Mobile Robot Control 2023 Group 5: Difference between revisions
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|Guglielmo Morselli | |Guglielmo Morselli | ||
|1959301 | |1959301 | ||
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== Navigation Assignment 1 == | |||
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== Navigation Assignment 2 == | |||
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* '''Description of the main idea''' | |||
We choose to implement the Artificial Potential Field (APF) algorithm for obstacle avoidance in robot navigation. It uses laser scanner data to detect obstacles, calculates repulsive forces based on obstacle proximity, converts these forces into velocity commands, and sends these commands to the robot, enabling dynamic navigation. | |||
* '''Screen recordings of the simulation results''' | |||
* '''Video of the robot's performance in real life''' | |||
https://tuenl-my.sharepoint.com/:v:/g/personal/r_li1_student_tue_nl/EfMXoIxWLJBPpgl8E2LfRTEB9AcTnkAUy7_B2xHfvAhClA?e=ssxpqS | |||
Revision as of 15:22, 17 May 2023
Group members:
| Name | student ID |
|---|---|
| Yuzhou Nie | 1863428 |
| Ronghui Li | 1707183 |
| Guglielmo Morselli | 1959301 |
- Description of the main idea
We choose to implement the Artificial Potential Field (APF) algorithm for obstacle avoidance in robot navigation. It uses laser scanner data to detect obstacles, calculates repulsive forces based on obstacle proximity, converts these forces into velocity commands, and sends these commands to the robot, enabling dynamic navigation.
- Screen recordings of the simulation results
- Video of the robot's performance in real life