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Quadruped Guidance Robot for the Visually Impaired: A Comfort-Based Approach (2203.03927v3)

Published 8 Mar 2022 in cs.RO, cs.SY, and eess.SY

Abstract: Guidance robots that can guide people and avoid various obstacles, could potentially be owned by more visually impaired people at a fairly low cost. Most of the previous guidance robots for the visually impaired ignored the human response behavior and comfort, treating the human as an appendage dragged by the robot, which can lead to imprecise guidance of the human and sudden changes in the traction force experienced by the human. In this paper, we propose a novel quadruped guidance robot system with a comfort-based concept. We design a controllable traction device that can adjust the length and force between human and robot to ensure comfort. To allow the human to be guided safely and comfortably to the target position in complex environments, our proposed human motion planner can plan the traction force with the force-based human motion model. To track the planned force, we also propose a robot motion planner that can generate the specific robot motion command and design the force control device. Our system has been deployed on Unitree Laikago quadrupedal platform and validated in real-world scenarios.

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References (24)
  1. S. Gilroy, D. Lau, L. Yang, E. Izaguirre, K. Biermayer, A. Xiao, M. Sun, A. Agrawal, J. Zeng, Z. Li et al., “Autonomous navigation for quadrupedal robots with optimized jumping through constrained obstacles,” in 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE).   IEEE, 2021, pp. 2132–2139.
  2. C. Mastalli, I. Havoutis, M. Focchi, D. G. Caldwell, and C. Semini, “Motion planning for quadrupedal locomotion: Coupled planning, terrain mapping, and whole-body control,” IEEE Transactions on Robotics, vol. 36, no. 6, pp. 1635–1648, 2020.
  3. J. Borenstein and I. Ulrich, “The guidecane-a computerized travel aid for the active guidance of blind pedestrians,” in Proceedings of International Conference on Robotics and Automation, vol. 2.   IEEE, 1997, pp. 1283–1288.
  4. T.-K. Chuang, N.-C. Lin, J.-S. Chen, C.-H. Hung, Y.-W. Huang, C. Teng, H. Huang, L.-F. Yu, L. Giarré, and H.-C. Wang, “Deep trail-following robotic guide dog in pedestrian environments for people who are blind and visually impaired-learning from virtual and real worlds,” in 2018 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2018, pp. 5849–5855.
  5. Z. Li and R. Hollis, “Toward a ballbot for physically leading people: A human-centered approach,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2019, pp. 4827–4833.
  6. K. A. Hamed, V. R. Kamidi, W.-L. Ma, A. Leonessa, and A. D. Ames, “Hierarchical and safe motion control for cooperative locomotion of robotic guide dogs and humans: A hybrid systems approach,” IEEE Robotics and Automation Letters, vol. 5, no. 1, pp. 56–63, 2019.
  7. A. Xiao, W. Tong, L. Yang, J. Zeng, Z. Li, and K. Sreenath, “Robotic guide dog: Leading a human with leash-guided hybrid physical interaction,” in 2021 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2021, pp. 11 470–11 476.
  8. M. Rubagotti, I. Tusseyeva, S. Baltabayeva, D. Summers, and A. Sandygulova, “Perceived safety in physical human–robot interaction—a survey,” Robotics and Autonomous Systems, vol. 151, p. 104047, 2022.
  9. S. Tachi, K. Tanie, K. Komoriya, Y. Hosoda, and M. Abe, “Guide dog robot—its basic plan and some experiments with meldog mark i,” Mechanism and Machine Theory, vol. 16, no. 1, pp. 21–29, 1981.
  10. Y.-P. Hsu, C.-C. Tsai, Z.-C. Wang, Y.-J. Feng, and H.-H. Lin, “Hybrid navigation of a four-wheeled tour-guide robot,” in 2009 ICCAS-SICE.   IEEE, 2009, pp. 4353–4358.
  11. R. Soltani-Zarrin, A. Zeiaee, and S. Jayasuriya, “Pointwise angle minimization: A method for guiding wheeled robots based on constrained directions,” in Dynamic Systems and Control Conference, vol. 46209.   American Society of Mechanical Engineers, 2014, p. V003T48A004.
  12. J. Guerreiro, D. Sato, S. Asakawa, H. Dong, K. M. Kitani, and C. Asakawa, “Cabot: Designing and evaluating an autonomous navigation robot for blind people,” in The 21st International ACM SIGACCESS conference on computers and accessibility, 2019, pp. 68–82.
  13. D. R. Bruno, M. H. de Assis, and F. S. Osório, “Development of a mobile robot: Robotic guide dog for aid of visual disabilities in urban environments,” in 2019 Latin American Robotics Symposium (LARS), 2019 Brazilian Symposium on Robotics (SBR) and 2019 Workshop on Robotics in Education (WRE).   IEEE, 2019, pp. 104–108.
  14. E. Folmer, “Exploring the use of an aerial robot to guide blind runners,” ACM SIGACCESS accessibility and computing, no. 112, pp. 3–7, 2015.
  15. H. Tan, C. Chen, X. Luo, J. Zhang, C. Seibold, K. Yang, and R. Stiefelhagen, “Flying guide dog: Walkable path discovery for the visually impaired utilizing drones and transformer-based semantic segmentation,” 2021 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1123–1128, 2021.
  16. R. K. Katzschmann, B. Araki, and D. Rus, “Safe local navigation for visually impaired users with a time-of-flight and haptic feedback device,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 3, pp. 583–593, 2018.
  17. S. Kalpana, S. Rajagopalan, R. Ranjith, and R. Gomathi, “Voice recognition based multi robot for blind people using lidar sensor,” in 2020 International Conference on System, Computation, Automation and Networking (ICSCAN).   IEEE, 2020, pp. 1–6.
  18. L. Yang, I. Herzi, A. Zakhor, A. Hiremath, S. Bazargan, and R. Tames-Gadam, “Indoor query system for the visually impaired,” in International Conference on Computers Helping People with Special Needs.   Springer, 2020, pp. 517–525.
  19. C. Ye, S. Hong, X. Qian, and W. Wu, “Co-robotic cane: A new robotic navigation aid for the visually impaired,” IEEE Systems, Man, and Cybernetics Magazine, vol. 2, no. 2, pp. 33–42, 2016.
  20. T. Flush, “The coordination of arm movements: an experimentally confirmed mathematical model.” J. neurosciences, vol. 5, 1987.
  21. J. A. Andersson, J. Gillis, G. Horn, J. B. Rawlings, and M. Diehl, “Casadi: a software framework for nonlinear optimization and optimal control,” Mathematical Programming Computation, vol. 11, no. 1, pp. 1–36, 2019.
  22. L. T. Biegler and V. M. Zavala, “Large-scale nonlinear programming using ipopt: An integrating framework for enterprise-wide dynamic optimization,” Computers and Chemical Engineering, vol. 33, no. 3, pp. 575–582, 2009.
  23. W. Hess, D. Kohler, H. Rapp, and D. Andor, “Real-time loop closure in 2d lidar slam,” in 2016 IEEE International Conference on Robotics and Automation (ICRA), 2016, pp. 1271–1278.
  24. C.-L. Hwang and K. Yoon, “Methods for multiple attribute decision making,” in Multiple attribute decision making.   Springer, 1981, pp. 58–191.
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