人在环多无人机任务的模型驱动需求

Ankit Agrawal, Jan-Philipp Steghöfer, J. Cleland-Huang
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引用次数: 14

摘要

使用半自动无人驾驶飞行器(uav或无人机)来支持紧急响应场景,如火灾监视和搜救,具有巨大的社会效益的潜力。机载传感器和人工智能(AI)使这些无人机能够在环境中自主操作。然而,在规划和指导无人机完成任务时,人类智能和领域专业知识至关重要。因此,人类和多架无人机需要作为一个团队进行协作,以成功执行时间紧迫的任务。我们提出了一个元模型来描述人类操作员和自主无人机群之间的相互作用。该元模型还提供了一种描述无人机和人的角色以及自主决策的语言。我们用需求引出问题的模板来补充元模型,以导出特定任务的模型。我们还确定了人类应该与无人机合作的常见场景,以增强无人机的自主性。我们通过一个搜索和救援任务的例子介绍了元模型和需求引出过程,在这个任务中,多个无人机与人类合作应对紧急情况。然后,我们将其应用于第二种场景,其中无人机支持第一响应者对抗结构火灾。我们的研究结果表明,元模型和问题模板支持这些复杂任务的人在环交互建模,表明它是多无人机任务的人在环交互建模的有用工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Model-Driven Requirements for Humans-on-the-Loop Multi-UAV Missions
The use of semi-autonomous Unmanned Aerial Vehicles (UAVs or drones) to support emergency response scenarios, such as fire surveillance and search-and-rescue, has the potential for huge societal benefits. Onboard sensors and artificial intelligence (AI) allow these UAVs to operate autonomously in the environment. However, human intelligence and domain expertise are crucial in planning and guiding UAVs to accomplish the mission. Therefore, humans and multiple UAVs need to collaborate as a team to conduct a time-critical mission successfully. We propose a meta-model to describe interactions among the human operators and the autonomous swarm of UAVs. The meta-model also provides a language to describe the roles of UAVs and humans and the autonomous decisions. We complement the meta-model with a template of requirements elicitation questions to derive models for specific missions. We also identify common scenarios where humans should collaborate with UAVs to augment the autonomy of the UAVs. We introduce the meta-model and the requirements elicitation process with examples drawn from a search-and-rescue mission in which multiple UAVs collaborate with humans to respond to the emergency. We then apply it to a second scenario in which UAVs support first responders in fighting a structural fire. Our results show that the meta-model and the template of questions support the modeling of the human-on-the-loop human interactions for these complex missions, suggesting that it is a useful tool for modeling the human-on-the-loop interactions for multi-UAVs missions.
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