Mobile Robot Control 2023 Group 7: Difference between revisions
Tag: 2017 source edit |
(→Assignment 1: A-star (global path planning): changed link) |
||
| (18 intermediate revisions by 2 users not shown) | |||
| Line 1: | Line 1: | ||
=== Group members: === | ===Group members:=== | ||
{| class="wikitable" | {| class="wikitable" | ||
|+ | |+Group-7 (BB8) | ||
! Name !! student ID | !Name!!student ID | ||
|- | |- | ||
| | |Ram Balaji Ramachandran||1896067 | ||
|- | |||
|Nisha Joy | |||
|1810502 | |||
|- | |||
|Dharshan Bashkaran Latha | |||
|1868950 | |||
|} | |} | ||
==Logs== | |||
*Output for: io.speak("test") [Pg. 14] | |||
*Odometry data consensus | |||
==Exercise-1: The art of not crashing [dont_crash.cpp]== | |||
<br />A code that keeps the robot moving forward until an object comes in front of it was created and the results were verified on the simulation platform. | |||
The code was then taken to testing on 4th of May 2023, and the testing was performed on the BOBO robot. | |||
There were some hiccups in compiling and running the code. It was all cleared and the test outcome was succcesful! The proximity adopted was 20 cm to have a closer cut-off. | |||
Test: https://tuenl-my.sharepoint.com/:v:/r/personal/d_bashkaran_latha_student_tue_nl/Documents/MRC/Exercise_1_Dont_crash/dont_crash.MOV?csf=1&web=1&e=lCJOCU | |||
<br /> | |||
==Assignment 1: A-star (global path planning)== | |||
[[File:Large maze.png|thumb|Path planning for Larger maze]] | |||
A global path planning algorithm was implemented. This was done by finding the optimal path using the A-star algorithm. And the shortest path is highlighted by blue. | |||
The output simulation for the small maze can be found here: https://tuenl-my.sharepoint.com/:v:/g/personal/d_bashkaran_latha_student_tue_nl/EUGPlltwUoxOoOblomY1t7sB1wIRHPjTd2NmKq-k_iveHQ?e=ZUmlf5 | |||
<br /> | |||
<br /> | |||
==Assignment 2: Local path planning== | |||
A local path planning algorithm was implemented using the open space approach and combination of DWA. However, the DWA was only partially implemented and needs much more improvement. | |||
The tests were simulated first on the simulator and after successful results they were tested on the real robot - coco. | |||
The speed parameters had to be corrected to ensure the safety of the robot. | |||
Axes correction had to be implemented. | |||
Trials for different test cases can be found here: https://tuenl-my.sharepoint.com/:f:/g/personal/d_bashkaran_latha_student_tue_nl/EgAvwqIqPRlHjYv-45-aqrkBj4EwR_3elw1qFfQwavSrtw?e=qaT1Sb | |||
<br /> | |||
==Assignment 3: Localization (Dead - reckoning)== | |||
For this assignment, the robot localization was estimated using the dead-reckoning method, where the position of the robot is computed from the input speed and time it traversed. | |||
<br /> | |||
Latest revision as of 15:05, 30 May 2023
Group members:
| Name | student ID |
|---|---|
| Ram Balaji Ramachandran | 1896067 |
| Nisha Joy | 1810502 |
| Dharshan Bashkaran Latha | 1868950 |
Logs
- Output for: io.speak("test") [Pg. 14]
- Odometry data consensus
Exercise-1: The art of not crashing [dont_crash.cpp]
A code that keeps the robot moving forward until an object comes in front of it was created and the results were verified on the simulation platform.
The code was then taken to testing on 4th of May 2023, and the testing was performed on the BOBO robot.
There were some hiccups in compiling and running the code. It was all cleared and the test outcome was succcesful! The proximity adopted was 20 cm to have a closer cut-off.
Assignment 1: A-star (global path planning)
A global path planning algorithm was implemented. This was done by finding the optimal path using the A-star algorithm. And the shortest path is highlighted by blue.
The output simulation for the small maze can be found here: https://tuenl-my.sharepoint.com/:v:/g/personal/d_bashkaran_latha_student_tue_nl/EUGPlltwUoxOoOblomY1t7sB1wIRHPjTd2NmKq-k_iveHQ?e=ZUmlf5
Assignment 2: Local path planning
A local path planning algorithm was implemented using the open space approach and combination of DWA. However, the DWA was only partially implemented and needs much more improvement.
The tests were simulated first on the simulator and after successful results they were tested on the real robot - coco.
The speed parameters had to be corrected to ensure the safety of the robot.
Axes correction had to be implemented.
Trials for different test cases can be found here: https://tuenl-my.sharepoint.com/:f:/g/personal/d_bashkaran_latha_student_tue_nl/EgAvwqIqPRlHjYv-45-aqrkBj4EwR_3elw1qFfQwavSrtw?e=qaT1Sb
Assignment 3: Localization (Dead - reckoning)
For this assignment, the robot localization was estimated using the dead-reckoning method, where the position of the robot is computed from the input speed and time it traversed.