Using robot operating system (ROS) and single board computer to control bioloid robot motion

Conference paper

Kalyani, G., Yang, Z., Gandhi, V. and Geng, T. 2017. Using robot operating system (ROS) and single board computer to control bioloid robot motion. 18th Towards Autonomous Robotic Systems (TAROS) Conference. Guildford, Surrey, UK 19 - 21 Jul 2017 Springer. pp. 41-50 https://doi.org/10.1007/978-3-319-64107-2_4
TypeConference paper
TitleUsing robot operating system (ROS) and single board computer to control bioloid robot motion
AuthorsKalyani, G., Yang, Z., Gandhi, V. and Geng, T.
Abstract

This paper presents a research study on the adaptation of a novel technique for placing a programmable component over the structural component of a Robotis Bioloid humanoid robot. Assimilating intelligence plays an important role in the field of robotics that enables a computer to model or replicate some of the intelligent behaviors of human beings but with minimal human intervention. As a part of this effort, this paper revises the Bioloid robot structure so as to be able to control the robotic movement via a single board computer Beaglebone Black (BBB) and Robot operating system (ROS). ROS as the development frame work in conjunction with the main BBB controller that integrates robotic functions is an important aspect of this research, and is a first of its kind approach. A full ROS computation has been developed by which an API that will be usable by high level software using ROS services has also been developed. The human like body structure of the Bioloid robot and BeagleBone Black running ROS along with the intellectual components are used to make the robot walk efficiently.

Conference18th Towards Autonomous Robotic Systems (TAROS) Conference
Page range41-50
ISSN0302-9743
ISBN
Hardcover9783319641065
PublisherSpringer
Publication dates
PrintJul 2017
Publication process dates
Deposited15 Aug 2017
Accepted01 May 2017
Output statusPublished
Accepted author manuscript
First submitted version
Copyright Statement

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-64107-2_4

Additional information

Paper published as: Kalyani G.K., Yang Z., Gandhi V., Geng T. (2017) Using Robot Operating System (ROS) and Single Board Computer to Control Bioloid Robot Motion. In: Gao Y., Fallah S., Jin Y., Lekakou C. (eds) Towards Autonomous Robotic Systems. TAROS 2017. Lecture Notes in Computer Science, vol 10454. Springer, Cham

Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-319-64107-2_4
LanguageEnglish
Book titleTowards Autonomous Robotic Systems: 18th Annual Conference, TAROS 2017, Guildford, UK, July 19–21, 2017, Proceedings
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A neuromorphic depth-from-motion vision model with STDP adaptation
Yang, Z., Murray, A., Worgotter, F., Cameron, K. and Boonsobhak, V. 2006. A neuromorphic depth-from-motion vision model with STDP adaptation. IEEE Transactions on Neural Networks. 17 (2), pp. 482-495. https://doi.org/10.1109/TNN.2006.871711
A generalized locomotion CPG architecture based on oscillatory building blocks
Yang, Z. and Franca, F. 2003. A generalized locomotion CPG architecture based on oscillatory building blocks. Biological Cybernetics. 89 (1), pp. 34-42. https://doi.org/10.1007/s00422-003-0409-7
Intelligent adaptive user interfaces for BCI based robotic control
Gandhi, V., Prasad, G., McGinnity, M., Coyle, D. and Behera, L. 2013. Intelligent adaptive user interfaces for BCI based robotic control. BCI meeting. USA Graz University of Technology Publishing House. https://doi.org/10.3217/978-3-85125-260-6-130
Quantum neural network-based EEG filtering for a brain-computer interface
Gandhi, V., Prasad, G., Coyle, D., Behera, L. and McGinnity, T. 2013. Quantum neural network-based EEG filtering for a brain-computer interface. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2013.2274436
EEG filtering with quantum neural networks for a Brain-Computer Interface (BCI)
Gandhi, V., Prasad, G., Coyle, D., Behera, L. and McGinnity, T. 2012. EEG filtering with quantum neural networks for a Brain-Computer Interface (BCI). Young researchers futures meeting: Neural engineering. University of Warwick 19 - 21 Sep 2012 pp. 21
Characterisation of information flow in an izhikevich network
Guo, L., Yang, Z., Graham, B. and Zhang, D. 2012. Characterisation of information flow in an izhikevich network. Huang, T. (ed.) ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I. Doha, Qatar 12 - 16 Nov 2012 Berlin Springer. https://doi.org/10.1007/978-3-642-34475-6_47
A mixture model classifier and its application on the biomedical time series
Yang, Z., Yang, Z., Eftestol, T., Steen, P., Lu, W. and Harrison, R. 2012. A mixture model classifier and its application on the biomedical time series. Applied Artificial Intelligence. 26 (6), pp. 588-597. https://doi.org/10.1080/08839514.2012.687665
A novel EEG signal enhancement approach using a recurrent quantum neural network for a Brain Computer Interface
Gandhi, V., Prasad, G., Coyle, D., Behera, L. and McGinnity, M. 2011. A novel EEG signal enhancement approach using a recurrent quantum neural network for a Brain Computer Interface. Technically Assisted Rehabilitation. Berlin, Germany 17 - 18 Mar 2011
An intelligent Adaptive User Interface (iAUI) for enhancing the communication in a Brain-Computer Interface (BCI)
Gandhi, V., Prasad, G., Coyle, D., Behera, L. and McGinnity, M. 2011. An intelligent Adaptive User Interface (iAUI) for enhancing the communication in a Brain-Computer Interface (BCI). UKIERI workshop on the Fusion of Brain-Computer Interface and Assistive Robotics. University of Ulster 07 - 08 Jul 2011
EEG denoising with a recurrent quantum neural network for a brain-computer interface
Gandhi, V., Arora, V., Behera, L., Prasad, G., Coyle, D. and McGinnity, T. 2011. EEG denoising with a recurrent quantum neural network for a brain-computer interface. 2011 International Joint Conference on Neural Networks (IJCNN). San Jose, CA, USA 31 Jul - 05 Aug 2011 IEEE. pp. 1583-1590 https://doi.org/10.1109/IJCNN.2011.6033413
Dynamic control of walking leg joints: a building block model perspective
Yang, Z. 2011. Dynamic control of walking leg joints: a building block model perspective. in: Seventh International Conference on Natural Computation (ICNC), 2011 IEEE.
Interfacing a dynamic interface paradigm for multiple target selection using a two class brain-computer interface
Gandhi, V., Coyle, D., Prasad, G., Bharti, C., Behera, L. and McGinnity, M. 2009. Interfacing a dynamic interface paradigm for multiple target selection using a two class brain-computer interface. Indo-US Workshop on System of Systems Engineering. IIT Kanpur, India 26 - 28 Oct 2009 https://doi.org/10.1049/cp.2009.1690
A novel paradigm for multiple target selection using a two class brain computer interface
Gandhi, V., Prasad, G., Coyle, D., Behera, L. and McGinnity, M. 2009. A novel paradigm for multiple target selection using a two class brain computer interface. Irish Signal & Systems Conference. Dublin, Ireland 10 - 11 Jun 2009 Dublin The Institution of Engineering and Technology (IET). https://doi.org/10.1049/cp.2009.1690
A general rhythmic pattern generation architecture for legged locomotion
Yang, Z. and Franca, F. 2008. A general rhythmic pattern generation architecture for legged locomotion. in: Pazos, A., Sierra, A. and Buceta, W. (ed.) Advancing Artificial Intelligence through Biological Process Applications New York IGI Global Publisher.
An artificial early visual model adopting spike-timing-dependent plasticity
Yang, Z. and Murray, A. 2006. An artificial early visual model adopting spike-timing-dependent plasticity. Neurocomputing. 69 (16-18), pp. 1904-1911. https://doi.org/10.1016/j.neucom.2005.11.017
A probabilistic neural network as the predictive classifier of out-of-hospital defibrillation outcomes
Yang, Z., Yang, Z., Lu, W., Harrison, R., Eftestol, T. and Steen, P. 2005. A probabilistic neural network as the predictive classifier of out-of-hospital defibrillation outcomes. Resuscitation. 64 (1), pp. 31-36. https://doi.org/10.1016/j.resuscitation.2004.07.002
Building artificial CPGs with asymmetric Hopfield networks
Franca, F. and Yang, Z. 2000. Building artificial CPGs with asymmetric Hopfield networks. The IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN 2000). Como, Italy 24 - 27 Jul 2000
Generating arbitrary rhythmic patterns with purely inhibitory neural networks
Yang, Z. and Franca, F. 1998. Generating arbitrary rhythmic patterns with purely inhibitory neural networks. in: Verleysen, M. (ed.) ESANN'1998 proceedings - 6th European symposium on artificial neural networks (Bruges, 22-23-24 April 1998) D facto. pp. 53-58