Project for the "Vehicle Mechatronics: Control" course at Aalto University. Our TurtleBot is required to complete a pre-fixed track without colliding with the walls, possibly travelling as fast as possible.

The Gazebo environment is the following:

The package shall be cloned/downloaded in the catkin_ws/src folder, and after that run the following commands from the ~/catkin folder:

$ catkin_make
$ source ~/.bashrc

To launch the world:

$ roslaunch vmc_project project_world.launch

To run the node which starts the TurtleBot and the controller:

$ rosrun vmc_project racer.py

The visualization of the potential field and of the trajectory is done using the Rviz widget.

To run the visualization run rviz and the command

rosrun tf static_transform_publisher 0 0 0 0 0 0 1 map base_scan 10

(can be copied-pasted from this file). Then from rviz open the configuration file racer.rviz.

To avoid the visualization and unnecessary computation, set to false the variable BOOL_PLOT in file racer.py.

Our solution implements a Potential Field path planning, mostly using data coming from LiDAR sensor. The potential field comprehend two different contributions:

• Repulsive from the walls, represented by the points detected by the LiDAR. The function used for each point is:

# distance function
dist_sqr = (x-p[0])**2 + abs(y-p[1])
# potential contribute of the point
pot_field[i,j] += pot_gain / (dist_sqr)

• Attractive from a point positioned at the end of the trajectory computed at previous time step. The function used for the attractive point is:

# distance function
dist_sqr = abs(x-att_p[0]) + (y-att_p[1])**2
# potential contribute of the point
pot_field[i,j] -= attr_gain / np.sqrt(dist_sqr)

With x,y point in which the potential field is computed, pot_gain magnitude constant of the walls repulsive field, attr_gain magnitude constant of the attractive field, p walls points and att_p position of the attraction point.

The resulting potential field is the following:

Using this function we are able to incentivize a forward trajectory and overcome problems related to local minima of the potential (e.g. at the crossroad).

Tuned parameters are the attractive and repulsive gains, the functions used, and the position of the attraction point with respect to the previous trajectory.

Lateral control is performed with the Pure Pursuit algorithm. The algorithm results simplified given that the trajectory is computed in the robot local reference frame. A PID controller is then implemented to adjust the yaw rate.

Tuned parameters are the PID gains and the look-ahead distance.

The lognitudinal control is impemented setting a maximum speed the robot can achieve, then applying a braking factor compsed by three different components:

• Deviation from desired yaw angle, set by the lateral control.
• Closest point distance on lateral sides.
• Closest point distance in front of the robot.

Each contribution has its own gain. Tuned parameters are the braking gains and the max velocity.

A possible upgrade is to choose the look-ahead distance as function of the error in the yaw angle or the distance of the walls.

Moreover, we tried to use two different nodes, the first to control the robot, and the second to compute the trajectory and plot the info on rviz (code can be found in the branch dev ). The second publishes the computed trajectory on a topic the first node is subscribed to. This way the second node can be utilized easily for debugging, being it detached from the controller node. However, the performances of the whole system seem to deteriorate, probably due to the lag in the trajectory communication.