Human-Computer Collaboration in Adaptive Supervisory Control and Function Allocation of Autonomous System Teams
Human-Computer Collaboration in Adaptive Supervisory Control and Function Allocation of Autonomous System Teams
复制标题
自治系统团队自适应监控与功能分配中的人机协作
DOI：
--
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Olinda Rodas
中科院分区：
文献类型：
--
作者：
R. Gutzwiller;D. Lange;J. Reeder;R. Morris;Olinda Rodas
讨论了在多个异构无人系统协作环境中，用于任务自适应性能的协作人机系统的基础。提出了一种用于监视任务和整个系统属性（包括操作员、自主性、世界状态和使命）的飞行控制系统。这些变量在全局系统的模型中进行比较，并且可以基于任务健康的角度执行重新分配任务的策略（例如通过接管传入任务来减轻过载的用户）。操作员仍然可以通过任务管理器控制分配，任务管理器还提供了一个功能分配界面，并完成了透明度的初步尝试。我们计划使用机器学习和操作员反馈，从人在回路实验中学习功能分配的配置。集成机器学习、机器学习和操作员反馈有望提高协作、透明度和人机性能。
The foundation for a collaborative, man-machine system for adaptive performance of tasks in a multiple, heterogeneous unmanned system teaming environment is discussed. An autonomics system is proposed to monitor missions and overall system attributes, including those of the operator, autonomy, states of the world, and the mission. These variables are compared within a model of the global system, and strategies that re-allocate tasks can be executed based on a mission-health perspective (such as relieving an overloaded user by taking over incoming tasks). Operators still have control over the allocation via a task manager, which also provides a function allocation interface, and accomplishes an initial attempt at transparency. We plan to learn about configurations of function allocation from human-in-the-loop experiments, using machine learning and operator feedback. Integrating autonomics, machine learning, and operator feedback is expected to improve collaboration, transparency, and human-machine performance.