A Real-time Control Approach for Unmanned Aerial Vehicles using Brain-computer Interface

09/02/2018
by   Ravi M. Vishwanath, et al.
0

Brain-computer interfacing (BCI) is a technology that is almost four decades old and it was developed solely for the purpose of developing and enhancing the impact of neuroprosthetics. However, in the recent times, with the commercialization of non-invasive electroencephalogram (EEG) headsets, the technology has seen a wide variety of applications like home automation, wheelchair control, vehicle steering etc. One of the latest developed applications is the mind-controlled quadrotor unmanned aerial vehicle. These applications, how- ever, do not require a very high-speed response and give satisfactory results when standard classification methods like Support Vector Machine (SVM) and Multi-Layer Perceptron (MLPC). Issues are faced when there is a requirement for high-speed control in the case of fixed-wing unmanned aerial vehicles where such methods are rendered unreliable due to the low speed of classification. Such an application requires the system to classify data at high speeds in order to retain the con- trollability of the vehicle. This paper proposes a novel method of classification which uses a combination of Common Spatial Paradigm and Linear Discriminant Analysis that provides an improved classification accuracy in real time. A non-linear SVM based classification technique has also been discussed. Further, this paper discusses the implementation of the proposed method on a fixed-wing and VTOL unmanned aerial vehicles.

READ FULL TEXT
research
09/28/2022

Practical Challenges in Landing a UAV on a Dynamic Target

Unmanned Aerial Vehicles grow more popular by the day and applications f...
research
05/19/2023

A Secure and Robust Approach for Distance-Based Mutual Positioning of Unmanned Aerial Vehicles

Unmanned aerial vehicle (UAV) is becoming increasingly important in mode...
research
09/21/2020

Reinforced Edge Selection using Deep Learning for Robust Surveillance in Unmanned Aerial Vehicles

In this paper, we propose a novel deep Q-network (DQN)-based edge select...
research
11/12/2019

Smoke Sky – Exploring New Frontiers of Unmanned Aerial Systems for Wildland Fire Science and Applications

Wildfire has had increasing impacts on society as the climate changes an...
research
03/03/2019

Detecting Invasive Insects with Unmanned Aerial Vehicles

A key aspect to controlling and reducing the effects invasive insect spe...
research
09/27/2018

A Way to Facilitate Decision Making in a Mixed Group of Manned and Unmanned Aerial Vehicles

A mixed group of manned and unmanned aerial vehicles is considered as a ...
research
11/14/2021

Methods for Combining and Representing Non-Contextual Autonomy Scores for Unmanned Aerial Systems

Measuring an overall autonomy score for a robotic system requires the co...

Please sign up or login with your details

Forgot password? Click here to reset