Task Quantifying Effective Crew Performance with Advanced Automated-Robotic Systems Risk
Last Published:  07/31/19 10:05:33 AM (Central)
Short Title: Crew/HARI Performance
Responsible HRP Element: Human Factors and Behavioral Performance
Collaborating Org(s):
Other:
Funding Status: Planned-Funded - Task expected to be within budget
Procurement Mechanism(s):
Solicited
Aims:

Future human spaceflight missions will have mixed agents (be it crew, robots, or automated systems) distributed across various distances from Earth. These hetereogeneous, non-colocated systems will have to be controlled by crew or ground controllers. There will be different types of robots, for instance free-flyers, dexterous manipulators, and rovers, as well as a variety of automated systems, such as cognitive assistants, self-verifying planetary power stations, and autonomous fault, detection, isolation, and recovery systems.   The supervision, command, and control of the robots and automated systems will have latency due to these distances from commanding operators, not to mention any additional inherent system latency due to the system’s dynamics. Operators must efficiently, effectively, and safely command these hetereogeneous systems while still minimizing training. NASA needs HARI interaction design guidelines and/or requirements, taking into account human capabilities, that will ensure flexible and robust interfaces for successful task performance by crew and ground teams to command and control advance automation-robotic systems.  As such, we need consider countermeasure designs that lead to effective crew performance with these complex systems (three prototype countermeasure solutions) as well as formalize methods and metrics to monitor the complex relationship between mixed agents, i.e., human, automation, and robotic systems. To date, performance measures have focused on human performance, such as situation awareness, trust, complacency, and workload. We need to better understand how human performance metrics are related to integrated system performance.

Aims:

  1. Determine and distinguish methods, tools, and methods for human-system performance. Identify trades between human performance measures and metrics, selecting appropriate performance measures that include subsystems and automation/robotic systems effects as well as human performance measures.

  2. Identify critical, spaceflight-specific human interaction challenges for distribute system control under various communication latencies. Distributed systems must be highly autonomous subsystems required by exploration missions. Conduct research that demonstrates proposed countermeasure design is effective. Recommend countermeasure guidelines that address human performance to ensure being proficient at operations does not exceed one hour.

  3. Identify critical, spaceflight-specific human interaction challenges for highly dexterous robotic system control under various communication latencies. Conduct research that demonstrates proposed countermeasure design is effective.  Recommend countermeasure guidelines that address human performance to ensure being proficient at operations does not exceed one hour.

  4. Identify critical, spaceflight-specific human interaction challenges for free-flyer and rover robotic systems control under various communication latencies. Conduct research that demonstrates proposed countermeasure design is effective. Recommend countermeasure guidelines that address human performance to ensure being proficient at operations does not exceed one hour.

  5. Validate each of these countermeasures and guidelines within a NASA ground analog, in conjunction with other NASA human-automation systems. Countermeasure designs should be prototype hardware/software systems demonstrating guidelines.

  6. Propose design guidelines, requirements, or standards for operator(s) to seamlessly manage supervisory command and control of distributed, heterogeneous agents for exploration class missions.

Potential outcomes of this research are to identify (a) a maximum number of mixed-agents per operator that can be successfully supervised and controlled, (b) a command and control strategy that is based on latency for HAR system designers to allocate control across HAR system, and (c) methods for improving communication and coordination across distributed mixed-agents. Trades between different performance measures and metrics should be examined to determine recommended guidelines.

 

This research will close HARI-02 and HARI-03 gaps as it will provide guidelines that provide human-automation-robotic integration guidelines for exploration missions; and it will provide methods and metrics to quantify human-automation-robotic performance

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