inproceedings{xiongwoergoettermanoonpong2013a,
      author = {Xiong, X. and Wörgötter, F. and Manoonpong, P.},
      title = {A Simplified Variable Admittance Controller Based on a Virtual Agonist-Antagonist Mechanism for Robot Joint Control},
      pages = {281-288},
      booktitle = {Proc. Intl Conf. on Climbing and Walking Robots CLAWAR 2013},
      year = {2013},
      month = {July},
      abstract = {Physiological studies suggest that the integration of neural circuits and biomechanics (e.g., muscles) is a key for animals to achieve robust and efficient locomotion over challenging surfaces. Inspired by these studies, we present a neuromechanical controller of a hexapod robot for walking on soft elastic and loose surfaces. It consists of a modular neural network (MNN) and virtual agonist-antagonist mechanisms (VAAM, i.e., a muscle model). The MNN coordinates 18 joints and generates basic locomotion while variable joint compliance for walking on different surfaces is achieved by the VAAM. The changeable compliance of each joint does not depend on physical compliant mechanisms or joint torque sensing. Instead, the compliance is altered by two internal parameters of the VAAM. The performance of the controller is tested on a physical hexapod robot for walking on soft elastic (e.g., sponge) and loose (e.g., gravel and snow) surfaces. The experimental results show that the controller enables the hexapod robot to achieve variably compliant leg behaviors, thereby leading to more energy-efficient locomotion on different surfaces. In addition, a finding of the experiments complies with the finding of physiological experiments on cockroach locomotion on soft elastic surfaces,}}
    Abstract: Physiological studies suggest that the integration of neural circuits and biomechanics (e.g., muscles) is a key for animals to achieve robust and efficient locomotion over challenging surfaces. Inspired by these studies, we present a neuromechanical controller of a hexapod robot for walking on soft elastic and loose surfaces. It consists of a modular neural network (MNN) and virtual agonist-antagonist mechanisms (VAAM, i.e., a muscle model). The MNN coordinates 18 joints and generates basic locomotion while variable joint compliance for walking on different surfaces is achieved by the VAAM. The changeable compliance of each joint does not depend on physical compliant mechanisms or joint torque sensing. Instead, the compliance is altered by two internal parameters of the VAAM. The performance of the controller is tested on a physical hexapod robot for walking on soft elastic (e.g., sponge) and loose (e.g., gravel and snow) surfaces. The experimental results show that the controller enables the hexapod robot to achieve variably compliant leg behaviors, thereby leading to more energy-efficient locomotion on different surfaces. In addition, a finding of the experiments complies with the finding of physiological experiments on cockroach locomotion on soft elastic surfaces,
    Review:
    Xiong, X. and Wörgötter, F. and Manoonpong, P. (2013).
    A Neuromechanical Controller of a Hexapod Robot for Walking on Sponge, Gravel and Snow Surfaces. Advances in Artificial Life. Proceedings of the 11th European Conference on Artificial Life ECAL, 989-996.
    BibTeX:
    @inproceedings{xiongwoergoettermanoonpong2013,
      author = {Xiong, X. and Wörgötter, F. and Manoonpong, P.},
      title = {A Neuromechanical Controller of a Hexapod Robot for Walking on Sponge, Gravel and Snow Surfaces},
      pages = {989-996},
      booktitle = {Advances in Artificial Life. Proceedings of the 11th European Conference on Artificial Life ECAL},
      year = {2013},
      editor = {Pietro Lio, Orazio Miglino, Giuseppe Nicosia, Stefano Nolfi and Mario Pavone},
      location = {Taormina (Italy)},
      month = {September 2-6},
      publisher = {MIT Press, Cambridge, MA},
      abstract = {Physiological studies suggest that the integration of neural circuits and biomechanics (e.g., muscles) is a key for animals to achieve robust and efficient locomotion over challenging surfaces. Inspired by these studies, we present a neuromechanical controller of a hexapod robot for walking on soft elastic and loose surfaces. It consists of a modular neural network (MNN) and virtual agonist-antagonist mechanisms (VAAM, i.e., a muscle model). The MNN coordinates 18 joints and generates basic locomotion while variable joint compliance for walking on different surfaces is achieved by the VAAM. The changeable compliance of each joint does not depend on physical compliant mechanisms or joint torque sensing. Instead, the compliance is altered by two internal parameters of the VAAM. The performance of the controller is tested on a physical hexapod robot for walking on soft elastic (e.g., sponge) and loose (e.g., gravel and snow) surfaces. The experimental results show that the controller enables the hexapod robot to achieve variably compliant leg behaviors, thereby leading to more energy-efficient locomotion on different surfaces. In addition, a finding of the experiments complies with the finding of physiological experiments on cockroach locomotion on soft elastic surfaces.}}
    Abstract: Physiological studies suggest that the integration of neural circuits and biomechanics (e.g., muscles) is a key for animals to achieve robust and efficient locomotion over challenging surfaces. Inspired by these studies, we present a neuromechanical controller of a hexapod robot for walking on soft elastic and loose surfaces. It consists of a modular neural network (MNN) and virtual agonist-antagonist mechanisms (VAAM, i.e., a muscle model). The MNN coordinates 18 joints and generates basic locomotion while variable joint compliance for walking on different surfaces is achieved by the VAAM. The changeable compliance of each joint does not depend on physical compliant mechanisms or joint torque sensing. Instead, the compliance is altered by two internal parameters of the VAAM. The performance of the controller is tested on a physical hexapod robot for walking on soft elastic (e.g., sponge) and loose (e.g., gravel and snow) surfaces. The experimental results show that the controller enables the hexapod robot to achieve variably compliant leg behaviors, thereby leading to more energy-efficient locomotion on different surfaces. In addition, a finding of the experiments complies with the finding of physiological experiments on cockroach locomotion on soft elastic surfaces.
    Review:
    Xiong, X. and Wörgötter, F. and Manoonpong, P. (2012).
    An Adaptive Neuromechanical Model for Muscle Impedance Modulations of Legged Robots. International Conference on Dynamic Walking 2012, 1-3.
    BibTeX:
    @conference{xiongwoergoettermanoonpong2012,
      author = {Xiong, X. and Wörgötter, F. and Manoonpong, P.},
      title = {An Adaptive Neuromechanical Model for Muscle Impedance Modulations of Legged Robots},
      pages = {1-3},
      booktitle = {International Conference on Dynamic Walking 2012},
      year = {2012},
      month = {05},
      url = {http://www.bccn-goettingen.de/Publications/articlereference.2012-06-13.4632442521},
      abstract = {Recently, an integrative view of neural circuits and mechanical components has been developed by neuroscientists and biomechanicians 11, 8. This view argues that mechanical components cannot be isolated from neural circuits in the context of substantially perturbed locomotion. Note that mechanical passive walkers with no neural circuits only show stable locomotion on flat terrain or small slopes 2. The argument of the integrative view has been supported by a cockroach experiment, which has demonstrated that more modulations of neural activities are detected when cockroaches run over a highly complex terrain with larger obstacles (more than three times cockroach hip height). Normally, cockroaches are able to solely rely on passive mechanical properties for rapid stabilization while confronted with moderate obstacles (less than three times cockroach hip height) 10. In addition, neural circuits and leg muscle activities tend to be entrained by mechanical feedback 11, 12, 14. Besides, it is well known that neural activities modulate muscle impedance such as stiffness and damping 7, 9, 15, such modulations can be utilized for stabilization in posture and locomotion 3.}}
    Abstract: Recently, an integrative view of neural circuits and mechanical components has been developed by neuroscientists and biomechanicians 11, 8. This view argues that mechanical components cannot be isolated from neural circuits in the context of substantially perturbed locomotion. Note that mechanical passive walkers with no neural circuits only show stable locomotion on flat terrain or small slopes 2. The argument of the integrative view has been supported by a cockroach experiment, which has demonstrated that more modulations of neural activities are detected when cockroaches run over a highly complex terrain with larger obstacles (more than three times cockroach hip height). Normally, cockroaches are able to solely rely on passive mechanical properties for rapid stabilization while confronted with moderate obstacles (less than three times cockroach hip height) 10. In addition, neural circuits and leg muscle activities tend to be entrained by mechanical feedback 11, 12, 14. Besides, it is well known that neural activities modulate muscle impedance such as stiffness and damping 7, 9, 15, such modulations can be utilized for stabilization in posture and locomotion 3.
    Review:
    Xiong, X. and Wörgötter, F. and Manoonpong, P. (2014).
    Virtual Agonist-antagonist Mechanisms Produce Biological Muscle-like Functions: An Application for Robot Joint Control. Industrial Robot: An International Journal, 340 - 346, 41, 4. DOI: 10.1108/IR-11-2013-421.
    BibTeX:
    @article{xiongwoergoettermanoonpong2014,
      author = {Xiong, X. and Wörgötter, F. and Manoonpong, P.},
      title = {Virtual Agonist-antagonist Mechanisms Produce Biological Muscle-like Functions: An Application for Robot Joint Control},
      pages = {340 - 346},
      journal = {Industrial Robot: An International Journal},
      year = {2014},
      volume= {41},
      number = {4},
      publisher = {Emerald Group Publishing Ltd.},
      url = {http://www.emeraldinsight.com/doi/abs/10.1108/IR-11-2013-421},
      doi = {10.1108/IR-11-2013-421},
      abstract = {Purpose - Biological muscles of animals have a surprising variety of functions, i.e., struts, springs, and brakes. According to this, the purpose of this paper is to apply virtual agonist-antagonist mechanisms to robot joint control allowing for muscle-like functions and variably compliant joint motions. Design/methodology/approach - Each joint is driven by a pair of virtual agonist-antagonist mechanism (VAAM, 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, e.g., 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 DOF 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).}}
    Abstract: Purpose - Biological muscles of animals have a surprising variety of functions, i.e., struts, springs, and brakes. According to this, the purpose of this paper is to apply virtual agonist-antagonist mechanisms to robot joint control allowing for muscle-like functions and variably compliant joint motions. Design/methodology/approach - Each joint is driven by a pair of virtual agonist-antagonist mechanism (VAAM, 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, e.g., 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 DOF 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).
    Review:
    Xiong, X. and Wörgötter, F. and Manoonpong, P. (2014).
    Neuromechanical control for hexapedal robot walking on challenging surfaces and surface classification. Robotics and Autonomous Systems, 1777 - 1789, 62, 12. DOI: 10.1016/j.robot.2014.07.008.
    BibTeX:
    @article{xiongwoergoettermanoonpong2014a,
      author = {Xiong, X. and Wörgötter, F. and Manoonpong, P.},
      title = {Neuromechanical control for hexapedal robot walking on challenging surfaces and surface classification},
      pages = {1777 - 1789},
      journal = {Robotics and Autonomous Systems},
      year = {2014},
      volume= {62},
      number = {12},
      url = {http://www.sciencedirect.com/science/article/pii/S0921889014001353},
      doi = {10.1016/j.robot.2014.07.008},
      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.}}
    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.
    Review:

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