Jiang Chengpeng, Blom Henk, Rattanagraikanakorn Borrdephong
Faculty of Aerospace Engineering, Delft University of Technology, Delft, The Netherlands.
International School of Engineering, Chulalongkorn University, Bangkok, Thailand.
Risk Anal. 2024 Sep 14;45(5):1115-31. doi: 10.1111/risa.17649.
Advantages of commercial UAS-based services come with the disadvantage of posing third party risk (TPR) to overflown population on the ground. Especially challenging is that the imposed level of ground TPR tends to increase linearly with the density of potential customers of UAS services. This challenge asks for the development of complementary directions in reducing ground TPR. The first direction is to reduce the rate of a UAS crash to the ground. The second direction is to reduce overflying in more densely populated areas by developing risk-aware UAS path planning strategies. The third direction is to develop UAS designs that reduce the product in case of a crashing UAS, where is the size of the crash impact area on the ground, and is the probability of fatality for a person in the crash impact area. Because small UAS accident and incident data are scarce, each of these three developments is in need of predictive models regarding their contribution to ground TPR. Such models have been well developed for UAS crash event rate and risk-aware UAS path planning. The objective of this article is to develop an improved model and assessment method for the product In literature, the model development and assessment of the latter two terms is accomplished along separate routes. The objective of this article is to develop an integrated approach. The first step is the development of an integrated model for the product . The second step is to show that this integrated model can be assessed by conducting dynamical simulations of Finite Element (FE) or Multi-Body System (MBS) models of collision between a UAS and a human body. Application of this novel method is illustrated and compared to existing methods for a DJI Phantom III UAS crashing to the ground.
基于商业无人机系统的服务具有优势，但也存在缺点，即会给地面上的人群带来第三方风险（TPR）。特别具有挑战性的是，地面TPR的施加水平往往会随着无人机系统服务潜在客户的密度呈线性增加。这一挑战要求在降低地面TPR方面开拓互补方向。第一个方向是降低无人机坠落到地面的速率。第二个方向是通过制定风险感知型无人机路径规划策略，减少在人口更密集地区的飞越。第三个方向是开发无人机设计，以在无人机坠毁时降低乘积，其中是地面上坠毁影响区域的大小，是坠毁影响区域内人员死亡的概率。由于小型无人机事故和事件数据稀缺，这三个发展方向中的每一个都需要关于其对地面TPR贡献的预测模型。对于无人机坠毁事件率和风险感知型无人机路径规划，此类模型已经得到了很好的发展。本文的目的是开发一种改进的模型和评估方法来计算乘积。在文献中，后两个术语的模型开发和评估是沿着不同的路线完成的。本文的目的是开发一种综合方法。第一步是开发乘积的综合模型。第二步是表明可以通过对无人机与人体碰撞的有限元（FE）或多体系统（MBS）模型进行动态模拟来评估这个综合模型。本文说明了这种新方法的应用，并将其与大疆精灵III无人机坠落到地面的现有方法进行了比较。
