Innovative Systems for Human Supervisory Control of Unmanned Vehicles

Abstract In the backdrop of increasingly intelligent machines, important issues of law have been raised by the use of robots that operate autonomous from human supervisory control. In particular, when systems operating with autonomous robot’s damage property or injure humans, it may be difficult to determinewho’s at fault and therefore liable under current legal schemes. This paper reviews product liability and negligence tort law which may be used to allocate liability for robots that damage property or cause injury. Further, the paper concludes with a discussion of different approaches to allocating liability in an age of increasingly intelligent and autonomous robots directed by sophisticated algorithms, analytical, and computational techniques

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Levels of abstraction in human supervisory control teams

Journal of Enterprise Information Management ◽

10.1108/17410390610708535 ◽

2006 ◽

Vol 19 (6) ◽

pp. 679-694 ◽

Cited By ~ 8

Author(s):

N.A. Stanton ◽

M.J. Ashleigh ◽

A.D. Roberts ◽

F. Xu

Keyword(s):

Supervisory Control ◽

Levels Of Abstraction ◽

Human Supervisory Control

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Neural networks and genetic algorithms can support human supervisory control to reduce fossil fuel power plant emissions

Cognition Technology & Work ◽

10.1007/s10111-002-0107-6 ◽

2003 ◽

Vol 5 (2) ◽

pp. 107-126 ◽

Cited By ~ 9

Author(s):

K. Li ◽

S. Thompson ◽

P. A. Wieringa ◽

J. Peng ◽

G. R. Duan

Keyword(s):

Neural Networks ◽

Genetic Algorithms ◽

Power Plant ◽

Supervisory Control ◽

Fossil Fuel ◽

Power Plant Emissions ◽

Fossil Fuel Power Plant ◽

Plant Emissions ◽

Human Supervisory Control

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Defining the Challenges Operators Face when Controlling Multiple Unmanned Vehicles

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/154193120504900338 ◽

2005 ◽

Vol 49 (3) ◽

pp. 392-396

Author(s):

Peter N. Squire ◽

Raja Parasuraman

Keyword(s):

Supervisory Control ◽

Unmanned Vehicles ◽

Human Robot Interaction ◽

Human Operator ◽

Simulation Environment ◽

Robot Interaction ◽

Human In The Loop ◽

Control Interface ◽

New Findings ◽

Single Operator

To achieve effective human-robot interaction (HRI) it is important to determine what types of supervisory control interfaces lead to optimal human-robot teaming. Research in HRI has demonstrated that operators controlling fewer robots against opponents of equal strength face greater challenges when control is restricted to only automation. Using human-in-the-loop evaluations of delegation-type interfaces, the present study examined the challenges and outcomes of a single operator supervising (1) more or less robots than a simulated adversary, with either a (2) flexible or restricted control interface. Testing was conducted with 12 paid participants using the RoboFlag simulation environment. Results from this experiment support past findings of execution timing deficiencies related to automation brittleness, and present new findings that indicate that successful teaming between a single human operator and a robotic team is affected by the number of robots and the type of interface.

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