Embedded Motion Control 2013 Group 8

Group members

Name:	Student id:	Email:
Robert Berkvens	s106255	r.j.m.berkvens@student.tue.nl
Jorie Teunissen	s102861	j.a.m.teunissen@student.tue.nl
Martin Tetteroo	s081356	m.tetteroo@student.tue.nl
Rob Verhaart	s080654	r.a.verhaart@student.tue.nl

Tutor

Name:	Email:
Rob Janssen	r.j.m.janssen@.tue.nl

Minutes

• Meeting 1 - 06-09-2013
• Meeting 2 - 13-09-2013

Planning

Week 1 (02-09-2013 - 08-09-2013)

• Installing software
• Following tutorials
• Setup SVN

Week 2 (09-09-2013 - 15-09-2013)

• Installing Jazz simulator
• Brainstorm on functions
• Setting up plan of approach

Martin: Measure middle road driving

Week 4 (23-09-2013 - 27-09-2013)

• September 25th Corridor Challenge

Week 8 (21-10-2013 - 25-10-2013)

• October 23th Final Challenge

Strategy

Modules:

• Corridor detection
• Relative location in corridor
• map
• route calculation
• Speed/angle regulator
• safety node
• arrow node

Corridor detection

This function detects all the walls. The walls are outputted as line segments in x,y coordinates.

Input: Laser_vector[1081] output: Vector[Lines], with lines[float x_begin; float y_begin; float x_end; float y_end]

Relative location

This function calculates the distance from the wall on the left and on the right and the angle of the robot towards these walls.

Input: Laser_vector[1081], Vector[Lines] Output: Location_rel[ float Dist_left; float dist_right, int Theta]

Map

Using the results of the previous modules this module will update the current map. The location of the robot and the walls/corners/deadends will be drawn in the map. The starting point of the map (0,0) is the start point of the robot. So every time the map from this point will be calculated. As an improvement a particle filter might be needed to draw a better map.

Algorithm

Gapdetection

Input: Laser_vector[1081] output: gap location [float x1; float x2; float xm; float y1; float y2; float ym]

A gap is detected by thresholding the derivative of the laser ranges, this derivative is defined as the difference of laser range [i] and [i-1]. If this difference is larger than the threshold, laser range [i] and [i-1] are labeled as gap corners. The function then starts calculating the position of these laser reflection points relative to its own position. The corner closest to the robot is labeled as point1 [x1,y1] and the other as point2 [x2,y2], the function also outputs the euclidean middle of both points [xm,ym].

Experimentation results

Corridor detection

We failed to succeed the corridor comptetition. We were given two opportunities to detect a hole and drive though it. However, the first attempt the safety overruled the state machine that was build, and the robot got stuck. the second attempt the robot didn't turn far enough and Pico was unable to correct the angle correctly, after which it again got stuck in the safety mode.

First Attempt

When the robot was started, the MiddleDrive function worked properly and Pico started of nicely. Whenever the robot drove towards a wall, the MiddleDrive corrected for it and made sure that the robot drives straight forward. However, when the robot approached the corner whithin 0.5 meter range, it seemed like the robot corrected to drive exactly to the middle of the hole. Since your workspace is rather small and SafeDrive defines a space of 40 cm around every angle of the robot, the robot approached the was too close and the SafeDrve overruled the normal behavior of the robot. So the robot tried to correct for the hole, but instead drove too close too the wall and got stuck in the SafeDrive mode.

Second Attempt

The second attempt the robot started of nicely again, driving straigt forward. When the robot approached the hole on the left within the range of 0.5 meters, it slowed down. Pico corrected a bit to the right, but stopped nicely in the middle of the hole. However, when the turn was started, it seemed to approach a wall again, such that the SafeDrive took over again. We confirmed that the turn was never completed and the robot got stuck agan in its safety mode.

Conclusion

Unfortunately, we didn't succeed the corridor competition, but we learned from our mistakes and know how to approach the problem correctly. Next time we'll have to make sure to reserve more time with the robot to learn its behavior in practical situations. All in all, we have to make sure that the safety doesn't overrule the normal behavior of the robot too soon.