Mobile Robot Control 2023 Group 7: Difference between revisions

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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
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
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==Assignment 1: A-star (global path planning)==
==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.  
[[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
 
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The output of the simulation can be found here: Navigation[A_star].mp4


==Assignment 2: Local path planning==
==Assignment 2: Local path planning==
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== Assignment 3: Localization (Dead - reckoning) ==
==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.
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Latest revision as of 15:05, 30 May 2023

Group members:

Group-7 (BB8)
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.

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


Assignment 1: A-star (global path planning)

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




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.