ARGoS; Computer simulations; E-puck2; Sensors; Swarm robotic; Design and analysis; Graphical representations; One-time; Plug-ins; Robot modeling; Robotic simulator; Sensors and actuators; Swarm robotics; Control and Systems Engineering; Software; Computer Science Applications; General Mathematics; HPC
Abstract :
[en] In this article we present a new plug-in for the ARGoS swarm robotic simulator to implement the E-Puck2 robot model, including its graphical representation, sensors and actuators. We have based our development on the former E-Puck robot model (version 1) by upgrading the existing sensors (proximity, light, ground, camera, and battery) and adding new ones (time of flight and simulated encoders) implemented from scratch. We have adapted the values produced by the proximity, light and ground sensors, including the E-Puck2's onboard camera according to its resolution, and proposed four new discharge models for the battery. We have evaluated this new plug-in in terms of accuracy and efficiency through comparisons with real robots and extensive simulations. In all our experiments the proposed plug-in has worked well showing high levels of accuracy. The observed increment of execution times when using the studied sensors varies according to the number of robots and types of sensors included in the simulation, ranging from a negligible impact to 53% longer simulations in the most demanding cases.
Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > PCOG - Parallel Computing & Optimization Group
Disciplines :
Computer science
Author, co-author :
STOLFI ROSSO, Daniel ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > PCOG
DANOY, Grégoire ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
External co-authors :
no
Language :
English
Title :
Design and analysis of an E-Puck2 robot plug-in for the ARGoS simulator
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Bibliography
Symeonidis, C., Nikolaidis, N., Chapter 18 - simulation environments. Iosifidis, A., Tefas, A., (eds.) Deep Learning for Robot Perception and Cognition, 2022, Academic Press, 461–490, 10.1016/B978-0-32-385787-1.00023-3.
Yoo, Y.-H., Ahmed, M., Bartsch, S., Kirchner, F., Realistic simulation of extraterrestrial legged robot in trade-off between accuracy and simulation time. IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society, 2010, 1603–1608, 10.1109/IECON.2010.5675443.
Pitonakova, L., Giuliani, M., Pipe, A., Winfield, A., Feature and performance comparison of the V-REP, gazebo and argos robot simulators. Giuliani, M., Assaf, T., Giannaccini, M.E., (eds.) Towards Autonomous Robotic Systems, 2018, Springer International Publishing, Cham, 357–368.
Pinciroli, C., Trianni, V., O'Grady, R., Pini, G., Brutschy, A., Brambilla, M., Mathews, N., Ferrante, E., Di Caro, G., Ducatelle, F., Birattari, M., Gambardella, L.M., Dorigo, M., ARGoS: A modular, parallel, multi-engine simulator for multi-robot systems. Swarm Intell. 6:4 (2012), 271–295, 10.1007/s11721-012-0072-5.
Rohmer, E., Singh, S.P.N., Freese, M., V-REP: A versatile and scalable robot simulation framework. 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013, 1321–1326, 10.1109/IROS.2013.6696520.
N. Koenig, A. Howard, Design and use Paradigms for Gazebo, an Open-Source Multi-Robot Simulator, in: 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566), Vol. 3, IEEE, pp. 2149–2154, http://dx.doi.org/10.1109/IROS.2004.1389727.
Mondada, F., Bonani, M., Raemy, X., Pugh, J., Cianci, C., Klaptocz, A., Magnenat, S., Zufferey, J.-C., Floreano, D., Martinoli, A., The E-puck, a robot designed for education in engineering. Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions, Vol. 1, 2009, IPCB: Instituto Politécnico de Castelo Branco, 59–65.
Gautam, A., Saxena, A.U., Mall, P., Mohan, S., Positioning multiple mobile robots for geometric pattern formation: An empirical analysis. 2014 Seventh International Conference on Contemporary Computing (IC3), 2014, 607–612, 10.1109/IC3.2014.6897242.
Khaldi, B., Cherif, F., Swarm robots circle formation via a virtual viscoelastic control model. 2016 8th International Conference on Modelling, Identification and Control (ICMIC), 2016, 725–730, 10.1109/ICMIC.2016.7804207.
Nemec, D., Janota, A., Hruboš, M., Gregor, M., Pirník, R., Mutual acoustic identification in the swarm of E-puck robots. Int. J. Adv. Robot. Syst., 14(3), 2017, 1729881417710794, 10.1177/1729881417710794.
Shayestegan, M., Din, S., Fuzzy logic controller for robot navigation in an unknown environment. 2013 IEEE International Conference on Control System, Computing and Engineering, 2013, 69–73, 10.1109/ICCSCE.2013.6719934.
Noormohammadi-Asl, A., Saffari, M., Teshnehlab, M., Neural control of mobile robot motion based on feedback error learning and mimetic structure. Electrical Engineering (ICEE), Iranian Conference on, 2018, 778–783, 10.1109/ICEE.2018.8472657.
Zhang, W.-A., Yang, X., Yu, L., Liu, S., Sequential fusion estimation for RSS-based mobile robots localization with event-driven WSNs. IEEE Trans. Ind. Inform. 12:4 (2016), 1519–1528, 10.1109/TII.2016.2585350.
Almasri, M.M., Alajlan, A.M., Elleithy, K.M., Trajectory planning and collision avoidance algorithm for mobile robotics system. IEEE Sens. J. 16:12 (2016), 5021–5028, 10.1109/JSEN.2016.2553126.
Kargar Tasooji, T., Marquez, H.J., Cooperative localization in mobile robots using event-triggered mechanism: Theory and experiments. IEEE Trans. Autom. Sci. Eng. 19:4 (2022), 3246–3258, 10.1109/TASE.2021.3115770.
Wulandari, T., Arrazi, M.H., Priandana, K., Development of landslide victim detection system using thermal imaging and histogram of oriented gradients on E-Puck2 robot. 2020 International Conference on Computer Science and Its Application in Agriculture (ICOSICA), 2020, 1–6, 10.1109/ICOSICA49951.2020.9243244.
Tasooji, T.K., Marquez, H.J., Decentralized event-triggered cooperative localization in multirobot systems under random delays: With/without timestamps mechanism. IEEE/ASME Trans. Mechatronics, 2022, 1–13, 10.1109/TMECH.2022.3203439.
Stolfi, D.H., Danoy, G., Optimising autonomous robot swarm parameters for stable formation design. Proceedings of the Genetic and Evolutionary Computation Conference, GECCO ’22, 2022, Association for Computing Machinery, New York, NY, USA, 1281–1289, 10.1145/3512290.3528709.
Floreano, D., Mitri, S., Hubert, J., E-puck. Nolfi, S., Mirolli, M., (eds.) Evolution of Communication and Language in Embodied Agents, 2010, Springer Berlin Heidelberg, Berlin, Heidelberg, 303–306, 10.1007/978-3-642-01250-1_19.
Mitri, S., Floreano, D., Keller, L., Evolutionary conditions for the emergence of communication. Nolfi, S., Mirolli, M., (eds.) Evolution of Communication and Language in Embodied Agents, 2010, Springer Berlin Heidelberg, Berlin, Heidelberg, 123–134, 10.1007/978-3-642-01250-1_8.
Adam, Y.M., Binti Sariff, N., Algeelani, N.A., E-puck mobile robot obstacles avoidance controller using the fuzzy logic approach. 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2021, 107–112, 10.1109/ICSCEE50312.2021.9497939.
Lopes, H.J.M., Lima, D.A., Cellular automata in path planning navigation control applied in surveillance task using the E-puck architecture. 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2020, 1117–1122, 10.1109/SMC42975.2020.9283048.
Millard, A.G., Joyce, R., Hilder, J.A., Fleeriu, C., Newbrook, L., Li, W., McDaid, L.J., Halliday, D.M., The pi-puck extension board: A raspberry pi interface for the E-puck robot platform. 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017, 741–748, 10.1109/IROS.2017.8202233.
Allen, J.M., Joyce, R., Millard, A.G., Gray, I., The pi-puck ecosystem: Hardware and software support for the E-puck and E-Puck2. Dorigo, M., Stützle, T., Blesa, M.J., Blum, C., Hamann, H., Heinrich, M.K., Strobel, V., (eds.) Swarm Intelligence, 2020, Springer International Publishing, Cham, 243–255.
Strobel, V., Castelló Ferrer, E., Dorigo, M., Blockchain technology secures robot swarms: A comparison of consensus protocols and their resilience to Byzantine robots. Front. Robot. AI, 7, 2020, 10.3389/frobt.2020.00054.
Neale, A., Millard, A.G., Integration and robustness analysis of the buzz swarm programming language with the pi-puck robot platform. Pacheco-Gutierrez, S., Cryer, A., Caliskanelli, I., Tugal, H., Skilton, R., (eds.) Towards Autonomous Robotic Systems, 2022, Springer International Publishing, Cham, 223–237.
L. Garattoni, G. Francesca, A. Brutschy, C. Pinciroli, M. Birattari, Software Infrastructure for E-Puck (and TAM) Technical Report TR/IRIDIA/2015-004, Tech. rep., 2015.
Mondada, F., Bonani, M., Riedo, F., Briod, M., Pereyre, L., Retornaz, P., Magnenat, S., Bringing robotics to formal education: The thymio open-source hardware robot. IEEE Robot. Autom. Mag. 24:1 (2017), 77–85, 10.1109/MRA.2016.2636372.
Sarparast, S., Tarapore, D., Thymio simulator based on ARGoS framework. 2023 URL https://github.com/resilient-swarms/thymio.
Magnenat, S., Rétornaz, P., Bonani, M., Longchamp, V., Mondada, F., ASEBA: A modular architecture for event-based control of complex robots. IEEE/ASME Trans. Mechatronics 16:2 (2011), 321–329, 10.1109/TMECH.2010.2042722.
Pinciroli, C., Talamali, M.S., Reina, A., Marshall, J.A.R., Trianni, V., Simulating kilobots within ARGoS: Models and experimental validation. Dorigo, M., Birattari, M., Blum, C., Christensen, A.L., Reina, A., Trianni, V., (eds.) Swarm Intelligence, 2018, Springer International Publishing, Cham, 176–187.
Talamali, M.S., Saha, A., Marshall, J.A.R., Reina, A., When less is more: Robot swarms adapt better to changes with constrained communication. Science Robotics, 6(56), 2021, eabf1416, 10.1126/scirobotics.abf1416.
Pfister, K., Hamann, H., Collective decision-making with Bayesian robots in dynamic environments. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022, 7245–7250, 10.1109/IROS47612.2022.9982019.
Zheng, Y., Allwright, M., Zhu, W., Kassawat, M., Han, Z., Dorigo, M., Swarm construction coordinated through the building material. Baratchi, M., Cao, L., Kosters, W.A., Lijffijt, J., van Rijn, J.N., Takes, F.W., (eds.) Artificial Intelligence and Machine Learning, 2021, Springer International Publishing, Cham, 188–202.
Webots, Y., Webots: Open source robot simulator. 2022 URL https://cyberbotics.com/.
Dobrokvashina, A., Lavrenov, R., Bai, Y., Svinin, M., Magid, E., Sensors modelling for servosila engineer crawler robot in webots simulator. 2022 Moscow Workshop on Electronic and Networking Technologies (MWENT), 2022, 1–5, 10.1109/MWENT55238.2022.9802400.
Li, C., Guo, S., Guo, J., Study on obstacle avoidance strategy using multiple ultrasonic sensors for spherical underwater robots. IEEE Sens. J. 22:24 (2022), 24458–24470, 10.1109/JSEN.2022.3220246.
Gu, S., Guo, S., Performance evaluation of a novel propulsion system for the spherical underwater robot (SURIII). Appl. Sci., 7(11), 2017, 10.3390/app7111196.
GCtronic, S., GCtronic – electronics and mechatronics. 2022 URL https://www.gctronic.com/.
Gutierrez, A., Campo, A., Dorigo, M., Donate, J., Monasterio-Huelin, F., Magdalena, L., Open E-puck range & bearing miniaturized board for local communication in swarm robotics. 2009 IEEE International Conference on Robotics and Automation, 2009, 3111–3116, 10.1109/ROBOT.2009.5152456.
