UAV path optimization with an integrated cost assessment model considering third-party risks in metropolitan environments

Maintaining safe separations is essential to achieve reliable operations for unmanned aerial vehicles (UAV). Given the escalating severity of collision risks associated with UAVs, separation criteria have emerged as foundational to both airspace design and collision avoidance strategies. However, without adequate consideration of the characteristics of small-UAV (sUAV) encounters and airspace structure, most of the existing models focus on assessing uniform separation volume with manned aircraft. This paper proposes a demarcation method of safety separation for sUAVs based on collision risk estimation. Firstly, five collision zones are developed for collision determination by considering the shape characteristics. Then, the collision risk model is established from the 3-D probability density function (PDF) with the supremum of total system error (TSE). Finally, the minimum safe separations for different encountering scenarios are demarcated by setting the threshold of risk. Simulation results testify that the selection of the collision zones and crossing angles affect the collision risk apparently. The results also demonstrate the effectiveness of separation criteria demarcated by 'Equivalent Level of Safety' (ELoS). Statistical analysis for 100 sampled environments shows the reliability of the proposed method, which captured the characteristics of sUAVs and could be utilized for elaborative separation framework in structured low-altitude airspace. 

Demarcation method of safety separations for sUAV based on collision risk estimation

To improve the trajectory prediction accuracy of unmanned aerial vehicles (UAVs) with random behavior intentions, this paper presents a short-term four-dimensional (4D) trajectory prediction method based on spatio-temporal trajectory clustering. A spatio-temporal trajectory clustering algorithm is first designed to cluster the UAV trajectory segments divided by a fixed time window. Each trajectory segment is given a category label that represents some certain type of behavior characteristics, such as climbing, turning, descending, etc. The convolutional neural network (CNN) is used to identify the category label of a given trajectory segment by learning the behavior characteristics of different trajectory segments. Based on the long-short-term memory network (LSTM), a short-term trajectory prediction model for different categories of label trajectory segments is established. The global trajectory prediction includes several steps adopting the corresponding prediction models. Historical trajectory data of UAVs are used to validate the proposed prediction method. Experiment results indicate that the method can obtain obviously better prediction accuracy in a short prediction time range (0-3s) with acceptable efficiency compared to LSTM, GRU and velocity trend extrapolation. 

Short-Term 4D Trajectory Prediction for UAV Based on Spatio-Temporal Trajectory Clustering

The frequent intrusion of unmanned aerial vehicles (UAVs) into civil aviation airports poses a severe challenge to the safety management of the airport. The subsequent flight delays and even the closure of the runway have seriously affected the normal operation of the airport. To measure the collision risk of the Unmanned-Manned (U-M) encounter (intruding UAV to manned aircraft), a collision risk assessment platform is developed in this paper to predict the U-M encounter collision risk of the airport restricted area. The designed platform can be used to dynamically generate 3D trajectories of the U-M encounter, and to assess the collision risk with the changing of the relative distance in the probabilistic curve. Further, to predict the collision risk of the airport restricted area, the collision risk heatmap representation is then introduced. A case study of Changi Airport, Singapore is employed to generate the runway collision risk heatmap based on the designed collision risk assessment platform. The results demonstrate how the collision risk of the separate runway and the integrated runway distributes. 

/pdf/uav-collision-risk-assessment-in-terminal-restricted-area-by-3mcx5evk.pdf

UAV Collision Risk Assessment in Terminal Restricted Area by Heatmap Representation

Drones play an important role in modern cities, they bring convenience but also bring corresponding risks. Falling drones can cause casualties or damage to urban facilities and serious property damage, which increases the third-party risk problem. An effective way to reduce these third-party risks is to avoid high-risk areas in path planning, but most current path planning methods are optimized to minimize flight distance, and third-party risk costs are rarely considered. In this paper, a comprehensive risk-cost UAV path planning method is proposed, which evaluates urban risk by establishing a third-party risk model that incorporates obstacle risk, death risk, and property loss risk. This paper proposes a Min-cost A* algorithm based on city risk assessment, and smooths the generated low-risk path through the improved Floyd algorithm. The results show that the path planning method can effectively reduce the risk in the flight path, improve the reliability of the UAV flight path in the urban environment, and solve the problem of planning the third-party risk path of the UAV. 

UAV path planning based on third-party risk modeling

Collision probability estimation for small unmanned aircraft systems

With the dramatic development of small unmanned aircraft systems (sUAS), how to ensure sUAS safety operation has been a growing concern. This article proposes a fast probabilistic collision detection method for sUAS based on probability density function approximations. Firstly, cylindrical collision zones for sUAS and obstacles are established by geometrical methods for simplifying collision modeling, and instantaneous collision probability for sUAS is expressed by a triple integral. Secondly, a rapid estimation algorithm is derived for instantaneous collision probability, and then the predicted collision probability in probabilistic collision detection can be obtained by the maximum of instantaneous collision probabilities during the encounter. Randomized tests indicate that the average computation time of the proposed algorithm is less than 0.001s, and the Mean Absolute Error (MAE) is less than 0.01 and the Root Mean Squared Error (RMSE) is less than 0.02. Finally, numerical simulations are carried out to analyze the influence of parameters, including crossing angle, predicted separation at the closest point of approach (CPA), and predicted time to CPA, on collision probabilities. The optimal detection time for collision detection is also discussed in the different types of encounters. The collision detection method proposed in this article can provide support for real-time collision avoidance and the definition of dynamic safety bounds for sUAS.

/pdf/fast-collision-detection-for-small-unmanned-aircraft-systems-3xzueul0el.pdf

Fast Collision Detection for Small Unmanned Aircraft Systems in Urban Airspace

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Collision probability estimation for small unmanned aircraft systems

Fast Collision Detection for Small Unmanned Aircraft Systems in Urban Airspace