Multi-UAV Supervisory Control Interface Technology (MUSCIT)

2012 ◽  
Author(s):  
Thomas C. Hughes ◽  
John K. Flach ◽  
Mark P. Squire ◽  
James Whalen ◽  
Douglas J. Zimmer ◽  
...  
Keyword(s):  
Supervisory Control ◽  
Control Interface ◽  
Multi Uav

Research on Pipeline Gas Application System

2013 ◽  
Vol 433-435 ◽  
pp. 1836-1840
Author(s):  
Ai Zhang Guo ◽  
Guo Ling Liu
Keyword(s):  
Remote Control ◽  
Data Acquisition ◽  
Supervisory Control ◽  
Management System ◽  
Interface Structure ◽  
Network Technology ◽  
Intelligent Building ◽  
Application System ◽  
Intelligent Home ◽  
Control Interface

This paper studied the traditional supervisory control and data acquisition and investigates what is the most important in pipeline gas management system. Then a novel scheme is proposed for remote control interface on pipeline gas providing management. This scheme introduced GPRS and internet network technology to the traditional supervisory control and data acquisition. The details of System topological structure is given and Remote control interface structure are listed. At last, the system performance is analyzed and the results indicate that pipeline gas management system using remote control interface has the excellent protection performance and operability of RCI. It can meet the demand of intelligent home and intelligent building.

Defining the Challenges Operators Face when Controlling Multiple Unmanned Vehicles

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.

Analysis and Design of Interface for a Bio-Inspired Underwater Vehicle (BUV): Toward an Optimized Blend of Knowledge-driven and Intuitive Control

2017 ◽  
Vol 61 (1) ◽  
pp. 1725-1729
Author(s):  
Ruijie Zhu ◽  
Abhiraj Deshpande ◽  
Marisa Lockhart ◽  
Hilary Bart-Smith ◽  
Inki Kim
Keyword(s):  
Supervisory Control ◽  
Cognitive Task ◽  
Design Principles ◽  
Unmanned Vehicles ◽  
Analytic Framework ◽  
Analysis And Design ◽  
Control Functions ◽  
Control Interface ◽  
Main Challenge ◽  
The Cost

The supervisory control of unmanned vehicles is likely to be an important form of next-generation human-machine interaction. Although the effective design of control interface is critical for high-performance human-robot teams, there is little framework beyond general design principles in the Human Factors discipline. The main challenge is to find an optimal balance between knowledge-driven control functions and intuitive maneuvers. In general, the supervisory control of unmanned vehicles requires its operator to map the vehicle’s motion parameters with the set of control functions implemented in an interface. Due to the complexity of the control functions and underlying domain-specific knowledge, it usually takes significant time and efforts to learn the mapping relationship and familiarize oneself with the interface. In this regard, intuitive control interface is an obvious virtue that can save the cost of learning the interface, as well as acceptance by a larger group of users. With increasing types and numbers of unmanned vehicles/robots, a lack of intuitiveness can bring about substantial usability issues, including the cost of learning how to control a new vehicle, and the cost of switching to different types of vehicles. Despite the needs, the notion of intuitive control has little theoretical foundation, thus, difficult to implement through design practices. It is the ultimate goal of the current research to generate design principles that balance between knowledge-driven control and intuitive control by establishing an analytic framework of cognitive task monitoring. The analytic framework intends to estimate the cognitive processing underlying a sequence of control actions, thereby, provides empirical evidence of intuitiveness versus knowledge-dependency in control. The current research uses a Bio-inspired Underwater Vehicles (BUV) to apply the analytic framework under a variety of operational scenarios to monitor the operator interaction. To evaluate the degree of intuitiveness versus knowledge-dependency, the existent interface built in LabVIEW (Ver. 2017, National Instruments, Corp., Austin, TX) is being tested on a group of experts and novices under a variety of task scenarios. As a result, the current interface is evaluated regarding the cost of learning, i.e. the degree of reliance on knowledge, and the cost of switching to different control functions, i.e. the degree of counter-intuitiveness. Finally, the analytic outcomes lead to the redesign of the interface.

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