Unmanned Aerial Vehicles (UAVs), in particular Drones, have gained significant importance in diverse sectors, mainly military uses. Recently, we can see a growth in acceptance of autonomous UAVs in civilian spaces as well. However, there is still a long way to go before drones are capable enough to be safely used without human surveillance. A lot of subsystems and components are involved in taking care of position estimation, route planning, software/data security, and collision avoidance to have autonomous drones that fly in civilian spaces without being harmful to themselves, other UAVs, environment, or humans. The ultimate goal of this research is to advance collision avoidance and mitigation techniques through quantitative safety risk assessment. To this end, it is required to identify the most relevant faults/failures/threats that can happen during a drone's flight/mission. The analysis of historical data is also a relevant instrument to help to characterize the most frequent and relevant issues in UAV systems, which may cause safety hazards. Then we need to estimate their impact quantitatively, by using fault injection techniques. Knowing the growing interests in UAVs and their huge potential for future commercial applications, the expected outcome of this work will be helpful to researchers for future related research studies. Furthermore, we envisage the utilization of expected results by companies to develop safer drone applications, and by air traffic controllers for building failure prediction and collision avoidance solutions.
翻译:无人驾驶飞行器(无人驾驶飞行器),特别是无人驾驶飞行器(无人驾驶飞行器),在不同的部门,主要是军事用途,已变得非常重要。最近,我们看到民用空间接受自主无人驾驶飞行器的情况也有所增加。然而,在无人驾驶飞行器能够足够安全地在没有人类监视的情况下使用之前,仍有很长的路要走。许多次系统和部件都涉及位置估计、路线规划、软件/数据安全以及避免碰撞等事项,这些无人驾驶飞行器在民用空间飞行而不会损害自身、其他无人驾驶飞行器、环境或人类。这项研究的最终目标是通过定量安全风险评估来推进避免和减缓碰撞的技术。为此,需要查明无人驾驶飞行器在飞行/飞行任务期间可能发生的最相关的故障/故障/威胁。对历史数据的分析也是帮助确定无人驾驶飞行器系统最经常和最相关的问题的特征的相关工具,这些问题可能造成安全危险。然后,我们需要通过使用错误注射技术,从数量上估计其影响。了解无人驾驶飞行器中日益增长的兴趣及其巨大的避免碰撞和减轻碰撞技术。为未来商业应用的预测结果,通过我们的未来安全性研究来预测安全地利用这一研究的结果,从而预测安全地研究的结果。