Designing Unmanned Aircraft Systems (UASs) for optimal autonomy while meeting user requirements is quite challenging. Researchers have focused on improving autonomy algorithms and verification methods to ensure safe and reliable autonomous behavior in UASs, but little research has been conducted on requirements engineering for UASs to answer design questions and explore the trade space for using autonomy to satisfy user requirements. This paper introduces a method to determine an optimal set of autonomous capabilities that satisfies UAS user requirements in the early stages of conceptual design. The method uses a modified Autonomy Requirements Engineering (ARE) process that applies quantitative measures and statistical analysis to Goal-Oriented Requirements Engineering (GORE). We demonstrate this method in a case study of a “disaster robot,” i.e., a hazard response UAS for which the autonomy requirements were optimized using a goal model developed in the Goal-oriented Requirement Language (GRL), as implemented in the modeling tool jUCMNav. The high-level goals of the hazard response UAS—system performance, cost, and safety—were evaluated using the formula-based GRL strategy evaluation algorithm resident in jUCMNav version 6.0. An autonomy trade space study was conducted through a Design and Analysis of Simulation Experiments (DASE). Our designed simulation experiment inserted the number of trials (evaluation strategies) and inputs into the goal model, and evaluation data were analyzed to optimize design factors based on user weightings of the response variables. This paper presents a structured method of ARE for UASs, which could be adopted more broadly across other domains, demonstrating how to optimize autonomous capabilities for different design conditions.

中文翻译：

---

无人机系统自主性需求目标模型分析

设计无人驾驶飞机系统 (UAS) 以实现最佳自主性，同时满足用户要求非常具有挑战性。研究人员一直专注于改进自主算法和验证方法，以确保 UAS 中安全可靠的自主行为，但很少对 UAS 的需求工程进行研究，以回答设计问题并探索利用自主性满足用户需求的交易空间。本文介绍了一种在概念设计的早期阶段确定满足 UAS 用户需求的最佳自主能力集的方法。该方法使用修改后的自治需求工程 (ARE) 过程，该过程将定量测量和统计分析应用于面向目标的需求工程 (GORE)。我们在“灾难机器人”的案例研究中演示了这种方法，即，一种危险响应 UAS，其自主性要求使用在面向目标的需求语言 (GRL) 中开发的目标模型进行了优化，如在建模工具 jUCMNav 中实现的那样。使用 jUCMNav 6.0 版中基于公式的 GRL 策略评估算法评估了危险响应 UAS 的高级目标——系统性能、成本和安全性。自主贸易空间研究是通过模拟实验的设计和分析 (DASE) 进行的。我们设计的模拟实验将试验次数（评估策略）和输入插入目标模型，并分析评估数据以基于响应变量的用户权重优化设计因素。本文提出了一种用于 UAS 的结构化 ARE 方法，该方法可以在其他领域更广泛地采用，