- Design Changes:
- Decided on a Figure-8 track for our demo
- Simplified path planning – in this case, each set of vehicles are in their own track and only need to worry about starting or stopping
- Added a L298N motor controller board for each vehicle in order to safely power the motors and NodeMCU chip
- Adds a little extra cost to each vehicle but still under budget
- Decided on a Figure-8 track for our demo
- Challenges:
- NodeMCU ESP8266 chip posed major connectivity issues whenever we tried to upload code.
- Everyone helped debug with different software tools, using different cables, different drivers, version of Arduino IDE and of esptool, etc.
- Researched on alternative Wifi modules for project
- Finally, ordered new NodeMCUs from another manufacturer and it worked
- Programming the motors through the NodeMCU and motor controller board
- Early tests have shown an unpredictability in controlling the vehicles. Some behaviors are unexpected or not exactly as programmed.
- Still able to get metrics such as speed and stopping distance though
- ArUco takes longer computation time when the tags are smaller
- We determined the max size of our tags to be 5 in. x 5 in. Therefore, in order to meet the latency requirements of 200ms and to also keep the entire track within the field of view of the camera, the height placement of the camera is extremely important
- A proposed workaround is having the camera placed at an angle instead of directly overhead the track. This would allow the tags to appear larger in the frames
- We would need to perform some image warping, however, to get an accurate estimation at the vehicles’ positions.
- Video frames of moving tags might be much lower resolution than pictures
- ArUco marker detection is performing very well so far in preliminary tests of images of printed tags, but still unclear how its performance may change when using the video camera
- NodeMCU ESP8266 chip posed major connectivity issues whenever we tried to upload code.
- Accomplishments:
- Finalized implementation plan by defining metrics
- Chose track size
- Determined height of the camera above the track
- Collected rough estimates of computation time of object detection algorithms
- Figured out the accuracy of object detection framework for our problem setting
- Finalized implementation plan by defining metrics
Team A1: Cooperative vs Non-cooperative Autonomous Driving
Carnegie Mellon ECE Capstone, Spring 2020: Kylee Santos, Tito Anammah, Serris Lew