Mobile Robot Control 2024 Optimus Prime: Difference between revisions

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=== '''<u>CONTENTS</u>''' ===
#Introduction
#Group members
#Exercise 1 - The art of not crashing
#Exercise 2 - Local navigation
#Exercise 3 - Global path planning
#Exercise 4 - Localization


===Group members:===
=== '''<u>INTRODUCTION</u>''' ===
We are Optimus Prime, a team of six members applying various control techniques and coding skills to optimize a robot for restaurant environments. Our goal is to enable the robot to efficiently deliver orders from the kitchen to the tables, even when faced with various obstacles. This project focuses on ensuring precise and reliable performance, ultimately improving service efficiency and the overall dining experience.
 
=== '''<u>GROUP MEMBERS</u>''' ===
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== '''Exercise 1 - The art of not crashing''' ==
This exercise aims to enhance our understanding of control techniques and obstacle avoidance algorithms.
== '''Exercise 2 - Local navigation''' ==
===== Artificial potential fields =====
===== '''Dynamic window approach''' =====

Revision as of 09:37, 20 May 2024

CONTENTS

  1. Introduction
  2. Group members
  3. Exercise 1 - The art of not crashing
  4. Exercise 2 - Local navigation
  5. Exercise 3 - Global path planning
  6. Exercise 4 - Localization

INTRODUCTION

We are Optimus Prime, a team of six members applying various control techniques and coding skills to optimize a robot for restaurant environments. Our goal is to enable the robot to efficiently deliver orders from the kitchen to the tables, even when faced with various obstacles. This project focuses on ensuring precise and reliable performance, ultimately improving service efficiency and the overall dining experience.

GROUP MEMBERS

Caption
Name student ID
Yuvan Dwaraga 1563793
Wiktor Bocian 1628798
Ramakrishnan Rajasekar 1979027
Ariyanayag Ramesh Skandan 2012618
Abhir Adiverekar 1984136
Suryakumar Hariharan 1974076

Exercise 1 - The art of not crashing

This exercise aims to enhance our understanding of control techniques and obstacle avoidance algorithms.

Exercise 2 - Local navigation

Artificial potential fields
Dynamic window approach
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