-
Notifications
You must be signed in to change notification settings - Fork 10
Challenge 1: Obstacle avoidance using the sonars sensors
Manos Tsardoulias edited this page Nov 17, 2015
·
1 revision
Go to your repository, return in master
branch and create a new one:
git checkout master
cd ~/catkin_ws/src/autonomous_systems_architectures/
git checkout -b challenge_1
Go to the autonomous_exploration/config/autonomous_explo.yaml
and change these:
calculate_target: False
velocities_architecture: 'motor_schema'
The task is to fill the function produceSpeedsSonars
located here. Based on the positions of the sonars and their measurements, produce a linear and an angular speecd in order for the robot to perform obstacle avoidance.
Hint: Try not to use mathematics instead of many if
clauses, if you want the robot to move smoothly.
Finally, push you code:
cd ~/catkin_ws/src/autonomous_systems_architectures/
git add -A .
git commit -m 'whatever message you want'
git push origin challenge_1
(The above must be performed for all challenges in the corresponding branches)
15 out of 100
- Installation / setup
- Modules description
- How to execute the code
- Challenge 1 [15 pts]: Obstacle avoidance using the sonars sensors
- Challenge 2 [15 pts]: Obstacle avoidance using the laser sensor
- Challenge 3 [10 pts]: Obstacle avoidance using sonar and laser sensors via a subsumption architecture
- Challenge 4 [10 pts]: Obstacle avoidance using sonar and laser sensors via a motor schema architecture
- Challenge 5 [5 pts]: Publish the robot path
- Challenge 6 [10 pts]: Update the coverage field
- Challenge 7 [15 pts]: Calculate velocities towards path traversing
- Challenge 8 [10 pts]: Calculate velocities towards path traversing, including the sonars and the laser measurements via a subsumption architecture
- Challenge 9 [10 pts]: Calculate velocities towards path traversing, including the sonars and the laser measurements via a motor schema architecture
- Challenge 10 [5 pts]: Improve the subgoal visitation check
- Challenge 11 [10 pts]: Create a smart target selection technique
- Challenge 12 [10 pts]: Improve the A* algorithm's efficiency
- Challenge 13 [up to 25 pts]: Surprise us!