Analyzing and Modeling Human Adaptive Spatial Control Skills Using General Principles of Optimal Control

该奖项的研究目标是对人类空间控制技能进行逆向工程，以弥合人类和自主系统之间的差距。自适应和多功能空间控制能力对于成功部署无人机和其他自主控制应用至关重要。了解人类如何获得这些技能对于人机系统的设计也很重要，例如直升机的主动安全系统甚至远程手术系统。该研究方法结合了心理行为实验和控制理论原理的应用。工作假设是人类的空间行为是由一种模型预测控制（MPC）过程和空间值函数（SVF）相结合决定的。MPC流程描述了如何根据即时感知的环境在线生成轨迹；SVF描述了如何在MPC过程中对全局环境和任务元素进行编码和使用。然后，该模型将用于研究飞行员如何在出现干扰、环境不确定性或出现新情况时调整他/她的策略。可交付成果包括建模框架的细节、研究成果的文档、跨学科学生教育和研究经验的成果。如果成功，这项研究将导致开发新的算法来模拟和复制人类在空间控制任务中的适应性，为具有前所未有的性能和适应性的自动驾驶汽车控制新技术铺平道路。对人类指导技能的改进理解将为新的操作员界面提供一个门户，增强人类的功能、有效性和安全性。此外，建模框架将为旨在理解大脑的神经学研究提供基础。部分控制过程的实施。最后，拟议的跨学科研究活动将促进新的教学和推广活动，同时吸引来自不同背景的学生。

The research objective of this award is to reverse-engineer human spatial control skills, with the goal of bridging the gap between human and autonomous systems. Adaptive and versatile spatial control capabilities are essential to successfully deploying unmanned aerial vehicles and other applications of autonomous control. understanding how humans achieve these skills is also important for the design of man-machine systems, such as active safety systems for helicopters or even tele-surgery systems. The research approach combines psycho-behavioral experiments and the application of control-theoretic principles. The working hypothesis is that human spatial behavior is determined by combining a type of model predictive control (MPC) process and a spatial value function (SVF). The MPC process describes how a trajectory is generated online,based on the immediate sensed environment; the SVF describes how the global environment and task elements are encoded and used in the MPC process. This model will then be used to study how the pilot adapts his/her strategies in the presence of disturbances, environment ncertainties, or when presented with novel ituations. Deliverables include the details of the odeling framework, documentation of research results, and outcomes of interdisciplinary student education and research experience.If successful, this research will lead to developing new algorithms to model and replicate human daptability in spatial control tasks, paving the way for novel technologies for autonomous vehicle ontrol with unprecedented performance and adaptability. The improved understanding of human uidance skills will provide a gateway to new operator interfaces, augmenting human unctionality, effectiveness, and safety. Furthermore, the modeling framework will providefoundations for neurological studies, aimed at understanding the brain?s implementation of the patial control processes. Finally, the proposed interdisciplinary research activities will promote ew teaching and outreach activities, while attracting students from diverse backgrounds.