A Distributed Approach for Collision Avoidance between Multirotor UAVs Following Planned Missions

Abstract

[EN] As the number of potential applications for Unmanned Aerial Vehicles (UAVs) keeps rising steadily, the chances that these devices get close to each other during their flights also increases, causing concerns regarding potential collisions. This paper proposed the Mission Based Collision Avoidance Protocol (MBCAP), a novel UAV collision avoidance protocol applicable to all types of multicopters flying autonomously. It relies on wireless communications in order to detect nearby UAVs, and to negotiate the procedure to avoid any potential collision. Experimental and simulation results demonstrated the validity and effectiveness of the proposed solution, which typically introduces a small overhead in the range of 15 to 42 s for each risky situation successfully handled.This work was partially supported by the "Ministerio de Ciencia, Innovacion y Universidades, Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Sociedad, Proyectos I+D+I 2018", Spain, under Grant RTI2018-096384-B-I00, and the Universitat Politecnica de Valencia (UPV) under grant number FPI-2017-S1 for the training of PhD researchers.Fabra Collado, FJ.; Zamora-Mero, WJ.; Sangüesa-Escorihuela, JA.; Tavares De Araujo Cesariny Calafate, CM.; Cano, J.; Manzoni, P. (2019). A Distributed Approach for Collision Avoidance between Multirotor UAVs Following Planned Missions. Sensors. 19(10):1-25. https://doi.org/10.3390/s19102404S1251910Mohamed, N., Al-Jaroodi, J., Jawhar, I., Idries, A., & Mohammed, F. (2020). Unmanned aerial vehicles applications in future smart cities. Technological Forecasting and Social Change, 153, 119293. doi:10.1016/j.techfore.2018.05.004SESAR Joint Undertakinghttps://www.sesarju.eu/Fabra, F., T. Calafate, C., Cano, J.-C., & Manzoni, P. (2018). MBCAP: Mission Based Collision Avoidance Protocol for UAVs. 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA). doi:10.1109/aina.2018.00090Drone Collision Avoidancehttps://create.arduino.cc/projecthub/anshulsingh163/drone-collision-avoidance-system-0b6002Liu, Z., & Foina, A. G. (2016). Feature article: an autonomous quadrotor avoiding a helicopter in low-altitude flights. IEEE Aerospace and Electronic Systems Magazine, 31(9), 30-39. doi:10.1109/maes.2016.150131Xiang, J., Liu, Y., & Luo, Z. (2016). Flight safety measurements of UAVs in congested airspace. Chinese Journal of Aeronautics, 29(5), 1355-1366. doi:10.1016/j.cja.2016.08.017Lin, Q., Wang, X., & Wang, Y. (2018). Cooperative Formation and Obstacle Avoidance Algorithm for Multi-UAV System in 3D Environment. 2018 37th Chinese Control Conference (CCC). doi:10.23919/chicc.2018.8483113Zhou, X., Yu, X., & Peng, X. (2019). UAV Collision Avoidance Based on Varying Cells Strategy. IEEE Transactions on Aerospace and Electronic Systems, 55(4), 1743-1755. doi:10.1109/taes.2018.2875556Kim, H., & Ben-Othman, J. (2018). A Collision-Free Surveillance System Using Smart UAVs in Multi Domain IoT. IEEE Communications Letters, 22(12), 2587-2590. doi:10.1109/lcomm.2018.2875477Wang, M., Voos, H., & Su, D. (2018). Robust Online Obstacle Detection and Tracking for Collision-Free Navigation of Multirotor UAVs in Complex Environments. 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV). doi:10.1109/icarcv.2018.8581330Ma, L. (2018). Cooperative Target Tracking using a Fleet of UAVs with Collision and Obstacle Avoidance. 2018 22nd International Conference on System Theory, Control and Computing (ICSTCC). doi:10.1109/icstcc.2018.8540717Chen, P.-H., & Lee, C.-Y. (2018). UAVNet: An Efficient Obstacel Detection Model for UAV with Autonomous Flight. 2018 International Conference on Intelligent Autonomous Systems (ICoIAS). doi:10.1109/icoias.2018.8494201Fabra, F., Calafate, C. T., Cano, J. C., & Manzoni, P. (2018). ArduSim: Accurate and real-time multicopter simulation. Simulation Modelling Practice and Theory, 87, 170-190. doi:10.1016/j.simpat.2018.06.009Accurate and real-time multi-UAV simulationhttps://bitbucket.org/frafabco/ardusim/src/master/MAVLink Micro Air Vehicle Communication Protocolhttp://qgroundcontrol.org/mavlink/startGorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18-27. doi:10.1016/j.rse.2017.06.031NS-2 The Network Simulatorhttp://nsnam.sourceforge.net/wiki/index.php/Main_PageOMNeT++ Discrete Event Simulatorhttps://omnetpp.org/Quaternium, Home of the Longest Flight Time Hybrid Dronehttp://www.quaternium.com/Gauss-Markov Mobilityhttps://doc.omnetpp.org/inet/api-current/neddoc/inet.mobility.single.GaussMarkovMobility.htmlFerrera, E., Alcántara, A., Capitán, J., Castaño, A., Marrón, P., & Ollero, A. (2018). Decentralized 3D Collision Avoidance for Multiple UAVs in Outdoor Environments. Sensors, 18(12), 4101. doi:10.3390/s1812410