Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
139 tokens/sec
GPT-4o
47 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Obstacle-Aware Navigation of Soft Growing Robots via Deep Reinforcement Learning (2401.11203v2)

Published 20 Jan 2024 in cs.RO

Abstract: Soft growing robots, are a type of robots that are designed to move and adapt to their environment in a similar way to how plants grow and move with potential applications where they could be used to navigate through tight spaces, dangerous terrain, and hard-to-reach areas. This research explores the application of deep reinforcement Q-learning algorithm for facilitating the navigation of the soft growing robots in cluttered environments. The proposed algorithm utilizes the flexibility of the soft robot to adapt and incorporate the interaction between the robot and the environment into the decision-making process. Results from simulations show that the proposed algorithm improves the soft robot's ability to navigate effectively and efficiently in confined spaces. This study presents a promising approach to addressing the challenges faced by growing robots in particular and soft robots general in planning obstacle-aware paths in real-world scenarios.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (26)
  1. K. Leibrandt, C. Bergeles, and G.-Z. Yang, “Concentric tube robots: Rapid, stable path-planning and guidance for surgical use,” IEEE Robotics & Automation Magazine, vol. 24, no. 2, pp. 42–53, 2017.
  2. G. Brantner and O. Khatib, “Controlling ocean one: Human–robot collaboration for deep-sea manipulation,” Journal of Field Robotics.
  3. R. J. Webster III and B. A. Jones, “Design and kinematic modeling of constant curvature continuum robots: A review,” The International Journal of Robotics Research, vol. 29, no. 13, pp. 1661–1683, 2010.
  4. J. D. Greer, L. H. Blumenschein, A. M. Okamura, and E. W. Hawkes, “Obstacle-aided navigation of a soft growing robot,” in 2018 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2018, pp. 1–8.
  5. I. A. Seleem, S. F. Assal, H. Ishii, and H. El-Hussieny, “Guided pose planning and tracking for multi-section continuum robots considering robot dynamics,” IEEE Access, vol. 7, pp. 166 690–166 703, 2019.
  6. P. Liljebäck, K. Y. Pettersen, Ø. Stavdahl, and J. T. Gravdahl, “A review on modeling, implementation, and control of snake robots,” Robotics and Autonomous Systems, vol. 60, no. 1, pp. 29–40, 2012.
  7. E. Del Dottore, A. Sadeghi, A. Mondini, V. Mattoli, and B. Mazzolai, “Toward growing robots: A historical evolution from cellular to plant-inspired robotics,” Frontiers in Robotics and AI, vol. 5, p. 16, 2018.
  8. H. Tsukagoshi, A. Kitagawa, and M. Segawa, “Active hose: An artificial elephant’s nose with maneuverability for rescue operation,” in Robotics and Automation, 2001. Proceedings 2001 ICRA. IEEE International Conference on, vol. 3.   IEEE, 2001, pp. 2454–2459.
  9. K. Isaki, A. Niitsuma, M. Konyo, F. Takemura, and S. Tadokoro, “Development of an active flexible cable by ciliary vibration drive for scope camera,” in Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on.   IEEE, 2006, pp. 3946–3951.
  10. D. Mishima, T. Aoki, and S. Hirose, “Development of pneumatically controlled expandable arm for search in the environment with tight access,” in Field and Service Robotics.   Springer, 2003, pp. 509–518.
  11. A. Sadeghi, A. Mondini, E. Del Dottore, V. Mattoli, L. Beccai, S. Taccola, C. Lucarotti, M. Totaro, and B. Mazzolai, “A plant-inspired robot with soft differential bending capabilities,” Bioinspiration and Biomimetics, vol. 12, no. 1, p. 015001, 2017.
  12. A. Sadeghi, E. Del Dottore, A. Mondini, and B. Mazzolai, “Passive morphological adaptation for obstacle avoidance in a self-growing robot produced by additive manufacturing,” Soft Robotics, vol. 7, no. 1, pp. 85–94, 2020.
  13. E. W. Hawkes, L. H. Blumenschein, J. D. Greer, and A. M. Okamura, “A soft robot that navigates its environment through growth,” Science Robotics, vol. 2, no. 8, p. eaan3028, jul 2017.
  14. L. H. Blumenschein, A. M. Okamura, and E. W. Hawkes, “Modeling of bioinspired apical extension in a soft robot,” in Conference on Biomimetic and Biohybrid Systems.   Springer, 2017, pp. 522–531.
  15. H. El-Hussieny, U. Mehmood, Z. Mehdi, S.-G. Jeong, M. Usman, E. W. Hawkes, A. M. Okarnura, and J.-H. Ryu, “Development and evaluation of an intuitive flexible interface for teleoperating soft growing robots,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2018, pp. 4995–5002.
  16. M. M. Coad, L. H. Blumenschein, S. Cutler, J. A. R. Zepeda, N. D. Naclerio, H. El-Hussieny, U. Mehmood, J.-H. Ryu, E. W. Hawkes, and A. M. Okamura, “Vine robots: Design, teleoperation, and deployment for navigation and exploration,” arXiv preprint arXiv:1903.00069, 2019.
  17. E.-H. Haitham, S.-G. Jeong, and J.-H. Ryu, “Dynamic modeling of a class of soft growing robots using euler-lagrange formalism,” in Society of Instrument and Control Engineers (SICE) 2019.   IEEE, Society of Instrument and Control Engineers (SICE), 2019.
  18. H. El-Hussieny, I. A. Hameed, and A. B. Zaky, “Plant-inspired soft growing robots: A control approach using nonlinear model predictive techniques,” Applied Sciences, vol. 13, no. 4, p. 2601, 2023.
  19. J. D. Greer, T. K. Morimoto, A. M. Okamura, and E. W. Hawkes, “A soft, steerable continuum robot that grows via tip extension,” Soft robotics, 2018.
  20. B. A. Jones and I. D. Walker, “Kinematics for multisection continuum robots,” IEEE Transactions on Robotics, vol. 22, no. 1, pp. 43–55, 2006.
  21. K. Ashwin, S. K. Mahapatra, and A. Ghosal, “Profile and contact force estimation of cable-driven continuum robots in presence of obstacles,” Mechanism and Machine Theory, vol. 164, p. 104404, 2021.
  22. V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski et al., “Human-level control through deep reinforcement learning,” nature, vol. 518, no. 7540, pp. 529–533, 2015.
  23. V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller, “Playing atari with deep reinforcement learning,” arXiv preprint arXiv:1312.5602, 2013.
  24. M. D. Zeiler, “Adadelta: an adaptive learning rate method,” arXiv preprint arXiv:1212.5701, 2012.
  25. T. Moore and D. Stouch, “A generalized extended kalman filter implementation for the robot operating system,” in Intelligent autonomous systems 13.   Springer, 2016, pp. 335–348.
  26. T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra, “Continuous control with deep reinforcement learning,” arXiv preprint arXiv:1509.02971, 2015.
Citations (4)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now