Echolocation

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Inspiration:

The team was inspired by bats who use echolocation to "see" the world. Bats are blind so they have to rely on sound to map the world around them to navigate the world, we are trying to use technology to enable blind people to do a similar thing.

What it does:

Echolocation uses distance sensors and spatial audio to map the space it is used in. The proximity of the user to an object in the space they are in determines the volume of that location using spatial audio. The closer the user is to an object, the higher the volume of that location using spatial audio.

How we built it:

Echolocation was built using Python, Arduino, C++, and MATLAB. MATLAB was used to transform the distance received from the distance sensors and interpret them into spatial audio. Arduino was used to convert the analog signals to digital signals for the MATLAB code. C++ was used to program and interpret the input from the distance receivers in the Arduino

Challenges we ran into: Some of the challenges we ran into are:

  • Figuring out how to transform the audio played into spatial audio
  • Transforming the distance into the signals for the audio
  • Transforming the data received from the distance sensors into the appropriate signal to be sent to the - MATLAB code
  • Having a compact design
  • Adapting to the range of distance sensed by the distance sensors

Accomplishments that we're proud of:

  • Transforming the audio into spatial audio
  • Mapping sound to originate from any point in 3d space
  • Getting the project to work to some degree
  • Creating a hardware-software pipeline including Arduino, MATLAB, and Python

What we learned:

  • How to work in a team
  • How to utilize the strengths of each team member
  • How to implement real-time audio signal processing

What's next for Echolocation:

The sensors used for echolocation are very limited and do not map a 3d space, to improve on this technology, a more detailed sensor system utilizing a point cloud environment would need to be used and the program would be modified to give more accurate eyes. Furthermore, we could implement AI and machine learning to train models to further support not only the surrounding but also objects.

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