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Scalable Motion Planning and Task-Oriented Coordination Scheme for Mobile Manipulators in Smart Manufacturing

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Abstract

In industry, mobile manipulators are commonly used to perform various tasks. However, current mobile manipulator motion planning still poses significant challenges due to the scalability of the manipulator motion planning, the non-holonomic nature of the mobile base, and the need to coordinate the motion of the manipulator and mobile base. Considering the accuracy disparity between the mobile base and manipulator, this paper proposes a joint space coordination scheme for mobile manipulators to improve the execution quality of the end-effector trajectory obtained by learning from demonstrations in the task space. Additionally, a task-oriented manipulability is developed to further enhance the execution accuracy. The results of extensive simulations are presented to show the effectiveness of the proposed method.

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Data Availability

The authors declare that the data supporting the findings of this study are available within the paper. Should any raw data files be needed in another format they are available from the corresponding author upon reasonable request.

References

  1. Schou, C., Andersen, R.S., Chrysostomou, D., Bøgh, S., Madsen, O.: Skill-based instruction of collaborative robots in industrial settings. Robot. Comput. Integr. Manuf. 53, 72–80 (2018)

    Article  Google Scholar 

  2. Meng, J., Wang, S., Li, G., Jiang, L., Zhang, X., Liu, C., Xie, Y.: Iterative-learning error compensation for autonomous parking of mobile manipulator in harsh industrial environment. Robot. Comput. Integr. Manuf. 68, 102077 (2021)

    Article  Google Scholar 

  3. Pedersen, M.R., Nalpantidis, L., Andersen, R.S., Schou, C., Bøgh, S., Krüger, V., Madsen, O.: Robot skills for manufacturing: From concept to industrial deployment. Robot. Comput. Integr. Manuf. 37, 282–291 (2016)

    Article  Google Scholar 

  4. Shkolnik, A., Tedrake, R.: Path planning in 1000+ dimensions using a task-space Voronoi bias. In: 2009 IEEE International Conference on Robotics and Automation. pp. 2061–2067. IEEE (2009)

  5. Seyboldt, R., Frese, C., Zube, A.: Sampling-based path planning to cartesian goal positions for a mobile manipulator exploiting kinematic redundancy. In: Proceedings of ISR 2016: 47st International Symposium on Robotics. pp. 1–9. VDE (2016)

  6. Yu, Q., Wang, G., Hua, X., Zhang, S., Song, L., Zhang, J., Chen, K.: Base position optimization for mobile painting robot manipulators with multiple constraints. Robot. Comput. Integr. Manuf. 54, 56–64 (2018)

    Article  Google Scholar 

  7. Kalakrishnan, M., Chitta, S., Theodorou, E., Pastor, P., Schaal, S.: STOMP: Stochastic trajectory optimization for motion planning. In: 2011 IEEE international conference on robotics and automation. pp. 4569–4574. IEEE (2011)

  8. Terasawa, R., Ariki, Y., Narihira, T., Tsuboi, T., Nagasaka, K.: 3D-CNN based heuristic guided task-space planner for faster motion planning. In: 2020 IEEE International Conference on Robotics and Automation (ICRA). pp. 9548–9554. IEEE (2020)

  9. Gul, F., Mir, I., Abualigah, L., Sumari, P., Forestiero, A.: A consolidated review of path planning and optimization techniques: Technical perspectives and future directions. Electron. 10, 1–38 (2021). https://doi.org/10.3390/electronics10182250

    Article  Google Scholar 

  10. Botteghi, N., Sirmacek, B., Mustafa, K.A.A., Poel, M., Stramigioli, S.: On reward shaping for mobile robot navigation: a reinforcement learning and SLAM based approach. arXiv preprint arXiv:2002.04109 (2020)

  11. Sandakalum, T., Ang, M.H., Jr.: Motion planning for mobile manipulators—a systematic review. Machines. 10, 97 (2022)

    Article  Google Scholar 

  12. Mukherjee, D., Gupta, K., Chang, L.H., Najjaran, H.: A survey of robot learning strategies for human-robot collaboration in industrial settings. Robot. Comput. Integr. Manuf. 73, 102231 (2022)

    Article  Google Scholar 

  13. Kulić, D., Takano, W., Nakamura, Y.: Incremental learning, clustering and hierarchy formation of whole body motion patterns using adaptive hidden markov chains. Int. J. Rob. Res. 27, 761–784 (2008)

    Article  Google Scholar 

  14. Petrović, M., Miljković, Z., Jokić, A.: A novel methodology for optimal single mobile robot scheduling using whale optimization algorithm. Appl. Soft Comput. 81, 105520 (2019). https://doi.org/10.1016/J.ASOC.2019.105520

    Article  Google Scholar 

  15. Duan, J., Ou, Y., Xu, S., Liu, M.: Sequential learning unification controller from human demonstrations for robotic compliant manipulation. Neurocomputing 366, 35–45 (2019). https://doi.org/10.1016/j.neucom.2019.07.081

    Article  Google Scholar 

  16. Yu, T., Chang, Q.: User-guided motion planning with reinforcement learning for human-robot collaboration in smart manufacturing. Expert Sys. Appl.  209, 118291 (2022)

  17. Petrović, L., Peršić, J., Seder, M., Marković, I.: Cross-entropy based stochastic optimization of robot trajectories using heteroscedastic continuous-time Gaussian processes. Rob. Auton. Syst. 133, 103618 (2020)

    Article  Google Scholar 

  18. Watanabe, K., Kiguchi, K., Izumi, K., Kunitake, Y.: Path planning for an omnidirectional mobile manipulator by evolutionary computation. In: 1999 Third International Conference on Knowledge-Based Intelligent Information Engineering Systems. Proceedings (Cat. No. 99TH8410). pp. 135–140. IEEE (1999)

  19. Wang, J., Chortos, A.: Control strategies for soft robot systems. Adv. Intell. Syst. 4, 2100165 (2022). https://doi.org/10.1002/aisy.202100165

    Article  Google Scholar 

  20. Abdor-Sierra, J.A., Merchán-Cruz, E.A., Rodríguez-Cañizo, R.G.: A comparative analysis of metaheuristic algorithms for solving the inverse kinematics of robot manipulators. Results Eng. 16, 100597 (2022). https://doi.org/10.1016/j.rineng.2022.100597

  21. Vazquez-Santiago, K., Goh, C.F., Shimada, K.: Motion Planning for Kinematically Redundant Mobile Manipulators with Genetic Algorithm, Pose Interpolation, and Inverse Kinematics. IEEE Int. Conf. Autom. Sci. Eng. 2021-August, 1167–1174 (2021). https://doi.org/10.1109/CASE49439.2021.9551546

  22. Castaman, N., Pagello, E., Menegatti, E., Pretto, A.: Receding horizon task and motion planning in changing environments. Rob. Auton. Syst. 145, 103863 (2021)

    Article  Google Scholar 

  23. Rastegarpanah, A., Gonzalez, H.C., Stolkin, R.: Semi-autonomous behaviour tree-based framework for sorting electric vehicle batteries components. Robotics 10, 1–18 (2021). https://doi.org/10.3390/robotics10020082

    Article  Google Scholar 

  24. Thakar, S., Srinivasan, S., Al-Hussaini, S., Bhatt, P.M., Rajendran, P., Yoon, Y.J., Dhanaraj, N., Malhan, R.K., Schmid, M., Krovi, V.N., Gupta, S.K.: A survey of wheeled mobile manipulation: a decision-making perspective. J. Mech. Robot. 15(2), 020801 (2023). https://doi.org/10.1115/1.4054611

  25. Mukadam, M., Yan, X., Boots, B.: Gaussian process motion planning. In: 2016 IEEE international conference on robotics and automation (ICRA). pp. 9–15. IEEE (2016)

  26. Zhao, M., Ansari, N., Hou, E.S.H.: Mobile manipulator path planning by a genetic algorithm. J. Robot. Syst. 11, 143–153 (1994)

    Article  MATH  Google Scholar 

  27. Iriondo, A., Lazkano, E., Susperregi, L., Urain, J., Fernandez, A., Molina, J.: Pick and place operations in logistics using a mobile manipulator controlled with deep reinforcement learning. Appl. Sci. 9, 348 (2019)

    Article  Google Scholar 

  28. Aleotti, J., Caselli, S., Reggiani, M.: Evaluation of virtual fixtures for a robot programming by demonstration interface. IEEE Trans. Syst. Man. Cybern. Part ASystems Humans 35, 536–545 (2005). https://doi.org/10.1109/TSMCA.2005.850604

  29. Gribovskaya, E., Khansari-Zadeh, S.M., Billard, A.: Learning non-linear multivariate dynamics of motion in robotic manipulators. Int. J. Rob Res. 30(1), 80–117 (2010). https://doi.org/10.1177/0278364910376251

  30. Dimeas, F., Aspragathos, N.: Reinforcement learning of variable admittance control for human-robot co-manipulation. IEEE Int. Conf. Intell. Robot. Syst. 2015-Decem, 1011–1016 (2015). https://doi.org/10.1109/IROS.2015.7353494

  31. Sermanet, P., Lynch, C., Chebotar, Y., Hsu, J., Jang, E., Schaal, S., Levine, S., Brain, G.: Time-contrastive networks: self-supervised learning from video. In: 2018 IEEE international conference on robotics and automation (ICRA), pp. 1134–1141. IEEE (2018)

  32. Wu, Y., Demiris, Y.: Towards one shot learning by imitation for humanoid robots. Proc. - IEEE Int. Conf. Robot. Autom. 2889–2894 (2010). https://doi.org/10.1109/ROBOT.2010.5509429

  33. Yu, T., Finn, C., Xie, A., Dasari, S., Zhang, T., Abbeel, P., Levine, S.: One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning. 6th Int. Conf. Learn. Represent. ICLR 2018 - Work. Track Proc. (2018). https://doi.org/10.15607/RSS.2018.XIV.002

  34. Kavan, L., Collins, S., O’Sullivan, C., Zara, J.: Dual quaternions for rigid transformation blending. Trinity Coll. Dublin, Tech. Rep. TCD-CS-2006–46. (2006)

  35. Laha, R., Rao, A., Figueredo, L.F.C., Chang, Q., Haddadin, S., Chakraborty, N.: Point-to-Point Path Planning Based on User Guidance and Screw Linear Interpolation. Proc. ASME Des. Eng. Tech. Conf. 8B-2021, (2021). https://doi.org/10.1115/DETC2021-71814

  36. Yoshikawa, T.: Manipulability of robotic mechanisms. Int. J. Rob. Res. 4, 3–9 (1985)

    Article  Google Scholar 

  37. Sugihara, T.: Solvability-unconcerned inverse kinematics by the Levenberg-Marquardt method. IEEE Trans. Robot. 27, 984–991 (2011). https://doi.org/10.1109/TRO.2011.2148230

    Article  Google Scholar 

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Acknowledgements

This work was supported by the U.S. National Science Foundation (NSF) Grant CMMI1853454.

Funding

This work was supported by the U.S. National Science Foundation (NSF) Grant CMMI1853454.

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Authors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA, 22904, USA

    Tian Yu & Qing Chang

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  1. Tian Yu
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Contributions

Tian Yu: Conceptualization, Methodology, Software, Validation, Methodology, Writing—Original Draft.

Qing Chang*: Supervision, Conceptualization, Methodology, Writing—Original Draft.

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Correspondence to Qing Chang.

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Yu, T., Chang, Q. Scalable Motion Planning and Task-Oriented Coordination Scheme for Mobile Manipulators in Smart Manufacturing. J Intell Robot Syst 109, 72 (2023). https://doi.org/10.1007/s10846-023-02005-y

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