Abstract
This paper extends an existing decisional framework for the navigation of Micro Aerial Vehicle (MAV) swarms. The work finds inspiration in the geocaching outdoor game. It leverages place recognition methods, information sharing and collaborative work between MAVs. It is unique in that a priori none of the MAVs knows the trajectory, waypoints and destination. The MAVs collectively solve a series of problems that involve the recognition of physical places and determination of their GPS coordinates. Our algorithm builds upon various methods that had been created for place recognition. The need for a decisional framework comes from the fact that all methods are fallible and make place recognition errors. In this paper, we augment the navigation algorithm with a decisional framework resolving conflicts resulting from errors made by place recognition methods. The errors divide the members of a swarm with respect to the location of waypoints (i.e., some members continue the trip following the proper itinary; others follow a wrong one). We propose four decisional algorithms to resolve conflicts among members of a swarm due to place recognition errors. The performance of the decisional algorithms is modeled and analyzed.
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References
Aerial Robots for Sewer Inspection, “ARSI”. http://echord.eu/essential_grid/arsi/. Accessed 2019
DHL: “DHL’s Parcelcopter: changing shipping forever”. http://bit.ly/2WV7KcO. Accessed 2019
Altigator: “Drones for search and rescue missions”. https://altigator.com/drones-for-search-rescue-missions/. Accessed 2019
Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. Int. J. Robot. Res. 5(1), 90–98 (1986)
LaValle, S.M.: Rapidly-exploring random trees: A new tool for path planning, Department of Computer Science, Iowa State University, Technical report (1998)
LaValle, S.: Planning Algorithms. Cambridge University Press, New York (2006)
Radmanesh, M., Kumar, M., Guentert, P.H., Sarim, M.: Overview of path-planning and obstacle avoidance algorithms for UAVs: a comparative study. Unmanned Syst. 06(02), 95–118 (2018)
Zhao, Y., Zheng, Z., Liu, Y.: Survey on computational-intelligence-based UAV path planning. Knowl. Based Syst. 158, 54–64 (2018)
Barbeau, M., Garcia-Alfaro, J., Kranakis, E.: Geocaching-inspired resilient path planning for drone swarms. In: IEEE Conference on Computer Communications (IEEE INFOCOM 2019) Workshops, pp. 620–625, April 2019. https://doi.org/10.1109/INFCOMW.2019.8845318
Chu, S., Narayanan, S., Kuo, C.J.: Environmental sound recognition with time-frequency audio features. IEEE Trans. Audio Speech Lang. Process. 17(6), 1142–1158 (2009)
Gehrig, M., Stumm, E., Hinzmann, T., Siegwart, R.: Visual place recognition with probabilistic voting. In: IEEE International Conference on Robotics and Automation (ICRA 2017), pp. 3192–3199 (2017)
Lowry, S., et al.: Visual place recognition: a survey. IEEE Trans. Robot. 32(1), 1–19 (2016)
Galvez-López, D., Tardos, J.D.: Bags of binary words for fast place recognition in image sequences. IEEE Trans. Robot. 28(5), 1188–1197 (2012)
Spaan, D., et al.: Thermal infrared imaging from drones offers a major advance for spider monkey surveys. Drones 3(2), 34 (2019)
Otto, A., Agatz, N., Campbell, J., Golden, B., Pesch, E.: Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: a survey. Networks 72(4), 411–458 (2018)
Turpin, M., Michael, N., Kumar, V.: Capt: concurrent assignment and planning of trajectories for multiple robots. Int. J. Robot. Res. 33(1), 98–112 (2014)
Rizqi, A.A.A., Cahyadi, A.I., Adji, T.B.: Path planning and formation control via potential function for UAV quadrotor. In: 2014 International Conference on Advanced Robotics and Intelligent Systems (ARIS), pp. 165–170, June 2014
Maravall, D., de Lope, J., Fuentes Brea, J.P.: A vision-based dual anticipatory/reactive control architecture for indoor navigation of an unmanned aerial vehicle using visual topological maps. In: Ferrández Vicente, J.M., Álvarez Sánchez, J.R., de la Paz López, F., Toledo Moreo, F.J. (eds.) IWINAC 2013. LNCS, vol. 7931, pp. 66–72. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38622-0_7
Maravall, D., de Lope, J., Fuentes, J.: Navigation and self-semantic location of drones in indoor environments by combining the visual bug algorithm and entropy-based vision. Front. Neurorobot. 11, 46 (2017)
Narendra, K., Thathachar, M.: Learning Automata: An Introduction. Courier Corporation (2012)
Park, S., Kim, H., Kim, K., Kim, H.: Drone formation algorithm on 3d space for a drone-based network infrastructure. In: IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), pp. 1–6, September 2016
Shrit, O., Martin, S., Alagha, K., Pujolle, G.: A new approach to realize drone swarm using ad-hoc network. In: 2017 16th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net), pp. 1–5, June 2017
Duan, H., Luo, Q., Shi, Y., Ma, G.: Hybrid particle swarm optimization and genetic algorithm for multi-UAV formation reconfiguration. IEEE Comput. Intell. Mag. 8(3), 16–27 (2013)
Barbeau, M., Garcia-Alfaro, J., Kranakis, E., Santos, F.: Quality amplification of error prone navigation for swarms of micro aerial vehicles. In: 2019 IEEE Globecom Workshops (GC Wkshps), pp. 1–6. IEEE (2019)
Kranakis, E., Krizanc, D.: Searching with uncertainty. In: 6th International Colloquium on Structural Information & Communication Complexity, SIROCCO 1999, Lacanau-Ocean, France, 1–3 July 1999, pp. 194–203 (1999)
Kirousis, L.M., Kranakis, E., Krizanc, D., Stamatiou, Y.C.: Locating information with uncertainty in fully interconnected networks. In: Herlihy, M. (ed.) DISC 2000. LNCS, vol. 1914, pp. 283–296. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-40026-5_19
Farfan-Escobedo, J.D., Enciso-Rodas, L., Vargas-Muñoz, J.E.: Towards accurate building recognition using convolutional neural networks. In: 2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON), pp. 1–4 (2017)
Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: an open-source slam system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017)
Mitzenmacher, M., Upfal, E.: Probability and Computing: Randomization and Probabilistic Techniques in Algorithms and Data Analysis. Cambridge University Press, New York (2017)
Acknowledgments
We acknowledge the financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC), the European Commission (H2020 SPARTA project, under grant agreement 830892), and the SAMOVAR research laboratory of Télécom SudParis.
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Barbeau, M., Garcia-Alfaro, J., Kranakis, E. (2020). Geocaching-Inspired Navigation for Micro Aerial Vehicles with Fallible Place Recognition. In: Grieco, L.A., Boggia, G., Piro, G., Jararweh, Y., Campolo, C. (eds) Ad-Hoc, Mobile, and Wireless Networks. ADHOC-NOW 2020. Lecture Notes in Computer Science(), vol 12338. Springer, Cham. https://doi.org/10.1007/978-3-030-61746-2_5
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