Hostname: page-component-76fb5796d-zzh7m Total loading time: 0 Render date: 2024-04-27T23:41:54.777Z Has data issue: false hasContentIssue false
  • English
  • Français

A divide-and-conquer control strategy with decentralized control barrier function for luggage trolley transportation by collaborative robots

Published online by Cambridge University Press:  17 August 2023

Xuheng Gao Open the ORCID record for Xuheng Gao [Opens in a new window] ,
Hao Luan Open the ORCID record for Hao Luan [Opens in a new window] ,
Bingyi Xia ,
Ziqi Zhao Open the ORCID record for Ziqi Zhao [Opens in a new window] ,
Jiankun Wang Open the ORCID record for Jiankun Wang [Opens in a new window]  and
Max Q.-H. Meng
Xuheng Gao
Affiliation:
Shenzhen Key Laboratory of Robotics Perception and Intelligence, Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China
Hao Luan
Affiliation:
Shenzhen Key Laboratory of Robotics Perception and Intelligence, Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China
Bingyi Xia
Affiliation:
Shenzhen Key Laboratory of Robotics Perception and Intelligence, Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China
Ziqi Zhao
Affiliation:
Shenzhen Key Laboratory of Robotics Perception and Intelligence, Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China
Jiankun Wang*
Affiliation:
Shenzhen Key Laboratory of Robotics Perception and Intelligence, Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China Jiaxing Research Institute, Southern University of Science and Technology, Jiaxing, China
Max Q.-H. Meng*
Affiliation:
Shenzhen Key Laboratory of Robotics Perception and Intelligence, Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China
*
Corresponding authors: Jiankun Wang, Max Q.-H. Meng; Emails: wangjk@sustech.edu.cn, max.meng@ieee.org
Corresponding authors: Jiankun Wang, Max Q.-H. Meng; Emails: wangjk@sustech.edu.cn, max.meng@ieee.org
Get access Rights & Permissions [Opens in a new window]

Abstract

This article focuses on the luggage trolley transportation problem, an essential part of robotic autonomous luggage trolley collection. To efficiently address the nonholonomic constraints derived from the formation of two collaborative robots and a queue of luggage trolleys, we propose a comprehensive framework consisting of a global planning method and a real-time divide-and-conquer control strategy. The popular Hybrid A* algorithm generates a feasible path as the global planner. A model predictive controller is designed to track this path stably and in real time. To maintain the formation so that the whole queue of robots and luggage trolleys does not split, a safety filter that consists of a discrete-time control Lyapunov function and a decentralized control barrier function is implemented in the transportation process. Finally, we conduct real-world experiments to verify the effectiveness of the proposed method on three representative paths, and the results show that our approach can achieve robust performance. The demonstration video can be found at https://www.youtube.com/watch?v=iPiT8BfLIpU.

Keywords

mobile robotsmulti-robot systemsautomationdecentralized controlcollaborative robots
Type
Research Article
Information
Robotica , Volume 41 , Issue 11 , November 2023 , pp. 3333 - 3348
Copyright
© The Author(s), 2023. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Wang, J. and Meng, M. Q.-H., “Real-time decision making and path planning for robotic autonomous luggage trolley collection at airports,” IEEE Trans. Syst. Man Cybern. Syst. 52(4), 2174–2183 (2022).CrossRefGoogle Scholar
Wang, J. and Meng, M. Q.-H.. Path Planning for Nonholonomic Multiple Mobile Robot System with Applications to Robotic Autonomous Luggage Trolley Collection at Airports. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2020) pp. 2726–2733.Google Scholar
Pan, J., Mai, X., Wang, C., Min, Z., Wang, J., Cheng, H., Li, T., Lyu, E., Liu, L. and Meng, M. Q.-H., “A searching space constrained partial to full registration approach with applications in airport trolley deployment robot,” IEEE Sens. J. 21(10), 11946–11960 (2021).CrossRefGoogle Scholar
Xiao, A., Luan, H., Zhao, Z., Hong, Y., Zhao, J., Chen, W., Wang, J. and Meng, M. Q.-H.. Robotic Autonomous Trolley Collection with Progressive Perception and Nonlinear Model Predictive Control. In: 2022 IEEE International Conference on Robotics and Automation (ICRA) (2022) pp. 4480–4486.Google Scholar
Gilroy, S., Lau, D., Yang, L., Izaguirre, E., Biermayer, K., Xiao, A., Sun, M., Agrawal, A., Zeng, J., Li, Z. and Sreenath, K.. 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.Google Scholar
Jian, Z., Lu, Z., Zhou, X., Lan, B., Xiao, A., Wang, X. and Liang, B.. Putn: A Plane-fitting Based Uneven Terrain Navigation Framework. In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE (2022) pp. 7160–7166.Google Scholar
Xiao, A., Tong, W., Yang, L., Zeng, J., Li, Z. and Sreenath, K.. 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. 11470–11476.Google Scholar
Dolgov, D., Thrun, S., Montemerlo, M. and Diebel, J., “Practical search techniques in path planning for autonomous driving,” Ann. Arbor 1001(48105), 18–80 (2008).Google Scholar
Hu, Y., Cui, M., Duan, J., Liu, W., Huang, D., Knoll, A. and Chen, G., “Model predictive optimization for imitation learning from demonstrations,” Rob. Auton. Syst. 163, 104381 (2023).CrossRefGoogle Scholar
An, X., Wu, C., Lin, Y., Lin, M., Yoshinaga, T. and Ji, Y., “Multi-robot systems and cooperative object transport: Communications, platforms, and challenges,” IEEE Open J. Comput. Soc. 4, 23–36 (2023).CrossRefGoogle Scholar
Bohren, J., Rusu, R. B., Jones, E. G., Marder-Eppstein, E., Pantofaru, C., Wise, M., Mösenlechner, L., Meeussen, W. and Holzer, S.. Towards Autonomous Robotic Butlers: Lessons Learned with the PR2. In: 2011 IEEE International Conference on Robotics and Automation (2011) pp. 5568–5575.Google Scholar
Wise, M., Ferguson, M., King, D., Diehr, E. and Dymesich, D., Fetch & Freight: Standard Platforms for Service Robot Applications. In: Workshop on autonomous mobile service robots (2016) pp. 1–6.Google Scholar
Sirintuna, D., Giammarino, A. and Ajoudani, A.. Human-Robot Collaborative Carrying of Objects with Unknown Deformation Characteristics. In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2022) pp. 10681–10687.Google Scholar
Zhang, H., Sheng, Q., Hu, J., Sheng, X., Xiong, Z. and Zhu, X., “Cooperative transportation with mobile manipulator: A capability map-based framework for physical human-robot collaboration,” IEEE/ASME Trans. Mech. 27(6), 4396–4405 (2022).CrossRefGoogle Scholar
Hu, J., Liu, W., Zhang, H., Yi, J. and Xiong, Z., “Multi-robot object transport motion planning with a deformable sheet,” IEEE Rob. Autom. Lett. 7(4), 9350–9357 (2022).CrossRefGoogle Scholar
Wang, C., Mai, X., Ho, D., Liu, T., Li, C., Pan, J. and Meng, M. Q.-H., “Coarse-to-fine visual object catching strategy applied in autonomous airport baggage trolley collection,” IEEE Sens. J. 21(10), 11844–11857 (2021).CrossRefGoogle Scholar
Juang, C.-F., Lu, C.-H. and Huang, C.-A., “Navigation of three cooperative object-transportation robots using a multistage evolutionary fuzzy control approach,” IEEE Trans. Cybern. 52(5), 3606–3619 (2022).CrossRefGoogle ScholarPubMed
Hu, J., Bhowmick, P. and Lanzon, A., “Group coordinated control of networked mobile robots with applications to object transportation,” IEEE Trans. Veh. Technol. 70(8), 8269–8274 (2021).CrossRefGoogle Scholar
Koung, D., Kermorgant, O., Fantoni, I. and Belouaer, L., “Cooperative multi-robot object transportation system based on hierarchical quadratic programming,” IEEE Rob. Autom. Lett. 6(4), 6466–6472 (2021).CrossRefGoogle Scholar
Tsiamis, A., Bechlioulis, C. P., Karras, G. C. and Kyriakopoulos, K. J.. Decentralized Object Transportation by Two Nonholonomic Mobile Robots Exploiting Only Implicit Communication. In: 2015 IEEE International Conference on Robotics and Automation (ICRA) (2015) pp. 171–176.Google Scholar
Wang, Z. and Schwager, M.. Kinematic Multi-robot Manipulation with no Communication Using Force Feedback. In: 2016 IEEE International Conference on Robotics and Automation (ICRA) (2016) pp. 427–432.Google Scholar
Wang, Z. and Schwager, M.. Multi-Robot Manipulation with noCommunication Using Only Local Measurements. In: 2015 54th IEEE Conference on Decision and Control (CDC) (2015) pp. 380–385.Google Scholar
Hichri, B., Adouane, L., Fauroux, J. C., Mezouar, Y. and Doroftei, I., “Flexible co-manipulation and transportation with mobile multi-robot system,” Assem. Autom. 39(3), 422–431 (2019).CrossRefGoogle Scholar
Dellaert, F., Fox, D., Burgard, W. and Thrun, S.. Monte Carlo Localization for Mobile Robots. In: Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat No.99CH36288C), vol. 2 (1999) pp. 1322–1328.Google Scholar
Mur-Artal, R. and Tardós, J. D., “ORB-SLAM2: An open-source slam system for monocular, stereo, and RGB-D cameras,” IEEE Trans. Rob. 33(5), 1255–1262 (2017).CrossRefGoogle Scholar
Hart, P. E., Nilsson, N. J. and Raphael, B., “A formal basis for the heuristic determination of minimum cost paths,” IEEE Trans. Syst. Sci. Cybern. 4(2), 100–107 (1968).CrossRefGoogle Scholar
Reeds, J. and Shepp, L., “Optimal paths for a car that goes both forwards and backwards,” Pac. J. Math. 145(2), 367–393 (1990).CrossRefGoogle Scholar
Andersson, J. A. E., Gillis, J., Horn, G., Rawlings, J. B. and Diehl, M., “Casadi: A software framework for nonlinear optimization and optimal control,” Math. Program. Comput. 11, 1–36 (2018).CrossRefGoogle Scholar
Wächter, A. and Biegler, L. T., “On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming,” Math. Program. 106(1), 25–57 (2006).CrossRefGoogle Scholar
Agrawal, A. and Sreenath, K., “Discrete Control Barrier Functions for Safety-critical Control of Discrete Systems with Application to Bipedal Robot Navigation,” In: Robotics: Science and Systems 2017. vol. 13, Cambridge, MA, USA.Google Scholar
Ames, A. D., Coogan, S., Egerstedt, M., Notomista, G., Sreenath, K. and Tabuada, P.. Control Barrier Functions: Theory and Applications. In: 2019 18th European control conference (ECC), IEEE (2019) pp. 3420–3431.Google Scholar
Ames, A. D., Xu, X., Grizzle, J. W. and Tabuada, P., “Control barrier function based quadratic programs for safety critical systems,” IEEE Trans. Autom. Control 62(8), 3861–3876 (2016).CrossRefGoogle Scholar
Wang, L., Ames, A. D. and Egerstedt, M., “Safety barrier certificates for collisions-free multirobot systems,” IEEE Trans. Rob. 33(3), 661–674 (2017).CrossRefGoogle Scholar