Abstract
The paper proposed a new method to endow a robot with the ability of human-robot collaboration and online obstacle avoidance simultaneously. In other words, we construct a probabilistic model for human-robot collaboration primitives to learn the nonlinear correlation between human and robot joint space and Cartesian space both based on interaction trajectories from the demonstration. This multidimensional probabilistic model not only helps to infer robot collaboration motion depending on the human action by the correlation between human and robot in joint space but also convenient to conduct robot obstacle avoidance reverse kinetics from cartesian space via the correlation between them. Specifically, as for the latter, a modulation matrix is established from the obstacle form to automatically generate robot obstacle avoidance trajectory in Cartesian space. Obstacle avoidance in the human-robot collaboration experimental is investigated, and its simulation results verify the feasibility and efficiency of the algorithm.
The authors acknowledge the National Natural Science Foundation of China (61773299, 515754112), Excellent Dissertation Cultivation Funds of Wuhan University of Technology (2017-YS-067).
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Akkaladevi, S., Heindl, C., Angerer, A., Minichberger, J.: Action recognition for human robot interaction in industrial applications. In: IEEE International Conference on Computer Graphics (2016)
Wang, Z., et al.: Probabilistic movement modeling for intention inference in human–robot interaction. Int. J. Robot. Res. 32(7), 841–858 (2013)
Paraschos, A., Daniel, C., Peters, J., Neumann, G.: Probabilistic movement primitives. In: Advances in Neural Information Processing Systems, pp. 2616–2624 (2014)
Sylvain, C., Florent, G., Aude, B.: On learning, representing, and generalizing a task in a humanoid robot. IEEE Trans. Syst. Man Cybern. Part B 37(2), 286–298 (2007)
Gu, Y., Alterovitz, R.: Demonstration-Guided Motion Planning (2017)
Perez-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 International Conference on Robotics & Automation (2015)
Maeda, G.J., Neumann, G., Ewerton, M., Lioutikov, R., Kroemer, O., Peters, J.: Probabilistic movement primitives for coordination of multiple human-robot collaborative tasks. Auton. Robot 41(3), 593–612 (2017)
Ewerton, M., Maeda, G., Peters, J., Neumann, G.: Learning motor skills from partially observed movements executed at different speeds. In: IEEE/RSJ International Conference on Intelligent Robots & Systems (2015)
Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics) (2006)
Gomez-Gonzalez, S., Neumann, G., Scholkopf, B., Peters, J.: Using probabilistic movement primitives for striking movements. In: IEEE-RAS International Conference on Humanoid Robots (2017)
Amor, H.B., Neumann, G., Kamthe, S., Kroemer, O., Peters, J.: Interaction primitives for human-robot cooperation tasks. In: IEEE International Conference on Robotics & Automation (2014)
Saveriano, M., Lee, D.: Distance based dynamical system modulation for reactive avoidance of moving obstacles. In: IEEE International Conference on Robotics & Automation (2014)
Khansari-Zadeh, S.M., Billard, A.: A dynamical system approach to realtime obstacle avoidance. Auton. Robot 32(4), 433–454 (2012)
Feder, H.J.S., Slotine, J.J.E.: Real-time path planning using harmonic potentials in dynamic environments. In: IEEE International Conference on Robotics & Automation (1997)
Khansari-Zadeh, S.M., Billard, A.: Learning stable nonlinear dynamical systems with gaussian mixture models. IEEE Trans. Robot. 27(5), 943–957 (2011). https://doi.org/10.1109/TRO.2011.2159412
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Fu, J., Wang, C., Du, J., Luo, F. (2019). Concurrent Probabilistic Motion Primitives for Obstacle Avoidance and Human-Robot Collaboration. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11745. Springer, Cham. https://doi.org/10.1007/978-3-030-27529-7_59
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DOI: https://doi.org/10.1007/978-3-030-27529-7_59
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