Unmanned Aircraft Systems(UASs) have advanced technologically and surged exponentially over recent years. Currently, due to safety concerns, most civil operations of UAS are conducted in low-level uncontrolled area or...Unmanned Aircraft Systems(UASs) have advanced technologically and surged exponentially over recent years. Currently, due to safety concerns, most civil operations of UAS are conducted in low-level uncontrolled area or in segregated controlled airspace. As the industry progresses, both operational and technological capabilities have matured to the point where UASs are expected to gain greater freedom of access to both controlled and uncontrolled airspace. Extensive technical and regulatory surveys have been conducted to enable the expanded operations. However, most surveys are derived from the perspective of UAS own operating mechanism and barely consider interactions of their non-segregated activities with the Air Traffic Management(ATM) system. Hence, to fill the gap, this paper presents a survey conducted from the perspective of Air Navigation Service Provider(ANSP), which serves to accommodate these new entrants to the overall national airspace while continuing flight safety and efficiency. The primary objectives of this paper are to:(A) describe what typical ANSP-supplied UAS Traffic Management(UTM) architecture is required to facilitate all types of civil UAS operations;(B) identify three major ANSP considerations on how UAS can be accommodated safely in civil airspace;(C) outline future directions and challenges related with UAS operations for the ANSP.展开更多
Unmanned aerial vehicles(UAVs)have gained much attention from academic and industrial areas due to the significant number of potential applications in urban airspace.A traffic management system for these UAVs is neede...Unmanned aerial vehicles(UAVs)have gained much attention from academic and industrial areas due to the significant number of potential applications in urban airspace.A traffic management system for these UAVs is needed to manage this future traffic.Tactical conflict resolution for unmanned aerial systems(UASs)is an essential piece of the puzzle for the future UAS Traffic Management(UTM),especially in very low-level(VLL)urban airspace.Unlike conflict resolution in higher altitude airspace,the dense high-rise buildings are an essential source of potential conflict to be considered in VLL urban airspace.In this paper,we propose an attention-based deep reinforcement learning approach to solve the tactical conflict resolution problem.Specifically,we formulate this task as a sequential decision-making problem using Markov Decision Process(MDP).The double deep Q network(DDQN)framework is used as a learning framework for the host drone to learn to output conflict-free maneuvers at each time step.We use the attention mechanism to model the individual neighbor's effect on the host drone,endowing the learned conflict resolution policy to be adapted to an arbitrary number of neighboring drones.Lastly,we build a simulation environment with various scenarios covering different types of encounters to evaluate the proposed approach.The simulation results demonstrate that our proposed algorithm provides a reliable solution to minimize secondary conflict counts compared to learning and non-learning-based approaches under different traffic density scenarios.展开更多
基金co-supported by the Outstanding Youth Fund of the National Natural Science Foundation of China (No. 61822102)the MIIT Technological Base Program (No. JSZL2016601B003)the National Key Research and Development Program (No. 2018YFB0505105)。
文摘Unmanned Aircraft Systems(UASs) have advanced technologically and surged exponentially over recent years. Currently, due to safety concerns, most civil operations of UAS are conducted in low-level uncontrolled area or in segregated controlled airspace. As the industry progresses, both operational and technological capabilities have matured to the point where UASs are expected to gain greater freedom of access to both controlled and uncontrolled airspace. Extensive technical and regulatory surveys have been conducted to enable the expanded operations. However, most surveys are derived from the perspective of UAS own operating mechanism and barely consider interactions of their non-segregated activities with the Air Traffic Management(ATM) system. Hence, to fill the gap, this paper presents a survey conducted from the perspective of Air Navigation Service Provider(ANSP), which serves to accommodate these new entrants to the overall national airspace while continuing flight safety and efficiency. The primary objectives of this paper are to:(A) describe what typical ANSP-supplied UAS Traffic Management(UTM) architecture is required to facilitate all types of civil UAS operations;(B) identify three major ANSP considerations on how UAS can be accommodated safely in civil airspace;(C) outline future directions and challenges related with UAS operations for the ANSP.
基金supported by the National Research Foundation(NRF),Singapore,and the Civil Aviation Authority of Singapore(CAAS),under the Aviation Transformation Programme(ATP).
文摘Unmanned aerial vehicles(UAVs)have gained much attention from academic and industrial areas due to the significant number of potential applications in urban airspace.A traffic management system for these UAVs is needed to manage this future traffic.Tactical conflict resolution for unmanned aerial systems(UASs)is an essential piece of the puzzle for the future UAS Traffic Management(UTM),especially in very low-level(VLL)urban airspace.Unlike conflict resolution in higher altitude airspace,the dense high-rise buildings are an essential source of potential conflict to be considered in VLL urban airspace.In this paper,we propose an attention-based deep reinforcement learning approach to solve the tactical conflict resolution problem.Specifically,we formulate this task as a sequential decision-making problem using Markov Decision Process(MDP).The double deep Q network(DDQN)framework is used as a learning framework for the host drone to learn to output conflict-free maneuvers at each time step.We use the attention mechanism to model the individual neighbor's effect on the host drone,endowing the learned conflict resolution policy to be adapted to an arbitrary number of neighboring drones.Lastly,we build a simulation environment with various scenarios covering different types of encounters to evaluate the proposed approach.The simulation results demonstrate that our proposed algorithm provides a reliable solution to minimize secondary conflict counts compared to learning and non-learning-based approaches under different traffic density scenarios.