Swarm Robotics as a Solution to Crops Inspection for Precision Agriculture
Abstract
This paper summarizes the concept of swarm robotics and its applicability to crop inspections. To increase the agricultural yield it is essential to monitor the crop health. Hence, precision agriculture is becoming a common practice for farmers providing a system that can inspect the state of the plants (Khosla and others, 2010). One of the rising technologies used for agricultural inspections is the use of unmaned air vehicles (UAVs) which are used to take aerial pictures of the farms so that the images could be processed to extract data about the state of the crops (Das et al., 2015). For this process both fixed wings and quadrotors UAVs are used with a preference over the quadrotor since it’s easier to operate and has a milder learning curve compared to fixed wings (Kolodny, 2017). UAVs require battery replacement especially when the environmental conditions result in longer inspection times (“Agriculture - Maximize Yields with Aerial Imaging,” n.d., “Matrice 100 - DJI Wiki,” n.d.). As a result, inspection systems for crops using commercial quadrotors are limited by the quadrotor´s maximum flight speed, maximum flight height, quadrotor´s battery time, crops area, wind conditions, etc. (“Mission Estimates,” n.d.).
Keywords: Swarm Robotics, Precision Agriculture, Unmanned Air Vehicle, Quadrotor, inspection.
References
[1] Agriculture - Maximize Yields with Aerial Imaging [WWW Document], n.d.. DJI Off. URL http://www.dji.com/enterprise/agriculture (accessed 6.11.17).
[2] Bayındır, L., 2016. A review of swarm robotics tasks. Neurocomputing 172, 292–321. doi:10.1016/j.neucom.2015.05.116
[3] Beni, G., 2005. From Swarm Intelligence to Swarm Robotics, in: Şahin, E., Spears, W.M. (Eds.), Swarm Robotics. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 1– 9. doi:10.1007/978-3-540-30552-1_1
[4] Correll, N., Martinoli, A., 2006. Collective inspection of regular structures using a swarm of miniature robots, in: Experimental Robotics IX. Springer, pp. 375–386.
[5] Couceiro, M.S., 2014. Evolutionary Robot Swarms Under Real-World Constraints.
[6] Das, J., Cross, G., Qu, C., Makineni, A., Tokekar, P., Mulgaonkar, Y., Kumar, V., 2015. Devices, systems, and methods for automated monitoring enabling precision agriculture, in: Automation Science and Engineering (CASE), 2015 IEEE International Conference on. IEEE, pp. 462–469.
[7] de Vries, E., Subbarao, K., 2011. Cooperative Control of Swarms of Unmanned Aerial Vehicles. American Institute of Aeronautics and Astronautics. doi:10.2514/6.2011-78
[8] Garcia, G.A., Keshmiri, S.S., 2016. Biologically inspired trajectory generation for swarming UAVs using topological distances. Aerosp. Sci. Technol. 54, 312–319. doi:10.1016/j.ast.2016.04.028
[9] Kennedy, J.F., Eberhart, R.C., Shi, Y., 2001. Swarm intelligence, The Morgan Kaufmann series in evolutionary computation. Morgan Kaufmann Publishers, San Francisco.
[10] Khosla, R., others, 2010. Precision agriculture: challenges and opportunities in a flat world, in: 19th World Congress of Soil Science, Soil Solutions for a Changing World. pp. 1–6.
[11] Kolodny, L., 2017. Fixed-wing drones not quite taking off in commercial market, a new DroneDeploy study finds. TechCrunch.
[12] Kumar, V., Kushleyev, A., Mellinger, D., 2017. Three-dimensional manipulation of teams of quadrotors. Google Patents.
[13] Leonard, E.C., 2016. Precision Agriculture, in: Wrigley, C., Corke, H., Seetharaman, K., Faubion, J. (Eds.), Encyclopedia of Food Grains (Second Edition). Academic Press, Oxford, pp. 162–167.
[14] Matrice 100 - DJI Wiki [WWW Document], n.d. URL http://wiki.dji.com/en/index.php/Matrice_100 (accessed 6.11.17).
[15] Meyer, J., Sendobry, A., Kohlbrecher, S., Klingauf, U., Von Stryk, O., 2012. Comprehensive simulation of quadrotor uavs using ros and gazebo, in: International Conference on Simulation, Modeling, and Programming for Autonomous Robots. Springer, pp. 400–411.
[16] Mission Estimates [WWW Document], n.d.. Drones Made Easy. URL http://support.dronesmadeeasy.com/hc/en-us/articles/ 205754946-Mission-Estimates (accessed 6.16.17).
[17] Mulgaonkar, Y., Cross, G., Kumar, V., 2015. Design of small, safe and robust quadrotor swarms, in: Robotics and Automation (ICRA), 2015 IEEE International Conference on. IEEE, pp. 2208–2215.
[18] Navarro, I., Matía, F., 2013. An Introduction to Swarm Robotics. ISRN Robot. 2013, 1–10. doi:10.5402/2013/608164
[19] Rutishauser, S., Correll, N., Martinoli, A., 2009. Collaborative coverage using a swarm of networked miniature robots. Robot. Auton. Syst. 57, 517–525. doi:10.1016/j.robot.2008.10.023
[20] Srbinovska, M., Gavrovski, C., Dimcev, V., Krkoleva, A., Borozan, V., 2015. Environmental parameters monitoring in precision agriculture using wireless sensor networks. J. Clean. Prod. 88, 297–307. doi:http://dx.doi.org/10.1016/j.jclepro.2014.04.036
[21] Tan, Y., Zheng, Z., 2013. Research Advance in Swarm Robotics. Def. Technol. 9, 18– 39. doi:10.1016/j.dt.2013.03.001
[22] Torres, M., Pelta, D.A., Verdegay, J.L., Torres, J.C., 2016. Coverage path planning with unmanned aerial vehicles for 3D terrain reconstruction. Expert Syst. Appl. 55, 441– 451. doi:10.1016/j.eswa.2016.02.007
[23] Weng, L., Liu, Q., Xia, M., Song, Y.D., 2014. Immune network-based swarm intelligence and its application to unmanned aerial vehicle (UAV) swarm coordination. Neurocomputing 125, 134–141. doi:10.1016/j.neucom.2012.06.053
[24] Zhang, C., Kovacs, J.M., 2012. The application of small unmanned aerial systems for precision agriculture: a review. Precis. Agric. 13, 693–712. doi:10.1007/s11119-012- 9274-5
[25] Zhu, X., Liu, Z., Yang, J., 2015. Model of Collaborative UAV Swarm Toward Coordination and Control Mechanisms Study. Procedia Comput. Sci. 51, 493–502. doi:10.1016/j.procs.2015.05.274