Quadcopters are interesting pieces of engineering, accessible to the general public since the introduction of radio controlled and small-scale Unmanned Aerial Vehicle (UAV) models. Since then, major developments regarding size, stability, autonomy, control, Artificial Intelligence, hardware & software development tools, etc., have been introduced by academia and enthusiast community. Nevertheless, the vast majority of the application purposes given to these machines still fall into the recreational scope.
Recently, some have tried to exploit more ‘useful’ purposes for these machines, e.g. a brain-controlled quadcopter for the disabled, a quadcopter tower-building task force or surveillance tasks.
The goal of this thesis is to further explore the applicability of quadcopters to Ambient Assisted Living (AAL) scenarios, namely as an alternative to solutions such as Thought Controlled Quadcopter or AAL DOMEO showcased in the latest AAL Forum editions. This MSc thesis intends to continue with the preliminary results achieved by a previous QuadAALper thesis, in which the basics – quadcopter setup (an Arducopter), wireless connectivity, basic interaction with mobile devices, etc. – have been established.
We would be particularly interested in a further integration of the quadcopter with smartphones (or a dedicated system running Android) as an interesting solution for a quadcopter ‘brain boost’ (i.e. using the smartphone on the quadcopter, making it its brain, something like DroneItYourself but with your smartphone as its brain. Some use cases can be explored to determine the feasibility of such solution in AAL scenarios, described below:
> Response to voice commands (using Siri for iOs or Jeanni for Android);
> Detection of alarm situations (burglars, temperature, CO2 - Carbon Dioxide saturation, etc.);
> Activity monitoring, ‘follow-me’ modes for indoor guidance (i.e. the quad should also be capable of indoors autonomous guidance), additional inputs for fall detection solutions, etc.;
> Applications needing user-authentication (taking advantage of facial recognition features);
> Project images and video call on the wall.
Author: Ricardo Nascimento
Type: MSc thesis
Partner: Faculdade de Engenharia da Universidade do Porto