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Reinforcement Learning Driven Cooperative Ball Balance in Rigidly Coupled Drones (2404.19070v1)

Published 29 Apr 2024 in cs.RO

Abstract: Multi-drone cooperative transport (CT) problem has been widely studied in the literature. However, limited work exists on control of such systems in the presence of time-varying uncertainties, such as the time-varying center of gravity (CG). This paper presents a leader-follower approach for the control of a multi-drone CT system with time-varying CG. The leader uses a traditional Proportional-Integral-Derivative (PID) controller, and in contrast, the follower uses a deep reinforcement learning (RL) controller using only local information and minimal leader information. Extensive simulation results are presented, showing the effectiveness of the proposed method over a previously developed adaptive controller and for variations in the mass of the objects being transported and CG speeds. Preliminary experimental work also demonstrates ball balance (depicting moving CG) on a stick/rod lifted by two Crazyflie drones cooperatively.

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References (27)
  1. K. Zarzycki and M. Ławryńczuk, “Fast real-time model predictive control for a ball-on-plate process,” Sensors, vol. 21, no. 12, p. 3959, 2021.
  2. K. J. Åström, “Theory and applications of adaptive control—a survey,” Automatica, vol. 19, no. 5, pp. 471–486, 1983.
  3. S. Barawkar, M. Kumar, and M. Bolender, “Decentralized adaptive controller for multi-drone cooperative transport with offset and moving center of gravity,” Aerospace Science and Technology, vol. 145, p. 108960, 2024.
  4. G. Loianno and V. Kumar, “Cooperative transportation using small quadrotors using monocular vision and inertial sensing,” IEEE Robotics and Automation Letters, vol. 3, no. 2, pp. 680–687, 2017.
  5. S. Barawkar and M. Kumar, “Force-torque (ft) based multi-drone cooperative transport using fuzzy logic and low-cost and imprecise ft sensor,” Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, p. 09544100231153686, 2023.
  6. K. Sreenath and V. Kumar, “Dynamics, control and planning for cooperative manipulation of payloads suspended by cables from multiple quadrotor robots,” rn, vol. 1, no. r2, p. r3, 2013.
  7. P. Culbertson and M. Schwager, “Decentralized adaptive control for collaborative manipulation,” in 2018 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2018, pp. 278–285.
  8. H. Kawasaki, S. Ito, and R. B. Ramli, “Adaptive decentralized coordinated control of multiple robot arms,” IFAC Proceedings Volumes, vol. 36, no. 17, pp. 387–392, 2003.
  9. G. A. Cardona, D. S. D’Antonio, R. Fierro, and D. Saldaña, “Adaptive control for cooperative aerial transportation using catenary robots,” in 2021 Aerial Robotic Systems Physically Interacting with the Environment (AIRPHARO).   IEEE, 2021, pp. 1–8.
  10. A. S. Aghdam, M. B. Menhaj, F. Barazandeh, and F. Abdollahi, “Cooperative load transport with movable load center of mass using multiple quadrotor uavs,” in 2016 4th International Conference on Control, Instrumentation, and Automation (ICCIA).   IEEE, 2016, pp. 23–27.
  11. F. Arab, F. A. Shirazi, and M. R. H. Yazdi, “Cooperative parameter estimation of a nonuniform payload by multiple quadrotors,” Robotica, pp. 1–20, 2021.
  12. F. Pierri, M. Nigro, G. Muscio, and F. Caccavale, “Cooperative manipulation of an unknown object via omnidirectional unmanned aerial vehicles,” Journal of Intelligent & Robotic Systems, vol. 100, no. 3, pp. 1635–1649, 2020.
  13. S. Barawkar and M. Kumar, “Cooperative transport of a payload with offset cg using multiple uavs,” in Dynamic systems and control conference, vol. 59162.   American Society of Mechanical Engineers, 2019, p. V003T21A008.
  14. E. Kaufmann, L. Bauersfeld, A. Loquercio, M. Müller, V. Koltun, and D. Scaramuzza, “Champion-level drone racing using deep reinforcement learning,” Nature, vol. 620, no. 7976, pp. 982–987, 2023.
  15. T. Haarnoja, V. Pong, A. Zhou, M. Dalal, P. Abbeel, and S. Levine, “Composable deep reinforcement learning for robotic manipulation,” in 2018 IEEE international conference on robotics and automation (ICRA).   IEEE, 2018, pp. 6244–6251.
  16. N. Rudin, D. Hoeller, P. Reist, and M. Hutter, “Learning to walk in minutes using massively parallel deep reinforcement learning,” in Conference on Robot Learning.   PMLR, 2022, pp. 91–100.
  17. J. Hwangbo, I. Sa, R. Siegwart, and M. Hutter, “Control of a quadrotor with reinforcement learning,” IEEE Robotics and Automation Letters, vol. 2, no. 4, pp. 2096–2103, 2017.
  18. L. Zhang, Y. Sun, A. Barth, and O. Ma, “Decentralized control of multi-robot system in cooperative object transportation using deep reinforcement learning,” IEEE Access, vol. 8, pp. 184 109–184 119, 2020.
  19. S. V. Manko, S. A. Diane, A. E. Krivoshatskiy, I. D. Margolin, and E. A. Slepynina, “Adaptive control of a multi-robot system for transportation of large-sized objects based on reinforcement learning,” in 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus).   IEEE, 2018, pp. 923–927.
  20. G. Eoh and T.-H. Park, “Cooperative object transportation using curriculum-based deep reinforcement learning,” Sensors, vol. 21, no. 14, p. 4780, 2021.
  21. S. Chen, G. Liu, Z. Zhou, K. Zhang, and J. Wang, “Robust multi-agent reinforcement learning method based on adversarial domain randomization for real-world dual-uav cooperation,” IEEE Transactions on Intelligent Vehicles, 2023.
  22. X. Li, J. Zhang, and J. Han, “Trajectory planning of load transportation with multi-quadrotors based on reinforcement learning algorithm,” Aerospace Science and Technology, vol. 116, p. 106887, 2021.
  23. Y. Duan, X. Chen, R. Houthooft, J. Schulman, and P. Abbeel, “Benchmarking deep reinforcement learning for continuous control,” in International conference on machine learning.   PMLR, 2016, pp. 1329–1338.
  24. T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, “Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,” in International conference on machine learning.   PMLR, 2018, pp. 1861–1870.
  25. M. Haklidir and H. Temeltaş, “Guided soft actor critic: A guided deep reinforcement learning approach for partially observable markov decision processes,” IEEE Access, vol. 9, pp. 159 672–159 683, 2021.
  26. S. Barawkar, M. Radmanesh, M. Kumar, and K. Cohen, “Admittance based force control for collaborative transportation of a common payload using two uavs,” in Dynamic Systems and Control Conference, vol. 58295.   American Society of Mechanical Engineers, 2017, p. V003T39A007.
  27. H. Tang, A. Wang, F. Xue, J. Yang, and Y. Cao, “A novel hierarchical soft actor-critic algorithm for multi-logistics robots task allocation,” Ieee Access, vol. 9, pp. 42 568–42 582, 2021.

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