Kilogrid: a novel experimental environment for the Kilobot robot
- 403 Downloads
We present the Kilogrid, an open-source virtualization environment and data logging manager for the Kilobot robot, Kilobot for short. The Kilogrid has been designed to extend the sensory-motor abilities of the Kilobot, to simplify the task of collecting data during experiments, and to provide researchers with a tool to fine-control the experimental setup and its parameters. Based on the design of the Kilobot and compatible with existing hardware, the Kilogrid is a modular system composed of a grid of computing nodes, or modules that provides a bidirectional communication channel between the Kilobots and a remote workstation. In this paper, we describe the hardware and software architecture of the Kilogrid system as well as its functioning to accompany its release as a new open hardware tool for the swarm robotics community. We demonstrate the capabilities of the Kilogrid using a 200-module Kilogrid, swarms of up to 100 Kilobots, and four different case studies: exploration and obstacle avoidance, site selection based on multiple gradients, plant watering, and pheromone-based foraging. Through this set of case studies, we show how the Kilogrid allows the experimenter to virtualize sensors and actuators not available to the Kilobot and to automatize the collection of data essential for the analysis of the experiments.
KeywordsKilogrid Kilobot Swarm robotics Virtualization Automated data collection Tracking Open source
This work was supported by the European Research Council through the ERC Advanced Grant “E-SWARM: Engineering Swarm Intelligence Systems” (contract 246939) to Marco Dorigo. Marco Dorigo acknowledges support from the Belgian F.R.S.-FNRS, for which he is a Research Director.
- Antoun, A., Valentini, G., Hocquard, E., Wiandt, B., Trianni, V., & Dorigo, M. (2016). Kilogrid: a modular virtualization environment for the Kilobot robot. In 2016 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 3809–3814). IEEE Press.Google Scholar
- Arvin, F., Krajník, T., Turgut, A. E., & Yue, S. (2015a). COS\(\phi \): Artificial pheromone system for robotic swarms research. In 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 407–412).Google Scholar
- Arvin, F., Xiong, C., & Yue, S. (2015b). Colias-\(\phi \): An autonomous micro robot for artificial pheromone communication. International Journal of Mechanical Engineering and Robotics Research, 4(4), 349–353.Google Scholar
- Becker, A., Habibi, G., Werfel, J., Rubenstein, M., & McLurkin, J. (2013). Massive uniform manipulation: Controlling large populations of simple robots with a common input signal. In 2013 IEEE/RSJ international conference on intelligent robots and systems (pp. 520–527).Google Scholar
- Beckers, R., Holland, O.E., & Deneubourg, J. L. (1994). From local actions to global tasks: Stigmergy and collective robotics. In Artificial life IV (pp. 181–189). MIT Press.Google Scholar
- Gauci, M., Radhika, N., & Rubenstein, M. (2017). Distributed autonomous robotic systems: The 13th international symposium. Springer, chap Programmable self-disassembly for shape formation in large-scale robot collectives (in press).Google Scholar
- Khaliq, A. A., & Saffiotti, A. (2015). Stigmergy at work: Planning and navigation for a service robot on an RFID floor. In IEEE International Conference on Robotics and Automation, ICRA 2015, (pp. 1085–1092). IEEE Press.Google Scholar
- Melhuish, C., Welsby, J., & Edwards, C. (1999). Using templates for defensive wall building with autonomous mobile ant-like robots. In Proceedings of towards intelligent mobile robots (TIMR99), Vol. 99.Google Scholar
- Mondada, F., Bonani, M., Raemy, X., Pugh, J., Cianci, C., Klaptocz, A., Magnenat, S., Zufferey, J.C., Floreano, D., & Martinoli, A. (2009). The e-puck, a robot designed for education in engineering. In P. J. S. Gonçalves, P. J. D. Torres & C. M. O. Alves (Eds.), Proceedings of the 9th conference on autonomous robot systems and competitions (Vol. 1, pp. 59–65). IPCB: Instituto Politécnico de Castelo Branco.Google Scholar
- Pickem, D., Wang, L., Glotfelter, P., Diaz-Mercado, Y., Mote, M., Ames, A.D., Feron, E., & Egerstedt, M. (2016). Safe, remote-access swarm robotics research on the robotarium. arXiv:1604.00640.
- Reina, A., Salvaro, M., Francesca, G., Garattoni, L., Pinciroli, C., Dorigo, M., & Birattari, M. (2015). Augmented reality for robots: Virtual sensing technology applied to a swarm of e-pucks. In 2015 NASA/ESA conference on adaptive hardware and systems (AHS) (pp. 1–6). IEEE Press.Google Scholar
- Rubenstein, M., Cabrera, A., Werfel, J., Habibi, G., McLurkin, J., & Nagpal, R. (2013). Collective transport of complex objects by simple robots: Theory and experiments. In T. Ito, C. Jonker, M. Gini & O. Shehory (Eds.), Proceedings of the 12th international conference on autonomous agents and multiagent systems, IFAAMAS, AAMAS ’13 (pp. 47–54).Google Scholar
- Soorati, M. D., & Hamann, H. (2016). Robot self-assembly as adaptive growth process: Collective selection of seed position and self-organizing tree-structures. In 2016 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 5745–5750).Google Scholar
- Støy, K. (2001). Using situated communication in distributed autonomous mobile robotics. In Proceedings of the 7th scandinavian conference on artificial intelligence, SCAI’01 (Vol. 1, pp. 44–52). IOS Press.Google Scholar
- Valentini, G., Hamann, H., & Dorigo, M. (2015). Efficient decision-making in a self-organizing robot swarm: On the speed versus accuracy trade-off. In R. Bordini, E. Elkind, G. Weiss & P. Yolum (Eds.), Proceedings of the 14th international conference on autonomous agents and multiagent systems, IFAAMAS, AAMAS ’15 (pp. 1305–1314).Google Scholar