Air Traffic Management Safety Challenges

The primary goal of the Air Traffic Management (ATM) system is to control accident risk. ATM safety has improved over the decades for many reasons, from better equipment to additional safety defences. But ATM safety targets, improving on current performance, are now extremely demanding. Safety analysts and aviation decision-makers have to make safety assessments based on statistically incomplete evidence. If future risks cannot be estimated with precision, then how is safety to be assured with traffic growth and operational/technical changes? What are the design implications for the USA’s ‘Next Generation Air Transportation System’ (NextGen) and Europe’s Single European Sky ATM Research Programme (SESAR)? ATM accident precursors arise from (eg) pilot/controller workload, miscommunication, and lack of upto- date information. Can these accident precursors confidently be ‘designed out’ by (eg) better system knowledge across ATM participants, automatic safety checks, and machine rather than voice communication? Future potentially hazardous situations could be as ‘messy’ in system terms as the Überlingen mid-air collision. Are ATM safety regulation policies fit for purpose: is it more and more difficult to innovate, to introduce new technologies and novel operational concepts? Must regulators be more active, eg more inspections and monitoring of real operational and organisational practices

Air Traffic Safety: continued evolution or a new Paradigm.

The context here is Transport Risk Management. Is the philosophy of Air Traffic Safety different from other modes of transport? – yes, in many ways, it is. The focus is on Air Traffic Management (ATM), covering (eg) air traffic control and airspace structures, which is the part of the aviation system that is most likely to be developed through new paradigms. The primary goal of the ATM system is to control accident risk. ATM safety has improved over the decades for many reasons, from better equipment to additional safety defences. But ATM safety targets, improving on current performance, are now extremely demanding. What are the past and current methodologies for ATM risk assessment; and will they work effectively for the kinds of future systems that people are now imagining and planning? The title contrasts ‘Continued Evolution’ and a ‘New Paradigm’. How will system designers/operators assure safety with traffic growth and operational/technical changes that are more than continued evolution from the current system? What are the design implications for ‘new paradigms’, such as the USA’s ‘Next Generation Air Transportation System’ (NextGen) and Europe’s Single European Sky ATM Research Programme (SESAR)? Achieving and proving safety for NextGen and SESAR is an enormously tough challenge. For example, it will need to cover system resilience, human/automation issues, software/hardware performance/ground/air protection systems. There will be a need for confidence building programmes regarding system design/resilience, eg Human-in-the-Loop simulations with ‘seeded errors’

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

[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

Texture dependence of motion sensing and free flight behavior in blowflies

Lindemann JP, Egelhaaf M. Texture dependence of motion sensing and free flight behavior in blowflies. Frontiers in Behavioral Neuroscience. 2013;6:92.Many flying insects exhibit an active flight and gaze strategy: purely translational flight segments alternate with quick turns called saccades. To generate such a saccadic flight pattern, the animals decide the timing, direction, and amplitude of the next saccade during the previous translatory intersaccadic interval. The information underlying these decisions is assumed to be extracted from the retinal image displacements (optic flow), which scale with the distance to objects during the intersaccadic flight phases. In an earlier study we proposed a saccade-generation mechanism based on the responses of large-field motion-sensitive neurons. In closed-loop simulations we achieved collision avoidance behavior in a limited set of environments but observed collisions in others. Here we show by open-loop simulations that the cause of this observation is the known texture-dependence of elementary motion detection in flies, reflected also in the responses of large-field neurons as used in our model. We verified by electrophysiological experiments that this result is not an artifact of the sensory model. Already subtle changes in the texture may lead to qualitative differences in the responses of both our model cells and their biological counterparts in the fly's brain. Nonetheless, free flight behavior of blowflies is only moderately affected by such texture changes. This divergent texture dependence of motion-sensitive neurons and behavioral performance suggests either mechanisms that compensate for the texture dependence of the visual motion pathway at the level of the circuits generating the saccadic turn decisions or the involvement of a hypothetical parallel pathway in saccadic control that provides the information for collision avoidance independent of the textural properties of the environment

Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search

Target search with unmanned aerial vehicles (UAVs) is relevant problem to many scenarios, e.g., search and rescue (SaR). However, a key challenge is planning paths for maximal search efficiency given flight time constraints. To address this, we propose the Obstacle-aware Adaptive Informative Path Planning (OA-IPP) algorithm for target search in cluttered environments using UAVs. Our approach leverages a layered planning strategy using a Gaussian Process (GP)-based model of target occupancy to generate informative paths in continuous 3D space. Within this framework, we introduce an adaptive replanning scheme which allows us to trade off between information gain, field coverage, sensor performance, and collision avoidance for efficient target detection. Extensive simulations show that our OA-IPP method performs better than state-of-the-art planners, and we demonstrate its application in a realistic urban SaR scenario.Comment: Paper accepted for International Conference on Robotics and Automation (ICRA-2019) to be held at Montreal, Canad