Master ME4512 Autonomous Mobile Robot

ME4512 Autonomous Mobile Robot - Robot tự hành

Điểm danh 15 phút đầu giờ học  và 15 phút sau tiết nghỉ giữa giờ học 

Master Class 20261 ,  Autonomous mobile Robot ,  

Class website: https://sites.google.com/view/bblab/teaching/master-me4512_autonomousrobot

Team code: o4v1tjx

Class sheet: 

https://docs.google.com/spreadsheets/d/13oYYeaAnrTVY9SE9rM6xzfMSP4AOmTVK-w9pVFYRYag/edit?usp=sharing

Link lecture materials:https://sites.google.com/view/bblab/teaching/me4512_rbth-autonomousrobot

Google drive document:

https://drive.google.com/drive/folders/1NZaiy-0S4KbtwfU9n9dsiQTA5Hlh65i9?usp=sharing

Course materials: 

Introduction to Autonomous Mobile Robots, 2004. Roland Siegwart and Illah R. Nourbakhsh

English lecture materials:

https://asl.ethz.ch/education/lectures/autonomous_mobile_robots/spring-2020.html

I. Practical excercises:

HW 1:  Setting up UBUNTU and ROS 2

Please take a screenshot of your computer running Ubuntu and ROS version and submit it to assignment on team 

1. Install the latest Stable version of Ubuntu 

https://releases.ubuntu.com/

Recommnet install directly on a machine running both Windows and Ubuntu simultaneously. Create partion of approximately 30Gb ~ 

https://ubuntuhandbook.org/index.php/2022/04/install-ubuntu-2204-step-by-step/

Video vietnamese:  https://www.youtube.com/watch?v=UoHK_gX_Q_o

 Select step 5: Manually create system partition to để tạo partition cài Ubuntu song song với window, hay ubuntu bản cũ hơn

Select Step 5: Manually create system partion for installing Ubuntu alongside window, or older version of ubuntu 

Create a bootable USB drive for installing Ubuntu: 

https://linux.how2shout.com/how-to-create-ubuntu-22-04-bootable-usb-drive-on-windows/

 2. Installing the ROS2 operation System:

https://docs.ros.org/en/lyrical/

Create workspace for ROS projects:

http://wiki.ros.org/catkin/Tutorials/create_a_workspace

Check Ubuntu version 

~$lsb_release -a 

ROS:   ~$ rosversion -d

Take a screenshot of the rosversion screen and upload it to assignment on the team. Check Turn In button -> Finish 

HW 2:  Self-study  the beginer level lessions in ROS2 tutorial

Examples: CLI tools , Beginner: Client libraries

https://docs.ros.org/en/jazzy/Tutorials.html

Please take screenshot at least three  tutorial and submit them to team 

HW 3:  Installing and running Turtle bot simulation Cài đặt và chạy Turtle bot simulation

Please take a screenshot of running program and submit them to the Ms Team:

Reference link : https://emanual.robotis.com/docs/en/platform/turtlebot3/simulation/#gazebo-simulation

Tutorial video: https://www.youtube.com/watch?v=_RBZ-9_rz9Y

Screen shot roslaunch turtlebot3_navigation turtlebot3_navigation.launch and send to Assignment on Team

HW4: LiDAR–Inertial–Visual Odometry

Summary this scientific paper in 10-15 slide powerpoint

FAST-LIVO2: Fast, Direct LiDAR–Inertial–Visual Odometry

https://arxiv.org/abs/2408.14035

Hw 5: Run Robocon 2027 simulation

1. Download simulation from Github

 https://github.com/shinichiba/robocon2027_simulator.git

Run robot on gamefield from base on readme tutorial file 

https://github.com/shinichiba/robocon2027_simulator/tre/main/src/builder_robot_sim

Take picture and upload to Ms team  as proof. 

2. Find and list 5 paper about fast navigation with dynamic obstacles for mobile robot in competition robotics game field. Summary Each paper in 2-3 slides. So the total length of report is about 15 slides. 

Final Project:  Program simulated Bulder Robot to compete on Simulation Robocon 2027 Game field

Vẽ sân bằng phần mềm 3D và hiển thị sân trên Gazebo, mô phỏng robot di chuyển trên sân. 

Tạo môi trường mô phỏng robot và sân thi đấu robocon theo mẫu: 

https://github.com/omkar-robotics/ros2-slam-navigation2-gazebo

https://www.youtube.com/watch?v=xX25qvZIKyU&t=156s

Ví dụ Promt AI: Hướng dẫn tôi thiết kế một robot 2 bánh đơn giản, sau đó cho vào gazebo và mô phỏng SLAM, navigation với ROS2

Github team BBLAB

https://github.com/luongngoctu3010/Robocon2027

“Adaptive LiDAR–Vision–IMU Sensor Fusion for Robust Localization and Navigation of Autonomous Mobile Robots in Dynamic Environments”

Vision–LiDAR Fusion for Robust AMR Navigation

When camera or LiDAR alone fails, can multimodal perception maintain navigation?

Point-cloud + RGB fusion, BEV representation, Transformer fusion

Real-Time Autonomous Navigation on Resource-Constrained Edge Computers

How can sophisticated AI navigation run at real-time speed on embedded hardware?

Model compression, quantization, TensorRT, NPU/GPU acceleration, edge computing

Một số mô hình xe tự hành dùng Omni Wheel