Increasing use of Unmanned Aerial Vehicle (UAV) in urban environments poses to an increased risk of fallen UAVs impacting people and vehicles on the ground, as well as colliding with manned aircraft in the vicinity of airports. Risk management of UAV flights for safe operations is essential. We proposed a comprehensive risk assessment model for UAV operation in urban environments. Three risk categories (people, vehicles, and manned aircraft) were considered and each risk cost was quantified using collision probability. We adjusted the risk costs in various magnitudes to a same scale and conducted a sensitivity analysis to determine the optimal coefficients of the three risk cost models. We then computed the total risk and generated a risk cost map for path planning. Modified path planning algorithms were used to produce a cost-effective path, and we compared their performances in terms of total risk cost and computational time. Lastly, we performed simulations to validate the feasibility and effectiveness of our proposed risk assessment model. The results show that the risk-cost-based path planning method can generate safer path for UAV operations than the traditional shortest-distance-based method. Our proposed model can be extended to complex urban environments by including more relevant parameters and data. INDEX TERMS Unmanned aerial vehicle, risk assessment model, risk cost map, path planning, urban environments Fuqing Dai holds the Dean's Chair in Air Traffic Management School at the Civil Aviation University of China. He is also a professor focusing on the research of airspace planning and management, flight procedures design and optimization, UTM and ATM systems. Professor Dai obtained his master's degree from Ecole Nationale de l'Aviation Civile, France and bachelor's degree from Nanjing University, China.

Deployments and operations of civilian unmanned aircraft in urban environments have been seeing a significant rise, which increases the demand of urban unmanned air traffic and the need for airspace. Large-scale UAV operations in urban environments may pose risks to people on the ground and manned aircraft in the air. To safely and efficiently utilize the urban airspace, several concept of operations (ConOps) about urban airspace utilization are proposed and demonstrated in this paper. The proposed ConOps concentrates on airspace configuration and operational rules from three aspects. Firstly, AirMatrix airspace configuration is introduced and expanded with operational rules in the network. To balance the flight flexibility and airspace complexity, different resolution AirMartix is introduced. Moreover, AirMatrix corridor is proposed to connect reserved areas with safe and efficient traffic flows considering Communication, Navigation and Surveillance (CNS) performance. Secondly, Free-Flight Operation (FFO) and Trajectory-Based Operation (TBO) are illustrated and compared in terms of operational efficiency and airspace capacity based on simulation studies. Thirdly, under the AirMatrix framework, airspace risk assessment and contingency management are investigated to provide suggestions for urban airspace safety management and fail-safe system design.

Drones have a wide range of applications in urban environments as they can both enhance people's daily activities and commercial activities through various operations and deployments. With the increasing number of drones, flight safety and efficiency become the main concern, and effective drone operations can make a difference. Accordingly, 4D path planning for drone operations is the focus of this paper, and the swarm-based method is proposed to solve this complicated optimization problem. Under the framework of 'AirMatrix', the problem is solved in two levels, i.e., 3D path planning for a single drone and conflict resolution among drones. In the multipath planning level, multiple alternative flight paths for each drone are generated to increase the acceptance rate of a flight request. The constraints on a single flight path and two different flight paths are considered. The goal is to obtain several different short flight paths as alternatives. A clustering improved ant colony optimization (CIACO) algorithm is employed to solve the multi-path planning problem. The crowding mechanism is used in clustering, and some improvements are made to strengthen the global and local search ability in the early and later phases of iterations. In the task scheduling level, the conflicts between two drones are defined in two circumstances. One is for the time interval of passing the same path point, another one is for the right-angle collision between two drones. A three-layer fitness function is proposed to maximize the number of permitted flights according to the safety requirement, in which the airspace utilization and the operators' requests are both considered. A 'cross-off' strategy is developed to calculate the fitness value, and a 'distributed-centralized' strategy is applied considering the task priorities of drones. A genetic algorithm (GA)-based task scheduling algorithm is also developed according to the characteristic of the established model. Simulation results demonstrate that 4D flight path of each drone can be generated by the proposed swarmed-based algorithms, and safe and efficient drone operations in a specific airspace can be ensured.

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This paper presents a risk-based model for UAV path planning in urban low altitude environments. Firstly, the risks in urban environments were identified and classified into four groups, and the corresponding risk costs were categorized into five different levels. A general risk cost model was then developed for qualifying all related risk costs. Secondly, a ConOps for urban air route network with plane and cubical diagonals was built and the obtained risk cost information were ingested into the network to assist path planning. Finally, an A*cost algorithm was developed to generate a safe and cost-effective path for UAVs in urban low altitude airspace. Simulation results show that the novel route network structure with diagonals has better performance, with an average reduction of 17.56% for path distance. While the proposed A*cost algorithm has good results in terms of total cost and computational time, outperforming traditional Dijkstra and Ant Colony algorithm.

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