Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
156 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Planning Optimal Trajectories for Mobile Manipulators under End-effector Trajectory Continuity Constraint (2309.12251v2)

Published 21 Sep 2023 in cs.RO

Abstract: Mobile manipulators have been employed in many applications that are traditionally performed by either multiple fixed-base robots or a large robotic system. This capability is enabled by the mobility of the mobile base. However, the mobile base also brings redundancy to the system, which makes mobile manipulator motion planning more challenging. In this paper, we tackle the mobile manipulator motion planning problem under the end-effector trajectory continuity constraint in which the end-effector is required to traverse a continuous task-space trajectory (time-parametrized path), such as in mobile printing or spraying applications. Our method decouples the problem into: (1) planning an optimal base trajectory subject to geometric task constraints, end-effector trajectory continuity constraint, collision avoidance, and base velocity constraint; which ensures that (2) a manipulator trajectory is computed subsequently based on the obtained base trajectory. To validate our method, we propose a discrete optimal base trajectory planning algorithm to solve several mobile printing tasks in hardware experiment and simulations.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (27)
  1. J. Sustarevas, D. Kanoulas, and S. Julier, “Autonomous mobile 3d printing of large-scale trajectories,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2022, pp. 6561–6568.
  2. J. Sustarevas, D. Kanoulas, and S. Julier, “Task-consistent path planning for mobile 3d printing,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2021, pp. 2143–2150.
  3. M. E. Tiryaki, X. Zhang, and Q.-C. Pham, “Printing-while-moving: a new paradigm for large-scale robotic 3d printing,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2019, pp. 2286–2291.
  4. X. Zhang, M. Li, J. H. Lim, Y. Weng, Y. W. D. Tay, H. Pham, and Q.-C. Pham, “Large-scale 3d printing by a team of mobile robots,” Automation in Construction, vol. 95, pp. 98–106, 2018.
  5. K. Dörfler, G. Dielemans, L. Lachmayer, T. Recker, A. Raatz, D. Lowke, and M. Gerke, “Additive manufacturing using mobile robots: Opportunities and challenges for building construction,” Cement and Concrete Research, vol. 158, p. 106772, 2022.
  6. R. Malhan and S. K. Gupta, “Finding optimal sequence of mobile manipulator placements for automated coverage planning of large complex parts,” in Proceedings of the ASME 2021 International Design Engineering Technical Conferences (IDETC), 2022.
  7. J. Xu, Y. Domae, T. Ueshiba, W. Wan, and K. Harada, “Planning a minimum sequence of positions for picking parts from multiple trays using a mobile manipulator,” IEEE Access, vol. 9, pp. 165 526–165 541, 2021.
  8. Q.-N. Nguyen, N. Adrian, and Q.-C. Pham, “Task-space clustering for mobile manipulator task sequencing,” in 2023 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2023, pp. 3693–3699.
  9. S. Srivastava, E. Fang, L. Riano, R. Chitnis, S. Russell, and P. Abbeel, “Combined task and motion planning through an extensible planner-independent interface layer,” in 2014 IEEE international conference on robotics and automation (ICRA).   IEEE, 2014, pp. 639–646.
  10. S. E. Jenny, L. L. Pietrasik, E. Sounigo, P.-H. Tsai, F. Gramazio, M. Kohler, E. Lloret-Fritschi, and M. Hutter, “Continuous mobile thin-layer on-site printing,” Automation in Construction, vol. 146, p. 104634, 2023.
  11. K. Nagatani, T. Hirayama, A. Gofuku, and Y. Tanaka, “Motion planning for mobile manipulator with keeping manipulability,” in IEEE/RSJ international conference on intelligent robots and systems, vol. 2.   IEEE, 2002, pp. 1663–1668.
  12. G. Oriolo, M. Ottavi, and M. Vendittelli, “Probabilistic motion planning for redundant robots along given end-effector paths,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 2.   IEEE, 2002, pp. 1657–1662.
  13. G. Oriolo and C. Mongillo, “Motion planning for mobile manipulators along given end-effector paths,” in Proceedings of the 2005 IEEE international conference on robotics and automation.   IEEE, 2005, pp. 2154–2160.
  14. T. Welschehold, C. Dornhege, F. Paus, T. Asfour, and W. Burgard, “Coupling mobile base and end-effector motion in task space,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2018, pp. 1–9.
  15. J. Haviland, N. Sünderhauf, and P. Corke, “A holistic approach to reactive mobile manipulation,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 3122–3129, 2022.
  16. T. Sandakalum and M. H. Ang Jr, “Motion planning for mobile manipulators—a systematic review,” Machines, vol. 10, no. 2, p. 97, 2022.
  17. G. B. Avanzini, A. M. Zanchettin, and P. Rocco, “Constraint-based model predictive control for holonomic mobile manipulators,” in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2015, pp. 1473–1479.
  18. J. Pankert and M. Hutter, “Perceptive model predictive control for continuous mobile manipulation,” IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 6177–6184, 2020.
  19. M. Giftthaler, F. Farshidian, T. Sandy, L. Stadelmann, and J. Buchli, “Efficient kinematic planning for mobile manipulators with non-holonomic constraints using optimal control,” in 2017 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2017, pp. 3411–3417.
  20. F. Zacharias, C. Borst, M. Beetz, and G. Hirzinger, “Positioning mobile manipulators to perform constrained linear trajectories,” in 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.   IEEE, 2008, pp. 2578–2584.
  21. N. Vahrenkamp, T. Asfour, and R. Dillmann, “Robot placement based on reachability inversion,” in 2013 IEEE International Conference on Robotics and Automation.   IEEE, 2013, pp. 1970–1975.
  22. Z. Xian, P. Lertkultanon, and Q.-C. Pham, “Closed-chain manipulation of large objects by multi-arm robotic systems,” IEEE Robotics and Automation Letters, vol. 2, no. 4, pp. 1832–1839, 2017.
  23. D. Rakita, B. Mutlu, and M. Gleicher, “Stampede: A discrete-optimization method for solving pathwise-inverse kinematics,” in 2019 International Conference on Robotics and Automation (ICRA).   IEEE, 2019, pp. 3507–3513.
  24. D. Rakita, B. Mutlu, and M. Gleicher, “Relaxedik: Real-time synthesis of accurate and feasible robot arm motion.” in Robotics: Science and Systems, vol. 14.   Pittsburgh, PA, 2018, pp. 26–30.
  25. M. Trümper, “Lagrangian mechanics and the geometry of configuration spacetime,” Annals of Physics, vol. 149, no. 1, pp. 203–233, 1983.
  26. R. Diankov, “Automated construction of robotic manipulation programs,” Ph.D. dissertation, Carnegie Mellon University, The Robotics Institute Pittsburgh, 2010.
  27. H. Pham and Q.-C. Pham, “A new approach to time-optimal path parameterization based on reachability analysis,” IEEE Transactions on Robotics, vol. 34, no. 3, pp. 645–659, 2018.
Citations (3)

Summary

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