Mobile Robot Control 2020 Group 2
Mobile Robot Control 2020 Group 2
Group members
Marzhan Baubekova - 1426311
Spyros Chatzizacharias - 1467751
Arjun Menaria - 1419684
Joey Verdonschot - 0893516
Bjorn Walk - 0964797
Bart Wingelaar - 0948655
Introduction
This is the wiki page of the group 2 of the mobile robot control 2020 course. The page contains all the information regarding the assignments accomplished within the course including the plan, procedure, algorithms, implementation and results. The aim of the
Design Document
Design document for the escape room challenge can be found here:
File:Mobile Robot Control Design Document Group2.pdf
Escape Room Challenge
The goal of the escape room challenge is to find the exit of a room autonomously and 'escape' the room through the exit corridor without bumping into the walls. In this challenge the robot is placed in a rectangular room, which dimensions as well as the initial position of the robot in the room are not known. In order for the robot to succeed in this challenge, a design document was created first, which includes the requirments & specifications, components, functions and interfaces.
As stated in the design document, the software of the robot is developed based on a finite state machine. The finite state machine will start in a predefined initial state and will go to next states if certain conditions are met. The finite state machine will follow the states until the final state is 'finished'. While going through these states, the robot communicates with the world model. The main functions of the world model are to store important information and to share it between all classes.The information stored in the world model is for example the direction of the exit when identified or the distance to a wall. The other classes determine the actual actions of the robot. The SafetyMonitor will retrieve for example the distance to a wall and determine if the distance is larger than a certain threshold. If the wall is too close, the SafetyMonitor will tell the robot to move away from the wall to get some clearance. The detection class retrieves the laser data and determines the distances to walls or finds the exit. These parameters are then stored in the world model. Drive control determines the movement of the robot. Path planning is not used in the escape room but will be used for the hospital challenge.
At the start of the escape room challenge the robot will start to scan the room. This scan is then analyzed to determine if the exit can be seen. If the exit is detected, the robot will move in the direction of that exit until it gets close to the exit. If the exit can not be seen, the robot will rotate approximately 180 degrees to do a second scan. If the exit is detected now, the robot will move as it would if during the first scan a exit was detected. If in the second scan the exit can still not be identified, the robot will move to the closest wall found in both scans and start to follow this wall using a wall following algorithm.
When the exit is found from one of the initial scans, the robot will rotate to face the correct direction towards the exit. It will then start to move forward towards the exit. When it gets close to a wall the robot assumes it is near the exit and starts the exit alignment procedure. This exit alignment procedure makes sure that the robot will enter the hallway approximately in the middle to stay clear of the walls. When the alignment is done the robot will start the exit entering procedure.
The wall following algorithm makes the robot move in a clockwise direction. When it gets close to a corner it will rotate approximately 90 degrees and use the wall to the left to correct and align to an almost perfect 90 degrees. When it suddenly sees a jump in the distance of the wall to the left, it detects this as the exit. The robot scans a larger part of the wall while driving to identify this gap. This also means that if it is only a small slit in the wall, it will not detect this slit as a exit. When the robot has then moved forward so that it is standing in front of the gap, it will rotate to face the gap. It will then align so that it is standing approximately in the middle of the gap and start the exit entering procedure.
The exit entering procedure makes sure the robot slowly drives forward until it is somewhat into the hallway. While it is doing this, it is scanning for walls to the left and right of the robot to make sure it will not hit any walls. If the robot gets to far out of the middle line of the hallway, it will reposition itself towards the middle. While in the hallway the procedure is quite similar with respect to aligning to the middle line of the hallway. In the hallway the robot will also align to be parallel to the left wall of the hallway. This is done to make sure the robot can not get to close to this wall. Combined with staying in the middle line of the hallway, this ensures that the robot will not hit any of the walls in the hallway.
While in the hallway the robot will also scan ahead slightly to the left and right. This is done to determine when the robot has crossed the finish line. When the robot no longer sees walls in the front left and right direction, it will assume that it has crossed the finish line.
A flow chart of finite state machine is shown below:
In the live Escape Room challenge the robot did not finish. In the first attempt this was due to the fact that the robot started close t0 the wall. This scenario was unfortunately not tested. The robot starts by scanning the room and trying to find the exit. When an exit is found the robot moves towards it. However, the criteria for the robot to determine that it was close to the exit was if a wall is nearby. This criteria works if the robot starts cleared of the walls and can drive straight to the exit. Since the robot started close to a wall in the challenge, it immediately thought it was at the exit and tried to allign with the hallway. This obviously did not work since there was no hallway.
The second attempt was a simple wall following algorithm. However, again a specific case was not tested making the robot still not finish the challenge. In this case it was the situation where the exit was very close to a corner. When the robot gets to a corner it is supposed to rotate approximately 90 degrees, and then use the wall to the left to align with the wall. Now that the exit was close to the corner, it could not detect the wall to align with, and the software got stuck in a loop.
The first code was fixed by changing the criteria for when the robot assumes it is close to the exit. Now the distance to the exit is measured at the 20Hz sampling rate. This distance was already calculated in the original code, however it was not stored and used. Now that it is used, the robot will only start the alignment procedure for the hallway when this distance is below a threshold. The robot will also move away from the wall slightly after the scanning to ensure safety margins. This was already in the code, but was not executed since the robot never started moving but immediately thought it was at the exit. The result is now that the robot escapes the room in 19 seconds and stops right after crossing the finish line and speaks "I have finished". The results is shown below:
Hospital Challenge
Path Planning
The algorithm that is chosen for the path planning is an A* algorithm. Before proceeding to algorithm, it is essential to transform map to a binary grid map. This step is done in Matlab and later translated to C++. There are two heuristic functions which are tested, namely diagonal and euclidean. Pathplanning with 2 heuristics.
For the selection of the algorithm used for pathplanning a table of pros and cons has been formulated, which includes the computer performance index. A* is given an index number of 100. The walls and Dijkstra compute indices are an educated guess. The others are based on the amount of nodes used in a study
| Algorithm | Compute index |
|---|---|
| A* | 100 |
| Rapidly-exploring random tree | 100 |
| Potential field algorithm | 40 |
| Dijkstra | 100 or higher |
| Wall following | 100000 |
| Wave propagation | 100 or higher |
In addition, several algorithms are compared with respect to the convergence speed, the robustness of the method, the smoothness of the resulted path, the difficulty of the implementation and the sufficiency for the hospital challenge. The results are demonstrated in the table .........
Localization
For the successful navigation it is important that the robot is aware of its current location. After considering known algorithms such as Markov localization, Kalman filter and partilce filters, it has been chosen to implement the line segment based localization. (Why?? restricted in time, experience, identification of landmarkes is problematic and etc.)
First, the algorithm requires features extraction and the most important features in the robot's environment are the straight lines and corners. These features are used to build a local map from laser data. To extract corners the following problem should be solved. It consists of two subproblems, namely segmentation, which deals with the number of lines and correspondence of data points to lines, and corner definition, which answers how to estimate corners from segmented lines. To solve the segmentation, the split and merge algorithm is used, which is a recursive procedure of fitting and splitting. Then the corners are defined at the location where splits happens in the split and merge algorithm.
| Split and Merge Algorithm: |
|---|
| Initialize segments set S. |
| 1. Filter LRF outliers by setting thresholds MAX_RANGE_LRF and MIN_RANGE_LRF. |
| 2. Convert LRF data to cartesian coordinates. |
| 3. Calculate the distance between two cartesian points and check if it is within threshold DUMP_DIST. |
| 4. Check for a jump in LRF ranges of the filtered data set and if the jump is bigger than threshold CLUSTER_MARGIN-> make segmentation |
| Split |
| 1. Fit a line to begin and end point in current set S. |
| 2. Find the most distant point to the line using heron's formula. |
| 3. If distance > threshold LSE_MARGIN, then split and repeat with left and right point sets. |
| Merge |
| 1. If two consecutive segments are close/collinear enough, obtain the common line and find the most distant point. |
| 2. If distance <= threshold, merge both segments. |
In our case the base b is ch and h is a distance-to-be-found dk. Area A is calculated by Heron's formula since all three distances ah, bh and ch can be found. To calculate area, define first
then
Finally, the distance can be calculated:
Visualization
Logs
| Meeting # | Date and Location | Agenda | Meeting notes |
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| 1 |
Date: 28-04-20 Time: 10:00 Platform: MS Teams |
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| 2 |
Date: 01-05-20 Time: 14:00 Platform: MS Teams |
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| 3 |
Date: 05-05-20 Time: 10:00 Platform: MS Teams |
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Work division
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| 4 |
Date: 11-05-20 Time: 11:00 Platform: MS Teams |
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Work division
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| 5 |
Date: 18-05-20 Time: 11:00 Platform: MS Teams |
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Work division
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| 6 |
Date: 22-05-20 Time: 14:00 Platform: MS Teams |
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Work division is the same
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| 7 |
Date: 26-05-20 Time: 11:30 Platform: MS Teams |
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Work division is the same
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| 8 | 5 | 10 | 15 |