Remote teleoperation of an unmanned aircraft with a brain-machine interface: Theory and preliminary results

Akce, Abdullah; Johnson, M. J.

doi:10.1109/robot.2010.5509671

Cited by 30 publications

(24 citation statements)

References 15 publications

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“…There are some works to control Unnamed Aerial Vehicle (UAV's) with BCI systems and aircrafts. In 2010, Akce et al [29] presents preliminary results of an interface that allows a human pilot to remotely teleoperate an unmanned aircraft flying at a fixed altitude using 8 electrodes to distinguish between left and right hand MI. They claimed the feasibility of this approach.…”

Section: Related Workmentioning

confidence: 99%

Testing Brain–Computer Interfaces with Airplane Pilots under New Motor Imagery Tasks

Rodríguez-Bermúdez¹,

López-Belchí²,

Girault³

2019

IJCIS

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The purpose of a brain-computer interface (BCI) is the recording of brain signals to translate them into commands. This work proposes new naturalistic and intuitive motor imagery (MI) BCI task for aircraft Pilots for a BCI System, and explore if they take advantage of their previous motor experience in MI-BCI experiments. Results show that Pilots get better performance than a control group and that it is possible to get good classification accuracy results with new naturalistic MI tasks.

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Section: Related Workmentioning

confidence: 99%

Testing Brain–Computer Interfaces with Airplane Pilots under New Motor Imagery Tasks

Rodríguez-Bermúdez¹,

López-Belchí²,

Girault³

2019

IJCIS

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“…Research using neural interfaces follows several other unmanned aircraft that were successfully flown/controlled by a pilot using an electroencephalogram (EEG) as input and live onboard video as visual feedback. 38,39 All autonomous research would be done using the onboard avionics package in autopilot mode, which would control the aircraft. It is important to note that there would be a pilot capable of remotely taking over control if necessary, via a toggle switch on the transmitter.…”

Section: Background and Motivationsmentioning

confidence: 99%

Development and Initial Testing of the Aero Testbed: A Large-Scale Unmanned Electric Aerobatic Aircraft for Aerodynamics Research

Dantsker

Johnson

Selig

2013

31st AIAA Applied Aerodynamics Conference

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This paper describes the development of a large 35%-scale unmanned aerobatic platform named the UIUC Aero Testbed, which is primarily intended to perform aerodynamics research in the full flight regime. The giant-scale aircraft with a 105-in (2.7-m) wingspan and weight of 37 lb (17 kg) was constructed from a commercially available radio control model aircraft with extensive modifications and upgrades including a 12-kW electric motor system that provides a thrust-to-weight ratio in excess of 2-to-1. It is equipped with an avionics suite that contains a high-frequency, high-resolution six degree-of-freedom (6-DOF) inertial measurement unit (IMU) that allows the system to collect aircraft state data. This information set can be used to generate high-fidelity aerodynamic data that can be used to validate high angle-of-attack flight-dynamic models. Collaboration in this project also led the Aero Testbed to have the capability to fly fully-and semi-autonomously in order to conduct autonomous flight research. A literature review of aerobatic unmanned aircraft used for research is first presented. Then the background and motivations for developing this platform are discussed. This is followed by a description of the planning and development that was involved. Finally, initial test flight results are presented, which include flight path trajectory plots of several aerobatic maneuvers. Nomenclature AV I= avionics integration ARF = almost ready to fly COT S = commercial off the shelf CG = center of gravity DOF = degree of freedom EEG = electroencephalogram IMU = inertial measurement unit RC = radio control

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“…In previous work [10], we made a heuristic choice, and used an ordered symbolic language to represent paths of piecewise-constant curvature. However, this decision made it hard to incorporate certain types of statistical information.…”

Section: Introductionmentioning

confidence: 99%

A brain-machine interface to navigate mobile robots along human-like paths amidst obstacles

Akce

Norton

2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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This paper presents an interface that allows a human user to specify a desired path for a mobile robot in a planar workspace with noisy binary inputs that are obtained at low bit-rates through an electroencephalograph (EEG). We represent desired paths as geodesics with respect to a cost function that is defined so that each path-homotopy class contains exactly one (local) geodesic. We apply max-margin structured learning to recover a cost function that is consistent with observations of human walking paths. We derive an optimal feedback communication protocol to select a local geodesicequivalently, a path-homotopy class-using a sequence of noisy bits. We validate our approach with experiments that quantify both how well our learned cost function characterizes human walking data and how well human subjects perform with the resulting interface in navigating a simulated robot with EEG.

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Remote teleoperation of an unmanned aircraft with a brain-machine interface: Theory and preliminary results

Cited by 30 publications

References 15 publications

Testing Brain–Computer Interfaces with Airplane Pilots under New Motor Imagery Tasks

Testing Brain–Computer Interfaces with Airplane Pilots under New Motor Imagery Tasks

Development and Initial Testing of the Aero Testbed: A Large-Scale Unmanned Electric Aerobatic Aircraft for Aerodynamics Research

A brain-machine interface to navigate mobile robots along human-like paths amidst obstacles

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