The goal of this project is to use neural networks to train the Robotis BioloidGP to detect trash and throw it away in trash cans, effectively keeping the office environment clean.

git clone https://github.com/NVIDIA-Jetson/jetson-trashformers.git
cd jetson-trashformers
make
sh runDetect.sh

These three commands will clone the project to the computer so that it can be run and/or edited to the the user's liking, compile the program, and allow the user to run the program to pick up trash. This program can only be run on the Jetson TX2.

CupNet is the neural network that we have created in order to detect cups. It has been trained on images of cups in multiple colors, as well as false positives to make the model more accurate. This neural network has been created and trained on NVIDIA DIGITS using the Caffe framework. We used the help of Dustin Franklin's Jetson Inference tutorial to learn more about using DIGITS and creating our own neural network.

This graph shows the model's statistics during the training period.

The model learns to draw bounding boxes around cups through training.

• ROBOTIS:

SDK OBTAINED FROM https://github.com/ROBOTIS-GIT/DynamixelSDK on June 29 2017

Copyright (c) 2016, ROBOTIS CO., LTD. All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

• Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

• Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

• Neither the name of ROBOTIS nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

• ZigBee

Source code available at http://support.robotis.com/en/software/zigbee_sdk/zig2serial/linux.htm

No license was found as of June 29 2017.

See 'lib' folder for the specific files.

• libdetectnet-camera.so
  • A shared object library with an edited and compiled version of detectnet-camera.cpp from Dustin's github.
• libdxl_sbc_cpp.so
  • A shared object library for the Dynamixel servos.
• libjetson-inference.so
  • A shared object library of Dusty's jetson-inference.
• libzgb.so
  • A shared object library to control robot commands via ZigBee.

• Ishan Mitra
• Shruthi Jaganathan
• Mark Theis
• Michael Chacko