Embedded Motion Control 2014 Group 5: Difference between revisions
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== Planning week 1 == | == Planning week 1 == | ||
Follow all tutorials before tuesday 6-5-2014. | Follow all tutorials before tuesday 6-5-2014. | ||
= Introduction = | |||
The goal of this course is to implement (embedded) software design (with C++ and ROS) to let a humanoid robot navigate autonomously. The humanoid robot Pico is programmed to find its way through a maze, without user intervention. This wiki page contains the approach and choices that were made by group 5. | |||
= Corridor Challenge = | |||
== Laser data == | |||
== Odometry == | |||
Odometry is the use of data of the angular positions of the robot wheels. This data is used to estimate the position of the robot relative to a starting point. The angular positions are converted into Carthesian coordinates (x-, y- and theta-direction). | |||
This data is never fully accurate, inter alia due to wheel slip. | |||
== Driving straight forward == | |||
== Wall avoidance == | |||
= Maze Challenge = | |||
Revision as of 19:21, 12 May 2014
Group 5 is claimed by:
Geert van Kollenburg
Paul Blatter
Kevin van Doremalen
Niek Wolma
Robin Franssen
Planning week 1
Follow all tutorials before tuesday 6-5-2014.
Introduction
The goal of this course is to implement (embedded) software design (with C++ and ROS) to let a humanoid robot navigate autonomously. The humanoid robot Pico is programmed to find its way through a maze, without user intervention. This wiki page contains the approach and choices that were made by group 5.
Corridor Challenge
Laser data
Odometry
Odometry is the use of data of the angular positions of the robot wheels. This data is used to estimate the position of the robot relative to a starting point. The angular positions are converted into Carthesian coordinates (x-, y- and theta-direction). This data is never fully accurate, inter alia due to wheel slip.