Embedded Motion Control 2014 Group 3
Group Members
| Name: | Student id: |
| Jan Romme | 0755197 |
| Freek Ramp | 0663262 |
| Anne Krus | 0734280 |
| Kushagra | 0873174 |
| Roel Smallegoor | 0753385 |
| Janno Lunenburg - Tutor | - |
Time survey
Link: time survey group 3
Planning
Week 1 (28/4 - 4/5)
Finish the tutorials
Week 2 (5/5 - 11/5)
Be able to detect walls and convert them to start and end points
Week 3 (12/5 - 18/5)
Finish strategy to be able to successfully finish the competition
Software
Overview
In the overview the different packages (dotted boxes) and nodes (solid boxes) are displayed. The topics are displayed at the sides of the nodes.
Strategy
State 1 - Driving straight
If PICO is not able to find a turning point but it can find two walls beside him he will drive strait through the corridor based on wall data.
State 2 - Turning
If PICO find a turning point close by he will start turning using the coordinates of the turning point.
State 3 - Outside of maze
If PICO can not find any walls beside him and no turning point is detected it will assume it is outside of the maze and will drive strait for a maximum of 3 seconds.
LaserProcessing
LaserData from Pico
The data from the laser on pico is in lasercloud format. This means that the data is represented in an array of distances. The starting angle and angle increment are known. This means we have the distances from laser to objects for a range of angles.
LaserCloud to Pointcloud
Because we are going to fit lines through the walls, it would be easier to have tha data in Carthesian Coordinates. In this node the laserData is transformed into a PointCloud, which is published on the topic. It is also possible to filter the data in this node when needed. For now all data is transformed into the PointCloud.
Safety
The safety node is created for testing. When something goes wrong and Pico is about to hit the wall the safety node will publish a Bool to tell the strategy it is not safe anymore. When the code is working well safety shouldn't be needed anymore.
Obstacle Detection
Finding Walls from PointCloud data
The node findWalls reads topic "/cloud" which contains laserdata in x-y coordinates relative to the robot. The node findWalls returns a list containing(xstart,ystart) and (xend, yend) of each found wall (relative to the robot). The following algorithm is made:
- Create a cv::Mat object and draw cv::circle on the cv::Mat structure corresponding to the x and y coordinates of the laserspots.
- Apply Probalistic Hough Line Transform cv::HoughLinesP
- Store found lines in list and publish this on topic "/walls"
A visualization of the output (left: laserdata from the real Pico right: detected lines 'walls'):
Select Walls
In FindWalls lines are fitted over all the walls. In selectwalls the walls are filtered to find the two walls in the driving direction. The walls are send as a starting and endpoint. To be able to compare the walls to eachother, the begin point is projected on x=0 (at height of Pico). The closest walls left and right of Pico with the same direction are the two walls to use for navigation.
Next part should be in strategy i think
From the grade of the walls compared to Pico a setpoint can be set on a certain distance in x direction, on which pico can correct to drive straight. A setpoint further away leads to smaller corrections compared to a setpoint closer to pico. The setpoint can also be uses for taking the turns.
Notes (TODO)
Week3
Combine detection and strategy part
+ Determine waypoint (turnpoint)
- Find door (using end of line)
+ Turn
+ Drive out of maze