RI: Medium: Computational Models, Interaction Mechanisms, and Planning Algorithms for Semi-Autonomous Systems

自主系统对社会产生了变革性的影响，因为它们有助于减少人力劳动，降低风险和成本，提高生产力和效率。它们已被部署在从家用产品到太空探索飞行器的广泛领域。然而，在许多领域，部署完全自主系统仍然存在相当大的障碍。这些障碍从技术到道德和法律的问题都有。例子包括驾驶汽车，搜索和救援行动中的机器人部署，自动化农业和机器人手术。当完全自主不可行时，通常希望自动化整个过程的部分。该项目提供了半自主系统规划的全面研究-在某些条件下能够自主操作的系统，但可能需要手动控制才能完成手头的任务。半自主系统的规划是具有挑战性的，因为它必须考虑到人类操作员和自动化系统的不同技能，它们之间的通信需要促进控制的顺利转移，人类响应的不确定性，参与程度和接管控制的准备程度，以及在解释或遵循计划时人为错误的可能性。该项目采用跨学科的方法，解决了计算挑战以及人类参与时出现的挑战。以半自动驾驶为主要研究领域，研究活动包括：设计通用图形模型来表示半自动系统的协同控制问题;开发有效的方法来表示和获取参与者的能力模型;开发高效的决策理论规划算法，利用启发式搜索和可达性分析来创建共享计划;开发算法来计算有关共享计划的重要统计数据和运行时反馈;开发捕获态势感知和人为错误模型的方法，并将其纳入规划过程;并为半自治系统中的规划创建一组具有挑战性的场景和测试问题。使用几个测试台，包括两个现实的驾驶模拟器的方法进行评估。

Autonomous systems offer transformational impacts on society as they help reduce human labor, decrease risks and costs, and improve productivity and efficiency. They have been deployed in a wide range of domains from household products to space exploration vehicles. In many areas, however, there are still considerable barriers to the deployment of fully autonomous systems. These barriers range from technological to ethical and legal issues. Examples include driving a car, robot deployment in search and rescue operations, automated farming, and robotic surgery. When full autonomy is not feasible, it is often desirable to automate parts of the entire process. This project offers a comprehensive study of planning for semi-autonomous systems -- systems that are capable of autonomous operation under some conditions, but may require manual control in order to complete the task at hand. Planning for semi-autonomous systems is challenging because it must account for the different skills of the human operator and the automated system, the communication between them required to facilitate smooth transfer of control, the uncertainty about human responsiveness, engagement level and readiness to take over control, and the possibility of human error in interpreting or following the plan. The project takes an interdisciplinary approach that addresses the computational challenges together with the challenges that rise whenever the human is in the loop. With a focus on semi-autonomous driving as the primary domain, research activities include: designing general-purpose graphical models to represent the problem of collaborative control of semi-autonomous systems; developing effective methods to represent and earn competence models of the actors; developing efficient decision-theoretic planning algorithms that exploit heuristic search and reachability analysis to create the shared plan; developing algorithms to compute vital statistics and runtime feedback about the shared plan; developing ways to capture models of situation awareness and human errors, and factor them into the planning process; and creating a set of challenging scenarios and test problems for planning in semi-autonomous systems. Evaluation of the approach is conducted using several testbeds including two realistic driving simulators.