Choice of Kinematic Structure of Modular Robotic System Depending on the Type of Motion Surface

Purpose of research. The purpose of this study is to identify the advantages and disadvantages of various kinematic structures (formations) of self-reconfigurable modular robotic systems depending on the type of surface over which the system is mainly to be moved.
Methods. Analysis of kinematic structures of modular robotic systems with respect to their displacement capabilities was carried out. Features of movement of these formations on different surfaces, as well as features of application of some formations are highlighted. A study of functionality of its own mobile autonomous reconfigurable system was carried out on the basis of described formations.
Results. According to the results of the study, the main structures of modular robotic systems were identified, among which the most popular are formations that have a chain architecture: "snake," "manipulator"; "walking" structures: "quadropod," "spider," "centipede," as well as mobile structures "machine," "wheel" and "ball." Based on the conducted analysis, structures were developed for their own modular robotics system. Geometric features and kinematic limitations of its modules were considered in developing the structures
Conclusion. The use of the analysis results will allow better adaptation of a modular self-reconfigurable robotic system to the surface on which this system moves. The selection of a particular formation of the modular system is also influenced by the required speed of movement over a given surface. Walking formations have the greatest adaptability to various types of surfaces, but they are also the most difficult from the point of view of control system developing.

1. Pavliuk N., Saveliev A., Cherskikh E., Pykhov D. Formation of modular structures with mobile autonomous reconfigurable system. In Proceedings of 14th International Conference on Electromechanics and Robotics “Zavalishin's Readings”. 2020; Springer, Singapore, pp. 383-395. https://doi.org/10.1007/978-981-13-9267-2_31.

2. 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. Robotics and Autonomous Systems. 2020; 127: 103467. https://doi.org/10.1016/j.robot.2020.103467.

3. Brunete A., Ranganath A., Segovia S., de Frutos J. P., Hernando, M., Gambao, E. Current trends in reconfigurable modular robots design. International Journal of Advanced Robotic Systems. 2017; 14(3): 1729881417710457. https://doi.org/10.1177/1729881417710457.

4. Jones A. B., Cameron T., Eichholz B., Loegering D., Kray T., Straub J. Selfreconfiguring modular robot learning for lower-cost space applications. In 2019 IEEE Aerospace Conference. 2019; pp. 1-6. https://doi.org/10.1109/AERO.2019.8742133.

5. Yim M., White P. J., Park M., Sastra J. Modular Self-Reconfigurable Robots. 2009.

6. Pavliuk N. A., Krestovnikov K. D., Pykhov D. E. Mobile autonomous reconfigurable system. Problemele energeticii regionale. 2018; 1: 125-135. DOI: 10.5281/zenodo.1217296.

7. Chennareddy S., Agrawal A., Karuppiah A. Modular self-reconfigurable robotic systems: a survey on hardware architectures. Journal of Robotics, 2017, https://doi.org/10.1155/2017/5013532.

8. Dorigo M., Tuci E., Groß R., Trianni V., Labella T. H., Nouyan S., ... Gambardella, L. M. The swarm-bots project. In International Workshop on Swarm Robotics. 2004; Springer, Berlin, Heidelberg, pp. 31-44. https://doi.org/10.1007/978-3-540-30552-1_4.

9. Mondada F., Gambardella L. M., Floreano D., Nolfi S., Deneuborg J. L., Dorigo M. The cooperation of swarm-bots: Physical interactions in collective robotics. IEEE Robotics & Automation Magazine. 2005; 12(2): 21-28. https://doi.org/10.1109/MRA.2005.1458313.

10. Davey J., Kwok N., Yim M. Emulating self-reconfigurable robots-design of the SMORES system. In 2012 IEEE/RSJ international conference on intelligent robots and systems. 2012; IEEE, pp. 4464-4469. https://doi.org/10.1109/IROS.2012.6385845.

11. Tosun T., Davey J., Liu C., Yim M. Design and characterization of the ep-face connector. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2016; IEEE, pp. 45-51. https://doi.org/10.1109/IROS.2016.7759033.

12. Daudelin J., Jing G., Tosun T., Yim M., Kress-Gazit H., Campbell M. An integrated system for perception-driven autonomy with modular robots. Science Robotics. 2018; 3(23). https://doi.org/10.1126/scirobotics.aat4983.

13. Jing G., Tosun T., Yim M., Kress-Gazit H. An End-To-End System for Accomplishing Tasks with Modular Robots. In Robotics: Science and systems. 2016; 2: p. 7.

14. Li Y., Zhu S., Wang Z., Zhang L., Ma X., Cui Z. The kinematics analysis of a novel self-reconfigurable modular robot based on screw theory. DEStech Transactions on Engineering and Technology Research. 2016. DOI: 10.12783/dtetr/mime2016/10196.

15. Jing G., Tosun T., Yim M., Kress-Gazit H. Accomplishing high-level tasks with modular robots. Autonomous Robots. 2018; 42(7): 1337-1354. https://doi.org/10.1007/s10514-018-9738-1.

16. Pacheco M., Fogh R., Lund H. H., Christensen D. J. Fable II: Design of a modular robot for creative learning. In 2015 IEEE International Conference on Robotics and Automation (ICRA). 2015; IEEE, pp. 6134-6139. https://doi.org/10.1109/ICRA.2015.7140060.

17. Patlolla S., Agrawal A., KR A. HexaMob—a hybrid modular robotic design for implementing biomimetic structures. Robotics. 2017; 6(4): 27. https://doi.org/10.3390/robotics6040027.

18. Sastra J., Chitta S., Yim M. Dynamic rolling for a modular loop robot. The International Journal of Robotics Research. 2009; 28(6): 758-773. https://doi.org/10.1177%2F0278364908099463.

19. Wei X., Tian Y., Wen S. Design and locomotion analysis of a novel modular rolling robot. Mechanism and Machine Theory. 2019; 133: 23-43. https://doi.org/10.1016/j.mechmachtheory.2018.11.004.

20. Yuan Q., Wang J. Design and Experiment of the NAO Humanoid Robot's Plantar Tactile Sensor for Surface Classification. In 2017 4th International Conference on Information Science and Control Engineering (ICISCE). 2017; IEEE, pp. 931-935. https://doi.org/10.1109/ICISCE.2017.197.

21. Rubtsova J. Approach to Image-Based Segmentation of Complex Surfaces Using Machine Learning Tools During Motion of Mobile Robots. In Electromechanics and Robotics. 2022; Springer, Singapore, pp. 191-200. (in Press). https://doi.org/10.1007/978-981-16-2814-6_17.

22. Pavliuk N., Smirnov P., Kondratkov A., Ronzhin A. Connecting gripping mechanism based on iris diaphragm for modular autonomous robots. In International Conference on Interactive Collaborative Robotics. 2019; Springer, Cham, pp. 260-269. https://doi.org/10.1007/978-3-030-26118-4_25.