Risk Assessment Model for UAV Cost-Effective Path Planning in Urban Environments

Abstract

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.

Cellular and Virtualization Technologies for UAVs: An Experimental Perspective

“…It is essential to consider that Multi-UAV operations entail certain risks, not only in colliding with aerial traffic (i.e., other UAVs, human-crewed aircraft in the proximity of airports), and accidents including people and vehicles on the ground in populated areas. Authors in [ 54 ] proposed a complete risk assessment model for UAV operation in urban environments with three main risk categories (people, vehicles, and human-crewed aircraft). The mission planner generally contemplates the operations and factors involving the most significant risk.…”

6G Enabled Unmanned Aerial Vehicle Traffic Management: A Perspective

“…However, another study [13] proposed a multi-agent path finding (MAPF) based on an enhanced conflict-based search mechanism for UTM that performs better and has more time efficiency than incremental planning based on Cooperative A*, and it can satisfy timely response on the delivery request to UTM service users. Several other studies have been conducted to improve the path planning for UAV systems [14], [15], [16], [15], [17], and [18].…”

The Rise of Drones in Internet of Things: A Survey on the Evolution, Prospects and Challenges of Unmanned Aerial Vehicles

“…The authors in [102] introduce a set of risks that need to be quantified or qualified and mitigated. Similarly, in [103] a comprehensive risk assessment model based on collision probability is proposed for UAV operation in urban environments. Three risk categories are considered, namely property, people and vehicles.…”

“…According to [127], one of the first proposals for a UTM construct -"Internet of Drones" [128] -emphasised the importance of such interdisciplinary approaches by combining best practices in air traffic control networks, cellular networks as well as the internet to devising one of the first UTM constructs in scientific literature. Since then we have witnessed a clear interest in such holistic research as seen in [5], [102], [127], [129]- [136] on UTM systems, in addition to domain-specific research on airspace design in [101], [111], [113]- [120], collision avoidance and risk mitigation in [103]- [106], [137]- [146] and communication and cybersecurity in [96]- [100], [147]- [159] as summarised below in Table 2.…”