Mobile Robot Control 2020 Group 6

Group Members

Students (name, id nr):
Joep Selten, 0988169
Emre Deniz, 0967631
Aris van Ieperen, 0898423
Stan van Boheemen, 0958907
Bram Schroeders, 1389378
Pim Scheers, 0906764

Logs

This section will contain information regarding the group meetings

List of Meetings

	Date/Time	Roles	Summary	Downloads
Meeting 1	Wednesday 29 April, 13:30	Chairman: Aris Minute-taker: Emre	Introductionary meeting, where we properly introduced ourselves. Discussed in general what is expected in the Design Document. Brainstormed of solutions for the Escape Room challenge. Set up division of tasks (Software Exploration/Design Document).	Minutes
Meeting 2	Wednesday 6 May, 11:30	Chairman: Emre Minute-taker: Stan	Discussing our V1 of the Design Document with Wouter. Devised a plan of attack of the escape room competition and roughly divided the workload into two parts (Perception + world model and Strategy + Control).	Minutes
Meeting 3	Monday 11 May, 11:00	Chairman: Stan Minute-taker: Joep	Discussing what needs to be finished for the Escape Room challenge.	Minutes
Meeting 4	Friday 15 May, 9:00	Chairman: Joep Minute-taker: Bram	Evaluating the escaperoom challenge and the groupwork so far. Made agreements to improve the further workflow of the project.	Minutes
Meeting 5	Wednesday 20 May, 11:00	Chairman: Bram Minute-taker: Pim	Discussing towards an approach for the hospital challenge. First FSM is introduced and localization/visualization ideas are discussed.	Minutes
Meeting 6	(Date), Time	Chairman: Pim Minute-taker: Aris	(Summary goes here)	(Downloadable minutes goes here)

Design Document

The design document, which describes the design requirements, specification, components, functions and interfaces can be seen here.

Escape Room Challenge

The escape room challenge required the PICO robot to escape a room with limited prior knowledge of the environment. The information architecture of the embedded software has been designed in the design document, the main components being: Perception, Monitor & Strategy, Control and the World Model.

Perception

The objective of the escape room challenge is finding and driving out of the exit. To be able to achieve this, the robot should recognize the exit and find its location, which is the main objective concerning perception. For this challenge, the features of the room are only stored locally. The robot tries to recognize the exit, where after the location w.r.t. the robot is determined. First of all, unusable data points of the LRF sensor have been filtered out. A line detection and an edge detection functionality has been implemented in order detect the walls of the room in local coordinates. This way, at each time step, the begin point, the end point, and the nearest point of the walls can be observed by PICO. The functions work in the following manner:

• Line detection: the LRF data consist of 1000 points with each a range value, which is the absolute distance to PICO. The line detection function loops over the data and calculates the absolute distance between two neighboring data points. When the distance exceeds the value d_gap, the line-segment can be separated.
• Edge detection: the line detection algorithm only detects if data points have a large distance relative to each other. The edge detection function detects if the line-segments (which result from the line detection) contain edges. The basic idea of the implemented algorithm can be seen in the figure below. The line segment in that figure has a starting data point A and an end point B. A virtual line, AB, is the drawn from point A to B. Finally the distance from the data points C_i, which lie inside the segment, to the virtual line AB is calculated, d_edge. The largest value d_edge can be considered an edge.

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  Line detection algorithm of the PICO robot.

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  Edge detection algorithm of the PICO robot.

With the ability to observe and locate walls, gaps can be easily detected. basic idea of this gap detection algorithm is that the robot looks for large distances between subsequent lines. The threshold for this difference can be tuned in order to set the minimum gap size. The world model not only stores the local coordinates of gaps, but it also stores the exit walls. The function gapDetect in the class Perception is responsible for storing both the gaps and exit walls in the world model. The visualization beneath shows the localization of a gap in a room. The bright red circle represents the set-point to which PICO will drive towards. This set-point contains a small adjustable margin which prevents collisions with nearby walls.

Some additional features were added, which adds robustness to the line/edge and the gap detection:

• Adjustable parameter MIN_LINE_LENGTH which sets the minimum amount of data points for which we can define a line. With this implementation stray data points will not be perceived as lines.
• Adjustable parameter MIN_GAP_SIZE, which sets the minimum gap size. When the gap size between two lines is lower than this value, everything inside that gap is ignored.
• Adjustable parameter GAP_MARGIN, which as previously mentioned adds a margin to the gap set-point.

With these features, a rather robust perception component has been developed. The resulting performance can be seen in the recording below. The detected lines and gap have been visualized. Small gaps and lines which are present in this custom map are ignored.

Monitor and strategy

Control

Validation

Challenge

On May 13th the escape room challenge was held, where the task was to exit a simple rectangular room through its one exit without any prior information about the room. We had prepared two branches of code, to allow ourselves to have a backup. With the software described in the previous sections, the first attempt showed behavior which was very close to the video below. Unfortunately, when the robot turned its back towards the wall it should be following, it got stuck in a loop which it could not escape. From the terminal we could read that the robot remained in a single state, called FollowWall. However, its reference direction did constantly change.

The code for the second attempt, which omitted the use of the states GoToGap and GoToFinish, made use of two states only, being FindWall and FollowWall. This meant that the issue we faced in the first attempt was still present in the new code, hence exactly the same behavior was observed.

During the interview, it was proposed by our representative that the issue was a result of the robot turning its back to the wall, meaning that the wall behind it is not entirely visible. In fact, because the robot can not see directly behind, the wall seems to be made out of two parts. During turning, the part of the wall which is closest to the robot is used in the FollowWall function changes, hence the reference point changes position. Then, with the new reference point the robot turns again, making the other section of the wall closest, causing the robot to turn back and enter a loop.

During testing with the room that was provided after the competition, a different root to our problems was concluded. As it turned out, the wall to the rear left of the robot almost vanishes when the robot is turning clockwise and its back is facing the wall, as can be seen in the left video above. This means that this wall no longer qualifies as a wall in the perception algorithm, hence it is not considered as a reference wall anymore. This means that the robot considers the wall to its left as its reference, meaning that it should turn counterclockwise again to start moving parallel to that. At that point, the wall below it passes over the threshold again, triggering once again clockwise movement towards the exit.

With this new observation about the reason the robot got stuck, which could essentially be reduced to the fact that the wall to be followed passed under the threshold, the first debugging step would be to lower this threshold. Reducing it from 50 to 20 points, allowed the robot to turn clockwise far enough, so that the portion of the wall towards the exit came closest and hence could be followed. This meant that the robot was able to drive towards the exit, and out of the escape room without any other issues, as can be seen in the video below. All in all, it turned out that the validation we had performed before the actual challenge missed this specific situation where the robot was in a corner and had to move more than 90 degrees towards the exit. As a result, we did not tune the threshold on the minimum amount of points in a wall well enough, which was actually the only change required to have the robot finish the escaperoom.