A vision-based scheme for kinematic model construction of re-configurable modular robots | IEEE Conference Publication | IEEE Xplore

A vision-based scheme for kinematic model construction of re-configurable modular robots


Abstract:

Re-configurable modular robotic (RMR) systems are advantageous for their reconfigurability and versatility. A new modular robot can be built for a specific task by using ...Show More

Abstract:

Re-configurable modular robotic (RMR) systems are advantageous for their reconfigurability and versatility. A new modular robot can be built for a specific task by using modules as building blocks. However, constructing a kinematic model for a newly conceived robot requires significant work. Due to the finite size of module-types, models of all module-types can be built individually and stored in a database beforehand. With this priori knowledge, the model construction process can be automated by detecting the modules and their corresponding interconnections. Previous literature proposed theoretical frameworks for constructing kinematic models of modular robots, assuming that such information was known a priori. While well-devised mechanisms and built-in sensors can be employed to detect these parameters, they significantly complicate the module design and thus are expensive. In this paper, we propose a vision-based method to identify kinematic chains and automatically construct robot models for modular robots. Each module is affixed with augmented reality (AR) tags that are encoded with unique IDs. An image of a modular robot is taken and the detected modules are recognized by querying a database that maintains all module information. The poses of detected module-links are used to compute: (i) the connection between modules and (ii) joint angles of joint-modules. Finally, the robot serial-link chain is identified and the kinematic model is constructed and visualized. Our experimental results validate the effectiveness of our approach. While implementation with only our RMR is shown, our method can be applied to other RMRs where self-identification is not possible.
Date of Conference: 24-28 September 2017
Date Added to IEEE Xplore: 14 December 2017
ISBN Information:
Electronic ISSN: 2153-0866
Conference Location: Vancouver, BC, Canada
References is not available for this document.

I. Introduction

Modular robotic systems (MRSs) use robotic modules as building blocks to create a variety of kinematic configurations, see Fig. 1. Each module is an independent mechatronic subsystem with a single function. Modules can be of different types. A new kinematic configuration can be specifically selected for a given task and built with modules of different types. This system design principle brings great versatility and flexibility. Throughout this paper, the phrase “kinematic configuration” refers to the robot morphology, whereas “configuration” indicates the robot state.

Select All
1.
A. Sprowitz, R. Moeckel, M. Vespignani, S. Bonardi and A.J. Ijspeert, "Roombots: A hardware perspective on 3d self-reconfiguration and locomotion with a homogeneous modular robot", Robotics and Autonomous Systems, vol. 62, no. 7, pp. 1016-1033, 2014.
2.
H. Kurokawa, K. Tomita, A. Kamimura, S. Kokaji, T. Hasuo and S. Murata, "Distributed self-reconfiguration of m-tran iii modular robotic system", The International Journal of Robotics Research, vol. 27, pp. 373-386, 2008.
3.
Y. Guan, L. Jiang and X. Zhang, "Development of novel robots with modular methodology", IEEE/rsj International Conference on Intelligent Robots and Systems, pp. 2385-2390, 2009.
4.
I.M. Chen, H.Y. Song, G. Chen and G. Yang, "Kernel for modular robot applications: Automatic modeling techniques", International Journal of Robotics Research, vol. 18, no. 2, pp. 225-242, 1999.
5.
R. Bischoff, J. Kurth, G. Schreiber, R. Koeppe, A. Albu-Schaeffer, A. Beyer, et al., "The kuka-dlr lightweight robot arm — a new reference platform for robotics research and manufacturing", Isr/robotik 2010 Proceedings for the Joint Conference of Isr, pp. 1-8, 2010.
6.
J. Baca, S.G.M. Hossain, P. Dasgupta, C.A. Nelson and A. Dutta, "Modred: Hardware design and reconfiguration planning for a high dexterity modular self-reconfigurable robot for extra-terrestrial exploration", Robotics & Autonomous Systems, vol. 62, no. 7, pp. 1002-1015, 2014.
7.
M. Yim, W.M. Shen, B. Salemi and D. Rus, "Modular self-reconfigurable robot systems [grand challenges of robotics]", IEEE Robotics & Automation Magazine, vol. 14, no. 1, pp. 43-52, 2007.
8.
M. Bordignon, K. Stoy and U.P. Schultz, "Generalized programming of modular robots through kinematic configurations", IEEE/rsj International Conference on Intelligent Robots and Systems IROS 2011, pp. 3659-3666, September, 2011.
9.
T. Collins and W.-M. Shen, Rebots: A drag-and-drop highperformance simulator for modular and self-reconfigurable robots, 2016.
10.
M. Park, S. Chitta, A. Teichman and M. Yim, "Automatic configuration recognition methods in modular robots", The International Journal of Robotics Research, vol. 27, no. 3–4, pp. 403-421, 2008.
11.
S. Niekum, ar_track_alvar — github repository, [online] Available: https://github.com/sniekum/ar_track_alvar.
12.
Y. Guan, X. Shi, X. Zhou, X. Zhang and H. Zhang, "A novel mobile robot capable of changing its wheel distance and body configuration", International Conference on Robotics and Biomimetics, pp. 806-811, 2009.
13.
Y. Guan, H. Zhu, W. Wu, X. Zhou, L. Jiang, C. Cai, et al., "A modular biped wall-climbing robot with high mobility and manipulating function", IEEE/ASME Transactions on Mechatronics, vol. 18, no. 6, pp. 1787-1798, 2013.
14.
P. Beeson and B. Ames, "Trac-ik: An open-source library for improved solving of generic inverse kinematics", IEEE RAS Humanoids Conference, pp. 928-935, 2015.
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References

References is not available for this document.