- Raymond Constantine - MAE
- Martin Heir - UPS
- Sam Liimatainen - ECE
- Zhenghao “Jack” Weng - MAE
Team 2 Car
Design and built an autonomous human following program using Yolo driven object detection algorithm This algorithm implements a box type target system that tracks human shape objects with in the camera’s field of view We utilized custom ROS nodes to have the incoming data from the camera publishing to a steering controller node, giving commands to the vesc via twist commands
Color Filter Flowchart (OpenCV):
The computer vision script works by converting each frame into HSV space, forming a mask for each target color (red, yellow, and green), and applying the hough circle transform to each masked image. If a circle of the proper size and color range is detected, the script will output the corresponding traffic signal logic to be used by ROS2 for directing the car.
The GIF above is a visualization of the computer vision script detection.
ROS2 Flow Chart:
Above is a flow chart that depicts the structure of the ROS2 Nodes that guide the robot. Here we see that the Lane Detection Node subscribes to the camera feed topic (which contains raw camera frame data) and publishes the centroid locational data to the centroid topic. The Lane Guidance Node was modified by Team 2 to subscribe to both the centroid topic from the Lane Detection Node as well as the camera feed topic; the node will guide the car based on the centroid data relative to the center of the camera frame (allowing the car to follow the lane lines) and in the event that a traffic signal is detected, the commands to obey the traffic signal will override the lane guidance commands. The Lane Guidance node publishes actuator values to the cmd_vel topic, which is interpreted by Adafruit Twist Node that controls the PWM signal sent to the car's throttle and steering.
Repositories ECE/MAE 148 WI22 Team2 GitHub ECE/MAE 148 WI22 Team2 GitLab (ROS2 Integration)