Abstract
The act of assisted feeding is a challenging task that requires a good reactive planning strategy to cope with an unpredictable environment. It can be seen as a tracking task, where some end effector must travel to a moving goal. This work builds upon state of the art algorithms, such as Discriminative Optimization, making use of a Kinect camera and a modular robotic arm to implement a closed form system that performs assisted feeding. It presents two different approaches: the use of a variable rate function for updating the trajectory with information on the moving goal, and the definition of different risk regions that will shape a safer trajectory.
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References
Boucher, F.: From need to innovation [industrial activities]. IEEE Robot. Autom. Mag. 22(3), 18–19 (2015)
Meng, J., et al.: Noninvasive electroencephalogram based control of a robotic arm for reach and grasp tasks. Sci. Rep. 6 (2016)
Fabrizio, F., De Luca, A.: Real-time computation of distance to dynamic obstacles with multiple depth sensors. IEEE Robot. Autom. Lett. 2(1), 56–63 (2017)
Yoshida, E., Yokoi, K., Gergondet, P.: Online replanning for reactive robot motion: practical aspects. In: IEEE/RSJ IROS, pp. 5927–5933 (2010)
Kunz, T., Reiser, U., Stilman, M., Verl, A.: Real-time path planning for a robot arm in changing environments. In: IEEE/RSJ IROS, pp. 5906–5911 (2010)
Vongkulbhisal, J., De la Torre, F., Costeira, J.P.: Discriminative optimization: theory and applications to point cloud registration. In: IEEE CVPR (2017)
Tondu, B., Bazaz, S.A.: The three-cubic method: an optimal online robot joint trajectory generator under velocity, acceleration, and wandering constraints. Int. J. Robot. Res. 18(9), 893–901 (1999)
Flash, T., Hogan, N.: The coordination of arm movements: an experimentally confirmed mathematical model. J. Neurosci. 5(7), 1688–1703 (1985)
Garrido-Jurado, S., Muñoz-Salinas, R., Madrid-Cuevas, F.J., Marín-Jiménez, M.J.: Automatic generation and detection of highly reliable fiducial markers under occlusion. Pattern Recogn. 47(6), 2280–2292 (2014)
Hebi API. http://hebirobotics.com/matlab/. Accessed 16 Mar 2017
Xiang, L., et al.: libfreenect2: Release 0.2 (2016). https://doi.org/10.5281/zenodo.50641
Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)
Gold, S., Rangarajan, A., Lu, C., Mjolsness, E.: New algorithms for 2D and 3D point matching: pose estimation and correspondence. Pattern Recogn. 31, 957–964 (1997)
Pérez-D’Arpino, C., Shah, J.A.: Fast target prediction of human reaching motion for cooperative human-robot manipulation tasks using time series classification. In: IEEE ICRA, pp. 6175–6182 (2015)
Acknowledgements
This work benefited greatly from professor Howie Choset’s support, who provided access to the Hebi Modules used in this work. We also thank our colleagues from the biorobotics lab at CMU who provided valuable insights and expertise on these modules. This research was supported in part by Fundação para a Ciencia e a Tecnologia [UID/EEA/50009/2013]. Manuel Marques is partially supported by FCT project [IF/00879/2012].
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Silva, C., Vongkulbhisal, J., Marques, M., Costeira, J.P., Veloso, M. (2017). Feedbot - A Robotic Arm for Autonomous Assisted Feeding. In: Oliveira, E., Gama, J., Vale, Z., Lopes Cardoso, H. (eds) Progress in Artificial Intelligence. EPIA 2017. Lecture Notes in Computer Science(), vol 10423. Springer, Cham. https://doi.org/10.1007/978-3-319-65340-2_40
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DOI: https://doi.org/10.1007/978-3-319-65340-2_40
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