Hostname: page-component-76fb5796d-vfjqv Total loading time: 0 Render date: 2024-04-29T08:39:54.024Z Has data issue: false hasContentIssue false

Development of the “Quad-SCARA” platform and its collision avoidance based on Buffered Voronoi Cell

Published online by Cambridge University Press:  18 September 2023

Xiao Sun*
Affiliation:
Department of Mechatronics, University of Yamanashi, Yamanashi, Japan
Kazuyoshi Ishida
Affiliation:
Department of Mechatronics, University of Yamanashi, Yamanashi, Japan
Koji Makino
Affiliation:
Department of Mechatronics, University of Yamanashi, Yamanashi, Japan
Kotaro Shibayama
Affiliation:
Department of Mechatronics, University of Yamanashi, Yamanashi, Japan
Hidetsugu Terada
Affiliation:
Department of Mechatronics, University of Yamanashi, Yamanashi, Japan
*
Corresponding author: Xiao Sun; Email: xsun@yamanashi.ac.jp

Abstract

As a solution to soft material manipulation, dual-arm robots that are capable of multi-point grasping have become the latest trend due to their higher capability and efficiency. To explore further development in soft material manipulation and go beyond the hardware limit of dual arms, in this paper the robot platform “Quad-SCARA” that consists of four Selective Compliance Assembly Robot Arm (SCARA) is developed and introduced. With the base of hardware platform, a novel collision avoidance system designed for the motion planning of Quad-SCARA in multi-point grasping state is proposed. This system is inspired by the idea of “Buffered Voronoi Cell (BVC),” an algorithm originally proposed for multi-agent collision avoidance. After appropriate adaptation and new proposition in implementation of BVC, the experiments of folding and spreading a handkerchief are performed, and the results in simulation and robot experiments are presented and discussed for the validation and evaluation of entire system.

Type
Research Article
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

Wu, Y., Yan, W., Kurutach, T., Pinto, L. and Abbeel, P., “Learning to Manipulate Deformable Objects without Demonstrations,” In: Proceedings of Robotics: Science and Systems (2020).Google Scholar
Seita, D., Ganapathi, A., Hoque, R., Hwang, M., Cen, E., Tanwani, A. K., Balakrishna, A., Thananjeyan, B., Ichnowski, J., Jamali, N., Yamane, K., Iba, S., Canny, J. and Goldberg, K., “Deep Imitation Learning of Sequential Fabric Smoothing from an Algorithmic Supervisor,” In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2020) pp. 96519658.10.1109/IROS45743.2020.9341608CrossRefGoogle Scholar
Lee, R., Ward, D., Dasagi, V., Cosgun, A., Leitner, J. and Corke, P., “Learning Arbitrary-Goal Fabric Folding with One Hour of Real Robot Experience,” In: Conference on Robot Learning (CoRL) (2020) pp. 23172327.Google Scholar
Hietala, J., Blanco-Mulero, D., Alcan, G. and Kyrki, V., “Learning Visual Feedback Control for Dynamic Cloth Folding,” In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2022) pp. 14551462.10.1109/IROS47612.2022.9981376CrossRefGoogle Scholar
Berkeley News, Meet Blue, the low cost, human friendly robot designed for AI. https://news.berkeley.edu/2019/04/09/meet-blue-the-low-cost-human-friendly-robot-designed-for-ai/ Google Scholar
Tsurumine, Y., Cui, Y., Uchibe, E. and Matsubara, T., “Deep reinforcement learning with smooth policy update: Application to robotic cloth manipulation,” Robot. Auton. Syst. 112, 7283 (2019).CrossRefGoogle Scholar
Hu, Z., Han, T., Sun, P., Pan, J. and Manocha, D., “3-D deformable object manipulation using deep neural networks,” IEEE Robot. Autom. Lett. 4(4), 42554261 (2019).CrossRefGoogle Scholar
Garcia-Camacho, I., Lippi, M., Welle, M. C., Yin, H., Antonova, R., Varava, A., Borras, J., Torras, C., Marino, A., Alenya, G. and Kragic, D., “Benchmarking bimanual cloth manipulation,” IEEE Robot. Autom. Lett. 5(2), 11111118 (2020).10.1109/LRA.2020.2965891CrossRefGoogle Scholar
Ha, H. and Song, S., “Flingbot: The Unreasonable Effectiveness of Dynamic Manipulation for Cloth Unfolding,” In: Conference on Robot Learning (CoRL) (2022) pp. 2433.Google Scholar
Weng, T., Bajracharya, S., Wang, Y., Agrawal, K. and Held, D., “Fabricflownet: Bimanual Cloth Manipulation with a Flow-Based Policy,” In: Conference on Robot Learning (CoRL) (2022) pp. 192202.Google Scholar
Avigal, Y., Berscheid, L., Asfour, T., Kröger, T. and Goldberg, K., “Speedfolding: Learning Efficient Bimanual Folding of Garments,” In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2022) pp. 18.Google Scholar
Sun, X., Ishida, K., Makino, K., Shibayama, K. and Terada, H., “End-Effector Collision Avoidance System for Four SCARA Robots Using Buffered Voronoi Cell,” In: Advances in Italian Mechanism Science. IFToMM Italy 2022. Mechanisms and Machine Science (Niola, V., Gasparetto, A., Quaglia, G. and Carbone, G., eds.), vol. 122 (Springer, Cham, 2022).Google Scholar
Decoupled Motion and Force Control for Underactuated Robots: Multi-Arm Manipulation. https://cogimon.eu/tubs-uedin-unibi-decoupled-motion-and-force-control-underactuated-robots Google Scholar
Seriani, S., Gallina, P., Scalera, L. and Lughi, V., “Development of n-DoF preloaded structures for impact mitigation in cobots,” J. Mech. Robot. 10(5), 112 (2018).10.1115/1.4040632CrossRefGoogle Scholar
Scalera, L., Vidoni, R. and Giusti, A., “Optimal Scaling of Dynamic Safety Zones for Collaborative Robotics,” In: 2021 IEEE International Conference on Robotics and Automation (ICRA) (2021) pp. 38223828.CrossRefGoogle Scholar
Scalera, L., Giusti, A., Vidoni, R. and Gaspareto, A., “Enhancing fluency and productivity in human-robot collaboration through online scaling of dynamic safety zones,” Int. J. Adv. Manuf. Technol. 121(9-10), 67836798 (2022).10.1007/s00170-022-09781-1CrossRefGoogle Scholar
Beckert, D., Pereira, A. and Althoff, M., “Online Verification of Multiple Safety Criteria for a Robot Trajectory,” In: 2017 IEEE 56th Annual Conference on Decision and Control (CDC) (2017) pp. 64546461.10.1109/CDC.2017.8264632CrossRefGoogle Scholar
Zhou, D., Wang, Z., Bandyopadhyay, S. and Schwager, M., “Fast, on-line collision avoidance for dynamic vehicles using Buffered Voronoi Cells,” IEEE Robot. Autom. Lett. 2(2), 10471054 (2017).10.1109/LRA.2017.2656241CrossRefGoogle Scholar
Wang, M. and Schwager, M., “Distributed Collision Avoidance of Multiple Robots with Probabilistic Buffered Voronoi Cells,” In: 2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS) (2019) pp. 169175.10.1109/MRS.2019.8901101CrossRefGoogle Scholar
Pierson, A., Schwarting, W., Karaman, S. and Rus, D., “Weighted Buffered Voronoi Cells for Distributed Semi-Cooperative Behavior,” In: 2020 IEEE International Conference on Robotics and Automation (ICRA) (2020) pp. 56115617.10.1109/ICRA40945.2020.9196686CrossRefGoogle Scholar
Abdullhak, M. and Vardy, A., “Deadlock Prediction and Recovery for Distributed Collision Avoidance with Buffered Voronoi Cells,” In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2021) pp. 429436.CrossRefGoogle Scholar
Zhu, H., Brito, B. and Alonso-Mora, J., “Decentralized probabilistic multi-robot collision avoidance using buffered uncertainty-aware Voronoi cells,” Auton. Robot. 46(2), 401420 (2022).10.1007/s10514-021-10029-2CrossRefGoogle Scholar
van den Berg, J., Lin, M. and Manocha, D., “Reciprocal Velocity Obstacles for Real-Time Multi-agent Navigation,” In: 2008 IEEE International Conference on Robotics and Automation (ICRA) (2008) pp. 19281935.10.1109/ROBOT.2008.4543489CrossRefGoogle Scholar
van den Berg, J., Guy, S. J., Lin, M. and Manocha, D., “Reciprocal N-body Collision Avoidance,” In: 14th International Symposium of Robotic Research (ISRR) (2011) pp. 319.Google Scholar
Okabe, A., Boots, B., Sugihara, K. and Chiu, S. N., Spatial Tessellations: Concepts and Applications of Voronoi Diagrams, vol. 501 (John Wiley & Sons, Ltd., New York, 2009).Google Scholar
Supplementary material: File

Sun et al. supplementary material

Sun et al. supplementary material

Download Sun et al. supplementary material(File)
File 49.1 MB