This week, I finished setting up the Raspberry Pi by uploading all the code to the microcontroller and started integrating the subsystems together. I spent some time refining the obstacle detection algorithm to ensure that it would turn in the correct direction if there was an obstacle on the left or right. We decided to only focus on obstacles in front of the cart, so I made sure to filter out any obstacles that are detected behind the LiDAR. We also started integrating all the components together. We hooked up the Arduino and the Raspberry Pi together to send the commands based on the obstacle detection algorithm, however we are running into issues with the connection, which we plan to smooth out next week.
I am still behind based on the Gantt Chart, but will plan to spend as much time as possible to finish up integrating all the parts. Next week, I plan to have the LiDAR fully integrated with the motors and be able to have very basic obstacle detection working.
Throughout this project, I’ve learned a lot about LiDAR, obstacle detection, and pathfinding algorithms. This was my first time using pathfinding algorithms, and I had the chance to learn the differences between global navigation and local navigation, and exploring different options like A Star and D Star, while evaluating the tradeoffs on what would work best for our project. To learn this information, I read a lot of articles on pathfinding algorithms and watched videos of people creating similar autonomous following projects to get an idea of what technologies were available for us to use.