To develop effective Human-automation/robotic (HAR) systems, NASA requires the development of methods and tools to inform the decisions regarding function allocation between robots and crewmembers that are able to objectively assess the implications of the assignment of these roles for the human-system performance trade space. This research will establish a validated method for the evaluation of function allocation between robots and automated systems and their human crew mates for use in deep space exploration missions. It will further produce computational models of different possible combinations of a three person human crew and various classes of robots for a variety of tasks which can be used as-is for additional analysis or modified for future concepts of operation. The method for function allocation will apply fast-time simulation, which will be validated by ground-based human-in-the-loop experimentation. It may also include human-in-the-loop simulation in an analog environment.
This project used current-day computational methods to model and simulate the human-robot teams at work. Investigators expanded on existing methods used in aeronautics to advance the field of computational simulation of function allocation for the improvement of manned space exploration where they encountered additional challenges of agents with differential capabilities, time delay of communication, and the need to represent limitations in resources which might be both physical as well as informational. The capability to simulate how human-robot teams work together will help systems designers understand the interaction between humans and space robotics to allow for robust and effective as well as efficient teamwork across missions and different crew-robot complements. This research also impacts the growing field of human-robot teaming, as robots continue to advance technically and become less like tools for humans and more like peers and teammates.
The research addresses three main research questions:
A three year effort was proposed to address these questions. In the first year a model of the function allocation design space that exists between humans and robots in deep space exploration missions was determined. This was done using a computational framework called Work Models that Compute (WMC), which allows investigators to model dynamical systems (such as space vehicles and robots), automated systems (such as the automated rendezvous and docking system) and human agents working together to achieve common goals. WMC was custom designed to model function allocation and to measure eight metrics of function allocation previously established by the proposers. In the second year the design space, deeply investigating each metric such as taskload, authority responsibility mismatch, coherency, etc., was explored, while beginning the validation process through the use of human-in-the-loop experiments with simulated robots. In the final year the combied effects was explored as the design space constraints, the tasks, crew stress levels and function allocation options are varied. Validation efforts continued using human-in-the-loop experiments with a combination of simulated robots and/or real robots. These experiments systematically explored a large number of conditions such that they serve not only to demonstrate the function allocation chosen by the method, but also to validate the method.
Computational simulation of function allocation can provide objective insight in the teamwork that is required for human-robot interaction. The researchers have created models suitable for such computational simulation, representing key aspects of human-robot interaction relative to the allocation of tasks between them. They present two human-robot teaming scenarios: one on the International Space Station (ISS) with multiple robots and the other on the surface of the moon with a lunar rover. These scenarios demonstrate the capability to both model complex teams and also introduce kinematics and dynamics to the models.
The researchers also present various metrics and measures and how they relate to teaming fluency and human perceptions of the robot. An experimental confirmation was conducted to test how similar the models performed to real scenarios.
The benefit of using computational simulations to evaluate function allocation is that they can be used to identify potential pros and cons of various function allocations in the earliest design stages, without a need to conduct costly human-in-the-loop experiments. When potential problems are identified early in design, changes to the supporting technology and operations can still be made to alleviate the negative effects of a selected function allocation.
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