人与UAS合作的任务分配决策:工作负载,信任和自信的影响
Yining Elena Zhang1, Jing Chen1, Liang Sun2
1Rice University, Houston, TX, USA.
概括
本研究探讨了多操作员无人机系统 (UAS) 任务中工作负载如何影响人类决策. 这些发现将有助于优化任务分配,以改善无人机操作.
科学领域:
- 人与计算机的交互
- 航空航天工程 航空航天工程
- 认知心理学 认知心理学
背景情况:
- 无人机系统 (UAS) 对于城市空中交通,包裹交付和应急响应日益重要.
- 采用m:N架构,涉及多个运营商 (m) 管理众多车辆 (N),提高了无人机系统的运营效率.
- 了解人类因素对于在复杂场景中有效运行无人机系统至关重要.
研究的目的:
- 在模拟的m:N UAS环境中调查工作负载对操作员任务分配决策的影响.
- 在不同的工作负载条件下,研究信任和自信在操作员决策中的调解作用.
- 为了比较UAS飞行员和初学者大学生之间的决策策略.
主要方法:
- 使用m:N架构设计了一个模拟的UAS包裹交付任务.
- 参与者 (UAS飞行员和大学生) 通过视频刺激暴露在两个工作负载条件下.
- 在模拟后测量了感知的工作量,信任和自信;记录了任务分配偏好.
主要成果:
- 工作负载在m:N UAS操作中显著影响任务分配决策.
- 信任和自信调解了工作负载和决策之间的关系.
- 专业知识水平 (UAS飞行员与学生) 影响任务分配策略和对工作负载的反应.
结论:
- 操作员的工作量和信任和自信等人为因素对于设计有效的m:N UAS任务分配系统至关重要.
- 根据操作员的专业知识量身定制任务分配策略对于优化UAS任务性能至关重要.
- 未来的研究应该集中在这些发现的现实世界的验证上,以提高无人机系统的安全性和效率.
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