Mobile Robot Control 2020 Group 3: Difference between revisions
TUe\s137911 (talk | contribs) |
TUe\s137911 (talk | contribs) |
||
Line 38: | Line 38: | ||
** The User-interaction should be minimal and User-friendly. | ** The User-interaction should be minimal and User-friendly. | ||
==Functions== | ===Functions=== | ||
'''Input data processing''' | '''Input data processing''' | ||
* Laser range finder interpretation | * Laser range finder interpretation |
Revision as of 13:00, 26 May 2020
Team members
M.N. de Boer (Martijn)
G.J.L. Creugers (Gijs)
P. Leonavicius (Pijus)
A.L. Nooren (Anna Lisa)
M.K. Salahuddin (Mohamed Kaleemuddin)
S. Narla (Shashank)
Design Document
Requirements
The following requirements regarding task performance, safety and sofware should be satisfied by the simulated PICO robot:
- Task performance
- The robot must be able to recognize target cabinets
- The robot is capable of planning a path and is able to adapt to unexpected circumstances, for instance a closed door.
- PICO can rotate in place, in order to re-position when in front of a cabinet
- Must be able to announce the completion of the current objective
- The robot should not be inactive for more than 25 seconds.
- The robot has to be able to detect static and dynamic object and present them in the world model.
- Safety
- The robot avoids bumping into walls and doors
- The robot avoids collisions with static and dynamic obstacles
- PICO must obey the limits on translation and rotation velocity
- PICO should maintain a 5cm Stopping distance from the obstacle.
- Software
- The software is started by a single executable
- The software can be easily updated .
- The User-interaction should be minimal and User-friendly.
Functions
Input data processing
- Laser range finder interpretation
Inputs: distance and angle from LaserData Interpreting data generated by the laser range finder, using a 2D SLAM algorithm.
- Odometer interpretation
Inputs: OdometryData Calculates speed of the mobile robot integrating position values, relays the data to the SLAM algorithm.
- Sensor Fusion
Combining sensory information from multiple sensors can have uncertainty.This module can help to have reliable information flow to correlate and deconstruct data.
- Vector map data interpretation
A function used for structuring data obtained from the provided map of the testing area. To be used as inputs for position estimation and path planning functions.