Mobile Robot Control 2024 Optimus Prime: Difference between revisions
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=== '''<u>Introduction</u>''' === | === '''<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. | 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. | ||
Revision as of 10:53, 20 May 2024
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.