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Obstacle Avoidance using Machine Vision

Obstacle Avoidance using Machine Vision. Joose Rautemaa 09455759. Introduction . A control system for a car that can avoid obstacles using Machine Vision D eveloped on and for a model car, could possibly be used in a real car too with minor modifications

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Obstacle Avoidance using Machine Vision

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  1. Obstacle Avoidance using Machine Vision Joose Rautemaa 09455759

  2. Introduction • A control system for a car that can avoid obstacles using Machine Vision • Developed on and for a model car, could possibly be used in a real car too with minor modifications • Hardware includes a Raspberry Pi and a Raspberry Pi Camera • Remote control capabilities over WLAN • Autonomous navigation will be based on a digital compass and GPS waypoints

  3. Aims • Relatively safe and fast autonomous travel • Recognizing obstacles with the camera allows avoiding them or going around them if possible • Using OpenCV for the recognition of objects • Ability to easily select the destination and also manually remotely control the car • A simple web interface that can be used with a laptop or a mobile phone over WLAN where a user can enter coordinates, select coordinates from a map, or manually control the car • The interface has an option for a direct camera feed

  4. Results • The Raspberry Pi is a very small and inexpensive device, but it is not hugely powerful. For faster object detection you would need a more powerful computer • The Raspberry Pi is very energy efficient which allows for much longer operating times if using electric power only • OpenCV is really reliable and efficient in recognizing objects in video feeds, however it requires a lot of processing power • GPS navigation might have to be on a separate device, for example on an Arduino that is attached via a serial link, because the Rasbperry Pi might not have enough prosessing power to handle that too.

  5. References • OpenCV documentation • Adafruit Learning Community • Various blogs and websites • Various forums and IRC channels • Peers and professionals

  6. What next? • Need to find a good model car that can be used as a base for actual testing of the control features • Need to start designing the GPS navigation system and the web interface • The latest version of OpenCV needs to be compiled for Raspberry Pi, this process might take up to 10 hours

  7. Pictures

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