We are a student design team at the University of Michigan. Our goal is to design an autonomous underwater robot capable of navigating through an obstacle course and complete various tasks while submerged and with no human input. We aim to compete in the RoboSub competition: https://robonation.org/programs/robosub/

Image: Our testing platform submarine.

Given camera input, guides the robot to the center of the gate by calculating the target heading using computer vision techniques.

Image: Calculated headings (blue arrow) from camera center (red dot) to target using contour detection to find orange gate poles. Bounding boxes are shown in green with each center marked by a green dot.

To run: python robosub.py

To use real-time camera input instead of file input, you would have to change how robosub.py gate_centering() function gets its data i.e. move it into the while loop in video_to_photo.

Make sure to suppress debugging image displays (cv2.imshow() ) and prints as necessary in robosub.py Currently, the navigation system works best when entirety of gate is seen.

Future direction: object tracking with and detection using deep learning. Probably more accurate and faster once gate bars are initially detected

pre-processing: sharpen image, remove noise, threshold filter orange colors

contour boxing: calculate the contours of the orange bars, then create a box representation of each bar

false postive removal: assume lowest 3 boxes are valid bars (above ones are false positives/ surface reflections)

target calculation: based on box boundaries (corners), calculate the target point (center of right half)

cam_test.py: tests if camera device is working in opencv.

mask_tuner.py: interactive tool to tune color mask thresholds.

Underwater color correction on test footage. After enhancement the gate outlines are much clearer.

Earlier method of gate detection using canny edge detection. During testing we discovered that edge detection isn't as robust as contour detection.