Mobile Robot Control 2024 Optimus Prime: Difference between revisions
No edit summary |
No edit summary |
||
| Line 35: | Line 35: | ||
== '''Exercise 1 - The art of not crashing''' == | == '''Exercise 1 - The art of not crashing''' == | ||
This exercise aims to enhance our understanding of control techniques and obstacle avoidance algorithms. | This exercise aims to enhance our understanding of control techniques and obstacle avoidance algorithms. | ||
=== '''Solutions''' === | |||
=== '''Learnings from each solution''' === | |||
=== '''Visual representation of sim''' === | |||
=== '''Practical video''' === | |||
== '''Exercise 2 - Local navigation''' == | == '''Exercise 2 - Local navigation''' == | ||
=== '''<u>Artificial potential fields</u>''' === | |||
==== Motivation ==== | |||
Our motivation for choosing the artificial potential field algorithm lies in its effectiveness for real-time obstacle avoidance and smooth navigation. This approach enables our robot to dynamically maneuver around obstacles by leveraging attractive and repulsive forces, ensuring efficient path planning. | Our motivation for choosing the artificial potential field algorithm lies in its effectiveness for real-time obstacle avoidance and smooth navigation. This approach enables our robot to dynamically maneuver around obstacles by leveraging attractive and repulsive forces, ensuring efficient path planning. | ||
===== '''Dynamic window approach''' ===== | ==== Solutions ==== | ||
==== '''Learnings from each solution''' ==== | |||
==== '''Visual representation of sim''' ==== | |||
==== '''Practical video''' ==== | |||
==== Pros and cons ==== | |||
=== '''<u>Dynamic window approach</u>''' === | |||
==== Motivation ==== | |||
Our motivation for choosing the Dynamic Window Approach (DWA) method is its ability to optimize both velocity and trajectory in real-time for safe and efficient navigation. This algorithm allows our robot to dynamically adjust its path by considering its kinematic constraints and the surrounding obstacles, ensuring precise and responsive movement. | Our motivation for choosing the Dynamic Window Approach (DWA) method is its ability to optimize both velocity and trajectory in real-time for safe and efficient navigation. This algorithm allows our robot to dynamically adjust its path by considering its kinematic constraints and the surrounding obstacles, ensuring precise and responsive movement. | ||
==== Solutions ==== | |||
==== '''Learnings from each solution''' ==== | |||
==== '''Visual representation of sim''' ==== | |||
==== '''Practical video''' ==== | |||
==== Pros and cons ==== | |||
== '''Exercise 3 - Global path planning''' == | |||
===== '''A* algorithm''' ===== | |||
Revision as of 09:52, 20 May 2024
Contents
- Introduction
- Group members
- Exercise 1 - The art of not crashing
- Exercise 2 - Local navigation
- Exercise 3 - Global path planning
- 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
| 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.
Solutions
Learnings from each solution
Visual representation of sim
Practical video
Artificial potential fields
Motivation
Our motivation for choosing the artificial potential field algorithm lies in its effectiveness for real-time obstacle avoidance and smooth navigation. This approach enables our robot to dynamically maneuver around obstacles by leveraging attractive and repulsive forces, ensuring efficient path planning.
Solutions
Learnings from each solution
Visual representation of sim
Practical video
Pros and cons
Dynamic window approach
Motivation
Our motivation for choosing the Dynamic Window Approach (DWA) method is its ability to optimize both velocity and trajectory in real-time for safe and efficient navigation. This algorithm allows our robot to dynamically adjust its path by considering its kinematic constraints and the surrounding obstacles, ensuring precise and responsive movement.