NASA’s future missions will push the bounds of human-space exploration and challenge the mission designers and engineers to create automated systems that will enable the joint human-automation teams to operate more autonomously as they move further from terrestrially based mission control and the time lag of communication becomes a challenge.
Objective Function Allocation Method for Human-Automation/Robotic Interaction using Work Models that Compute
Future manned space missions will require astronauts to work with a variety of robotic systems. To develop effective human-robot teams, NASA needs objective methods for function allocation between humans and robots. This study develops an objective methodology for function allocation between humans and robots for future manned space missions. Some problems that need to be addressed in function allocation include: (a) monitoring of agents, (b) agents waiting on other agents (idle time), (c) high task load of agents, (d) excessive amount of communication required.
NSF-CPS: Adaptive Intelligence for Cyber-Physical Automotive Active Safety System Design and Evaluation
The main objective of this research is to use techniques and models from human factors, computational neuroscience, and adaptive and real-time optimal control theory in order to investigate the effects of the introduction of learning and adaptation to the next generation of ASCS. In particular, we will:
(a) Learn the driver’s habits, driving skills, patterns and weaknesses.
(b) Model his/her current cognitive state along multiple dimensions such as attentiveness, aggressiveness, etc.
The most common causes of aircraft incidents and accidents today are pilot spatial disorientation and/or loss of energy situation awareness. To re-assess the underlying mechanisms of SD and/or LESA, we are building a computational model of human pilot. Having a computational model allows us to create fast-time simulations instead of relying on the real-time human-in-the-loop simulations. The purpose is to utilize this novel model for large-scale evaluations spanning wide range of potential conditions and variations in flight crew behavior.
The research aims to contribute to analyze human/automation roles and responsibilities. This work will provide scenario-based methods for validation and verification of current day and NextGen concepts of operation and automated forms supporting these concepts of operation.
NSF GRFP - Design Knowledge Coordination: Enhancing Novice Aerospace Engineers’ Coordinated Decision-Making
Design Knowledge Coordination is a structured approach to integrating design considerations across the different disciplines in engineering design through use of goals, tasks, metrics, and decisions. A key aspect to connecting coordination to aerospace engineering design is the recognition that this process encompasses distinct, yet interdependent aspects of design.
This work addresses three research questions:
Human spaceflight is arguably one of mankind's most challenging engineering feats, requiring carefully crafted synergy between human and technological capabilities. One critical component of human spaceflight pertains to the activity conducted outside the safe confines of the spacecraft, known as Extravehicular Activity (EVA). Successful execution of EVAs requires significant effort and real-time communication between astronauts who perform the EVA and the ground personnel who provide real-time support.
Collision avoidance on large transport aircraft involves many components: Air Traffic Control (ATC), the pilot, and collision avoidance systems such as the Traffic alert and Collision Avoidance System (TCAS). This research explores pilots’ interactions with ATC, the environment, and current and future collision avoidance systems such as TCAS and systems using ADS-B, ACAS-X, and Interval Management.
We are interested in machines that can learn new things from people who are not Machine Learning (ML) experts. We propose a research agenda framed around the human factors (HF) and ML research questions of teaching an agent via demonstration and critique. Ultimately, we will develop a training simulation game with several nonplayer characters, all of which can be easily taught new behaviors by an end-user.
Decision makers are consistently asked to make decisions about the course of action required to achieve mission success regardless of the time pressure and the quantity and quality of information available. To be successful, they will adapt their decision strategies to the environment and even use heuristics, simple rules that use little information and can be processed quickly. To support these decision makers, we are designing proactive decision support systems that support adaptive decision making along a range analytic and heuristic strategies.