Mobile Robot Control 2024 Optimus Prime
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.
Solution 1
The odometry and laser data were obtained from the sensor which makes the robot aware of the surroundings although the data doesn't provide any instructions for the robot to stop or go, discussion was done to include a constant safe distance value so that the robot doesn't bump the obstacles. Loops were introduced in cases which allows us to make the robot to follow the code as long as the condition is satisfied. In this case if the obstacle were close to the robot (range < safe distance), the robot comes to halt.
Solution 2
Alterations done to the previous solution by introducing rotation values which make the robot to stop the movements in all directions in case of obstacle too close and then to move at a forward distance.
Learnings from solution
The robot's performance in this case was evidenced in both simulation and in practical session. Alterations in the code were done so that, in case of the obstacle detection the robot was made to move in the x direction in adherence to the safe distance.
Visual representation of simulation Practical video
Not able to link the video maybe just pictures are only allowed, not sure though.
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 manoeuvre 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.