This repository contains our code for the fifth competition of CMPUT 412, Experimental Mobile Robotics. The challenges in this competition are identical to the previous competition, competition 4, our solution to which builds on our solutions to competitions 3 and 2.
As described previously, the competition consists of a course on which the robot must demonstrate various tasks, such as line following, shape detection, and AR tag detection. This competition differs only in the scoring: multiple runs around the track are allowed, and points are cumulative in all runs. The point amounts are also re-balanced to favour box-pushing. For details on the individual tasks, see our previous report.
The project requires ROS Kinetic with the turtlebot, openni, ar_track_alvar, usb_cam, amcl and gmapping packages. Additionally, gTTs is required to generate the sounds.
pip install gTTS
The robot is setup as the following, with an RGB-D camera facing forward and a webcam on the bottom front, looking down:
| Robot setup | RGB-D camera | Bottom camera |
|---|---|---|
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Two blocks of styrofoam are taped to either side of the robot in order to aid box-pushing while not blocking the view of the bottom camera:
| Styrofoam setup (front view) | Styrofoam setup (top view) |
|---|---|
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The code should be procured by cloning this repository. To build the components ROS needs to run the code, use
cd competition5
catkin build
source devel/setup.bash
Then, generate the sound files
cd util/sound
python gen.py
cd -
Finally, obtain fastai_trained.onnx from the releases of this repository and copy it to src/feature_detector/scripts/server.
Plug the Kobuki base, both cameras and a joystick into your laptop. Then run the launch file
roslaunch competition4 competition4.launch
and run the python file src/competition4/scripts/competition4.py.
We re-used the following components from previous competitions:
- Changing state at red lines
- Turning
- Stopping
- Waypoint navigation
- Off-ramp and on-ramp
- Searching for AR cube and target marker
- Shape detection (locations 2 and 3)
While line-following is conceptually similar to previous competitions, we made several small changes to the algorithm:
- The white mask has been updated to be more sensitive than before, to prevent the robot from immediately losing the line after location 1. It also uses the HSL colour space rather than HSV.
- To remove false positives, the line is only considered to be seen if there are more than 100 pixels matching the line colour.
- If the line is lost, the robot considers its location in pixel coordinates to be identical to its last known location.
- The robot does not move at constant speed, but rather slows down as the center of mass of the line pixels moves away from the center of the camera (this was present in earlier competitions, but appears to be unmentioned in their reports).
We now use GMapping for mapping and AMCL for localization. The map is limited to the location 4 area only. We scattered objects around that area randomly when mapping in order to give GMapping more features and improve map quality. We then manually cleaned up the map, including removing these objects.
| Original map | Cleaned-up map |
|---|---|
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We localized the robot within the course using AMCL. We put a waypoint on the stop line just before the off ramp, and the robot reset its localization to that waypoint before exiting via the off ramp.
Our box-pushing strategy is similar to our previous approach, but with some changes to make the process more robust. The algorithm is as follows:
- Locate the box and the target by navigating back and forth from two waypoints on either side of location 4 and spinning at each waypoint. When both the box and target are located in this way, continue on to the next step.
- Find the square closest to the box, and the square closest to the target. Each of these squares' locations are pre-defined on our map.
- Navigate to the square to the left of the box (if the box needs to be pushed to the right) or to the right of the box (otherwise). Navigating to a square means in this case using ROS's navigation stack to move to the outside of the square, then using a simple proportional controller to move to the square's waypoint coordinates according to the robot's odometry. This two-step procedure allows greater accuracy than we were able to obtain from the navigation stack, and also improves robustness (as small localization errors can prevent ROS's navigation stack from navigating the robot close to a wall).
- Turn to face the box (i.e. turn 90 degrees to the left or the right)
- Move forward (correcting with odometry) until the box's projected position (calculated by adding an offset to the robot's center) is at or past the center of the target square.
Note that, unlike our last competition, we do not attempt to recognize a failure to push the box, or to implement recovery behaviour if such a situation occures. The above algorithm proved in testing to be robust enough for our purposes without such techniques.
Although our code has the ability to perform all the tasks in the competition, to earn the maximum amount of points we chose to only attempt box-pushing. A video of a test run of our robot illustrates our technique:
