Abstract
Modular robots are automated modules that can change their morphology self-sufficiently and progressively for control or reconfiguration purposes. Self-reconfiguration is a very challenging problem in modular robots systems. Existing algorithms are complex and not suitable for low resources devices. In this paper, we propose a parallel and fully distributed cluster-based algorithm to convert a set of blocks/modules in a new geometrical configuration. We proposed a cluster based self-reconfiguration algorithm. The main idea is to study the impact of clustering on the self-reconfiguration problem. The modules in each cluster remain together and try to move in order to find the final configuration. Based on this concept, our algorithm operates in parallel in each cluster to fasten reconfiguration process and ultimately the set of blocks will reach the desirable shape. We evaluate our algorithm on the centimeter-scale sliding blocks, developed in the Smart Blocks project in a simulator showing the entire reconfiguration process in real-time. We show the effectiveness of our algorithm, especially the benefits of clustering in self-reconfiguration task by comparing performance of both approaches (with and without clustering) while varying the number of clusters.
This work has been supported by the EIPHI Graduate School (contract ANR-17-EURE-0002).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alattas, R.J., Patel, S., Sobh, T.M.: Evolutionary modular robotics: survey and analysis. J. Intell. Rob. Syst. 95(3–4), 815–828 (2019)
Bassil, J., Moussa, M., Makhoul, A., Piranda, B., Bourgeois, J.: Linear distributed clustering algorithm for modular robots based programmable matter. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS (2020)
Fekete, S.P., Gmyr, R., Hugo, S., Keldenich, P., Scheffer, C., Schmidt, A.: Algorithmic aspects of manipulating programmable matter with finite automata. Algorithmica 83, 1–26 (2020)
Hall, M.D., Ozdemir, A., Groß, R.: Self-reconfiguration in two-dimensions via active subtraction with modular robots. In: Robotics: Science and System XVI (2020)
Hauser, S., Mutlu, M., Léziart, P.-A., Khodr, H., Bernardino, A., Ijspeert, A.J.: Roombots extended: challenges in the next generation of self-reconfigurable modular robots and their application in adaptive and assistive furniture. Rob. Auton. Syst. 127, 103467 (2020)
Kawano, H.: Complete reconfiguration algorithm for sliding cube-shaped modular robots with only sliding motion primitive. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3276–3283 (2015)
Kawano, H.: Tunneling-based self-reconfiguration of heterogeneous sliding cube-shaped modular robots in environments with obstacles. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 825–832 (2017)
Kawano, H.: Distributed Tunneling Reconfiguration of Sliding Cubic Modular Robots in Severe Space Requirements, p. 14 (2018)
Lengiewicz, J., Hołobut, P.: Efficient collective shape shifting and locomotion of massively-modular robotic structures. Auton. Rob. 43(1), 97–122 (2019)
Piranda, B., Laurent, G., Bourgeois, J., et al.: A new concept of planar self-reconfigurable modular robot for conveying microparts. Mechatronics 23(7), 906–915 (2013)
Piranda, B., Bourgeois, J.: A distributed algorithm for reconfiguration of lattice-based modular self-reconfigurable robots. In: 2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP), pp. 1–9. IEEE (2016)
Piranda, B., Fekete, S., Richa, A., Römer, K., Scheideler, C.: Your simulator for programmable matter. In: Algorithmic Foundations of Programmable Matter, Visiblesim (2016)
Shao, J., Yang, Q., Liu, J.,Kramer, S.: Graph clustering with density-cut. arXiv preprint (2016)
Thalamy, P., Piranda, B., Bourgeois, J.: A survey of autonomous self-reconfiguration methods for robot-based programmable matter. Rob. Auton. Syst. 120, 103242 (2019)
Yim, M., Shen, W.M., Salemi, B., Rus, D., Moll, M., Lipson, H., Klavins, E., Chirikjian, G.S.: Modular self-reconfigurable robot systems [grand challenges of robotics]. IEEE Rob. Autom. Mag 14(1), 43–52 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Moussa, M., Piranda, B., Makhoul, A., Bourgeois, J. (2021). Cluster-Based Distributed Self-reconfiguration Algorithm for Modular Robots. In: Barolli, L., Woungang, I., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2021. Lecture Notes in Networks and Systems, vol 225. Springer, Cham. https://doi.org/10.1007/978-3-030-75100-5_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-75100-5_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-75099-2
Online ISBN: 978-3-030-75100-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)