Abstract
Mobile manipulators are often comprised of an extensive kinematic chain resulting from an industrial robot mounted on top of an autonomous mobile robot. In such a way, the system not only retains the parameters embedded in the two sub-systems, hence DH parameters for the industrial robot and odometry parameters for the mobile robot, but also includes the relative transformation between the two parts and an additional transformation for a camera mounted on the kinematic chain.In this complex setup, it is relatively simple to introduce kinematic inaccuracies, or in some cases, to operate the system in such a way that the kinematic parameters vary(e.g., rubber wheels on high payload).Estimating the values of such parameters might be too demanding for the on-board computing system.In this work, we propose a cloud-based visually aided parameter estimation method, which constantly receives data from the mobile manipulator and generates better estimates of the kinematic parameters through an UKF dual estimation.The overall system architecture is presented to the reader, together with the reasons for relying to a cloud based paradigm, for then giving a theoretical analysis and real world experiments and results.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
For the nomenclature and acronym, refer to RiA R15.08 where Autonomous Mobile Robots (AMR), Industrial Mobile Manipulator (IMM), Autonomous Guided Vehicle (AGV) etc. are defined.
- 2.
The unscented transformation is a method to compute the evolution of a random variable through a nonlinear map.
References
Wang, R., Wu, A., Chen, X., Wang, J.: A point and distance constraint based 6r robot calibration method through machine vision. Robot. Comput. Integr. Manuf. 65, 101959 (2020)
Özgüner, O., et al.: Camera-robot calibration for the Da Vinci robotic surgery system. IEEE Trans. Autom. Sci. Eng. 17(4), 2154–2161 (2020)
Shah, M., Bostelman, R., Legowik, S., Hong, T.: Calibration of mobile manipulators using 2D positional features. Measurement 124, 322–328 (2018)
Zhou, Z., Li, L., Wang, R., Zhang, X.: Experimental eye-in-hand calibration for industrial mobile manipulators. In: 2020 IEEE International Conference on Mechatronics and Automation (ICMA), pp. 582–587. IEEE (2020)
Xuan, J.-Q., Xu, S.-H., et al.: Review on kinematics calibration technology of serial robots. Int. J. Precis. Eng. Manuf. 15(8), 1759–1774 (2014)
Bostelman, R., Hong, T., Marvel, J.: Survey of research for performance measurement of mobile manipulators. J. Res. Nat. Inst. Stand. Technol. 121, 342 (2016)
Yang, M., Yang, E., Zante, R.C., Post, M., Liu, X.: Collaborative mobile industrial manipulator: a review of system architecture and applications. In: 2019 25th International Conference on Automation and Computing (ICAC), pp. 1–6. IEEE (2019)
Huang, Z., Wang, Q.: Industrial robot control system optimized by wireless resources and cloud resources based on cloud edge multi-cluster containers. Int. J. Syst. Assur. Eng. Manage. 1–10 (2021). https://doi.org/10.1007/s13198-021-01254-0
Vick, A., Vonásek, V., Pěnička, R., Krüger, J.: Robot control as a service—towards cloud-based motion planning and control for industrial robots. In: 2015 10th International Workshop on Robot Motion and Control (RoMoCo), pp. 33–39. IEEE (2015)
Dey, S., Mukherjee, A.: Robotic SLAM: a review from fog computing and mobile edge computing perspective. In: Adjunct Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing Networking and Services, pp. 153–158 (2016)
Tzafestas, S.G.: Mobile robot control and navigation: a global overview. J. Intell. Robot. Syst. 91(1), 35–58 (2018). https://doi.org/10.1007/s10846-018-0805-9
Dyumin, A., Puzikov, L., Rovnyagin, M., Urvanov, G., Chugunkov, I.: Cloud computing architectures for mobile robotics. In: 2015 IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW), pp. 65–70. IEEE (2015)
Saha, O., Dasgupta, P.: A comprehensive survey of recent trends in cloud robotics architectures and applications. Robotics 7(3), 47 (2018)
Li, S., Zheng, Z., Chen, W., Zheng, Z., Wang, J.: Latency-aware task assignment and scheduling in collaborative cloud robotic systems. In: 2018 IEEE 11th International Conference on Cloud Computing (CLOUD), pp. 65–72. IEEE (2018)
Shukla, S., Hassan, M.F., Tran, D.C., Akbar, R., Paputungan, I.V., Khan, M.K.: Improving latency in Internet-of-Things and cloud computing for real-time data transmission: a systematic literature review (SLR). Clust. Comput. 1–24 (2021). https://doi.org/10.1007/s10586-021-03279-3
Cesen, F.E.R., Csikor, L., Recalde, C., Rothenberg, C.E., Pongrácz, G.: Towards low latency industrial robot control in programmable data planes. In: 2020 6th IEEE Conference on Network Softwarization (NetSoft), pp. 165–169. IEEE (2020)
Mutti, S., Pedrocchi, N.: Improved tracking and docking of industrial mobile robots through UKF vision-based kinematics calibration. IEEE Access 9, 127664–127671 (2021)
Wan, E.A., Van Der Merwe, R.: The unscented Kalman filter for nonlinear estimation. In: Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No. 00EX373), pp. 153–158. IEEE (2000)
Fiorenzani, T., Manes, C., Oriolo, G., Peliti, P.: Comparative study of unscented Kalman filter and extended kalman filter for position/attitude estimation in unmanned aerial vehicles. In: Institute for Systems Analysis and Computer Science (IASI-CNR), Rome, Italy, Report, p. 08 (2008). http://www.iasi.cnr.it/new/publications.php/id_p/2/anno/0/id_autore/0/id_tipologia/6/rep/3459. http://www.iasi.cnr.it/ResearchReports/R08008
Julier, S., Uhlmann, J., Durrant-Whyte, H.F.: A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Tranans. Autom. control 45(3), 477–482 (2000)
Barfoot, T.D., Furgale, P.T.: Associating uncertainty with three-dimensional poses for use in estimation problems. IEEE Trans. Robot. 30(3), 679–693 (2014)
Stanford Artificial Intelligence Laboratory et al.: Robotic operating system. www.ros.org
Bradski, G.: The OpenCV library. Dr. Dobb’s J. Softw. Tools 25, 120–123 (2000)
Labbe, R.: filterpy. https://github.com/rlabbe/filterpy
Van Der Merwe, R.: Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models. Oregon Health and Science University (2004)
Park, F.C., Martin, B.J.: Robot sensor calibration: solving AX = XB on the Euclidean group. IEEE Tranans. Robot. Autom. 10(5), 717–721 (1994)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Mutti, S., Renò, V., Nitti, M., Dimauro, G., Pedrocchi, N. (2022). Cloud-Based Visually Aided Mobile Manipulator Kinematic Parameters Calibration. In: Mazzeo, P.L., Frontoni, E., Sclaroff, S., Distante, C. (eds) Image Analysis and Processing. ICIAP 2022 Workshops. ICIAP 2022. Lecture Notes in Computer Science, vol 13373. Springer, Cham. https://doi.org/10.1007/978-3-031-13321-3_23
Download citation
DOI: https://doi.org/10.1007/978-3-031-13321-3_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-13320-6
Online ISBN: 978-3-031-13321-3
eBook Packages: Computer ScienceComputer Science (R0)