A Deep Learning Strategy For On-Orbit Servicing Via Space Robotic Manipulator
Tóm tắt
Autonomous robotic systems are currently being addressed as a critical element in the development of present and future on-orbit operations. Modern missions are calling for systems capable of reproducing human’s decision-making process, thus enhancing their performance. Generally, space manipulators are mounted on a floating spacecraft in a microgravity environment, consequently leading to a mutual influence between the robotic arms and the platform dynamics, thus making the motion planning and control design more challenging than those of terrestrial robots. Another aspect to be considered is that space robots are designed as lightweight systems, resulting in a significant dynamic coupling between their rigid motion and structural elasticity. These effects involve critical issues in modelling their dynamics and designing a suitable controller. In this context, Deep Neural Network (DNN) architectures and the related Deep Learning (DL) techniques have widely proved to have powerful capability in solving data-driven nonlinear modelling problems and they can hence represent a viable solution for space activities. The present paper deals with the design of a DNN controller for a space manipulator system, which has to follow a specific path for a typical on-orbit servicing mission. The goal is to provide proper control inputs autonomously adapting to the given desired trajectory. Structural flexibility and joint friction features are implemented in the dynamic model as well.
Tài liệu tham khảo
Flores-Abad, A., Ma, O., Pham, K., Ulrich, S.: A review of space robotics technologies for on-orbit servicing. Progress Aerosp. Sci. 68, 1–26 (2014)
King, D.: Space servicing: past, present and future. In: Proceedings of the 6th International Symposium on Artificial Intelligence and Robotics & Automation in Space. Montréal, Canada (2001)
Di Cairano, S., Park, H., Kolmanovsky, I.: Model predictive control approach for guidance of spacecraft rendezvous and proximity maneuvering. Int. J. Robust Nonlinear Control 22(12), 1398–1427 (2012)
Stolfi, A., Gasbarri, P., Sabatini, M.: Optimal in-orbit operations of a multi-degree of freedom space manipulator. In: Proceedings of the 69th International Astronautical Congress (IAC), Bremen, Germany (2018)
Stolfi, A., Gasbarri, P., Sabatini, M.: A combined impedance-PD approach for controlling a dual-arm space manipulator in the capture of a non-cooperative target. Acta Astronaut. 139, 243–253 (2017)
Stolfi, A., Gasbarri, P., Misra, A.K.: A two-arm flexible space manipulator system for post-grasping manipulation operations of a passive target object. In: Proceedings of the 70th International Astronautical Congress (IAC). Washington D.C., USA (2019)
Gasbarri, P., Pisculli, A.: Dynamic/control interactions between flexible orbiting space-robot during grasping, docking and post-docking maneuvers. Acta Astronautica 110, 225–238 (2015)
Meng, D., Lu, W., Xu, W., She, Y., Wang, X., Liang, B., Yuan, B.: Vibration suppression control of free-floating space robots with flexible appendages for autonomous target capturing. Acta Astronaut. 151, 904–918 (2018)
Jia, S., Jia, Y., Xu, S., Hu, Q.: Maneuver and active vibration suppression of free-flying space robot. IEEE Trans. Aerosp. Electron. Syst. 54(3), 1115–1134 (2018)
Hochreiter, S, Bengio, Y., Frasconi, P., Schmidhuber, J.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. In: A field guide to dynamical recurrent neural networks (2003)
Olsson, H., Astrӧm, K.J., Canudas de Wit, C., Gӓfvert, M., Lischinsky, P.: Friction models and friction compensation. Eur. J. Control 4, 176–195 (1998)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Rosato, A., Panella, M., Araneo, R., Andreotti, A.: A neural network-based prediction system of distributed generation for the management of microgrids. IEEE Trans. Ind. Appl. (2019)
Rosato, A., Altilio, R., Araneo, R., Panella, M.: Prediction in photovoltaic power by neural networks. Energies 10(7), 1003–1028 (2017)
Kingma, D., Ba, J.: A method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR), San Diego, CA, USA (2015)