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docs/course-deliverables/group.md

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+# Group Assignments
+
+## Physical Robot
+As the course progresses your group will be required to present physical evidence of your robot's progress, including videos of your **physical robot** completing the given objectives.
+
+### Hardware
+*TODO*
+
+### DonkeyCar: Computer Vision 
+*TODO: link documentation for physical autonomous laps in DonkeyCar*
+
+### DonkeyCar: GPS Navigation
+*TODO: link documentation for GPS laps in Donkey*
+
+### ROS2
+#### Line-Following Laps
+*TODO: line following docs*
+
+#### Lane-Following Laps
+*TODO: lane following docs*
+
+
+## Final Project Progress Reports
+Starting around **Week 6** your group will give a short, 5-minute presentation to your classmates at the end of each week. These progress reports should be no more than 3-5 slides discussing what goals you have met, what you are still working on, what is working, and what still needs debugging.
+

docs/course-deliverables/individual.md

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+# Individual Assignments
+
+## DonkeySim
+During this course, you will need to upload a video of your trained model car successfully completing 3 autonomous laps on the DonkeySim track **using three different scenarios** that are outlined in the [Virtual Machine Guide](../guidebooks/50-vm-guide.md).
+
+### 3 Autonomous Laps on `localhost`
+*TODO: Link to DonkeyCar documentation for local host*
+
+### Training on the GPU Cluster
+*TODO: Link to documentation for GPU cluster*
+
+### 3 Autonomous Laps on remote server `roboticist.dev`
+*TODO: Link to documentation for remote server*
+
+## The Construct 
+*TODO: link online course details*

docs/course-overview/outline.md

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+# Introductions and Class Logistics
+
+## Instruction Team
+- Jack Silberman - [[email protected]](mailto:[email protected]) - PhD, Faculty MAE, ECE and HDSI
+- TBD - email - TA/Tutor
+
+## Safety
+- Follow the return to learn, that simple
+    - Lab access. Please observe UCSD’s safety guidelines - Compliance with required trainings.
+    - Be safe, ask for help; don’t be afraid to test things but be safe. You are in school to learn, you are supposed to have help.
+
+## Adapting to Changes
+- For the past 5+ years, more than 1/2 of you would not be in this class. ***Be patient with us***.
+    - We expanded it from 28 to 60 students, 7 to 15 Teams.
+    - This means we had to find a space to fit all of you in a lab environment. *Please help by not talking at the same time as the instructional team.*
+- We basically have double of the workload with the same resources.
+    - Do your part and be patient when things don’t work. Do some hacking and look for solutions with us. **Don’t expect solutions given to you without doing some work too.**
+- We are using cutting-edge technology; *it may cut us a bit while we master it.*
+
+## Logistics
+- Lectures and times are mixed since we do hands-on lab within the lectures.
+    - Tuesdays and Thursdays: **5:30 - 6:50 PM**
+        - Office hours at the lab: 7:00 - 8:30 PM
+    - Additional office hours TBD as needed
+- If needed, access to EBUII 339 TritonAI lab TBD with TA
+- Discord for communication, Professor Silberman will share the invite link
+- Assignments will be posted on Discord channels
+    - Video evidence of course deliverables to be shared on Discord
+- See *Grading Formula*
+- Top requests from past quarters
+    - More time for final project
+    - Help on Python coding
+    - More structure on "what" and "when"
+- **This is a 4 credit class.** If you cannot dedicate 10 hours per week, please consider giving a chance to the dozens of other students on the waiting list. If you put in the effort, you will be successful.
+- This class is hands-on-fun-busy where you will gain *highly useful skills to add to your resume.*
+- There are no exams or lists of exercises to work on.
+    - Your time will be used learning new skills that can make you **stand out** on job or graduate school applications.
+    - There are two 360 evaluations where your teammates will judge you. Also, other teams can help on your grade. More on that in grading formula.
+- CBT / e-book license on Robot Ignite Academy (The Construct) is required. You have homework already.
+
+## Curriculum
+- Deep Learning AI - Human Behave Cloning
+    - Simulator on a virtual machine on your host computer.
+    - AI model Training using UCSD’s Supercomputer Center.
+    - Multi-robots race online against racers from around the world.
+    - Autonomous laps using a Physical robot at UCSD’s scale race track.
+    - Optional - We were invited for an event sponsored by a Bank in the Bay Area
+        - Date to be confirmed - end of the quarter
+        - 10 to 15 students sponsored to travel for a day in Oakland California
+        - Help companies founder train their robots with their kids then race; possible network opportunity for you.

docs/course-overview/syllabus.md

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+# 10-Week Course Syllabus
+
+## Week 1
+### Lecture and Lab
+- Class expectations
+- "What will a robotics class enable me to do?"
+	- Search web for robotics jobs, i.e. keywords 'deep learning,' 'ROS2,' 'AI'
+- Grading formula
+- Syllabus
+	- Classes schedule and high level deliverables
+- Introduction to the class resources
+	- Laboratory and tool
+- Laboratory and safety training
+- Design robot, start building robot
+- Deep Learning
+	- Virtual Machines
+	- DonkeyCar: running on student host computer
+	- DonkeySim: running on student host computer
+- CBT/e-book
+	- Embedded Linux
+	- Python
+	- Concepts of ROS2
+
+### Assignments Due
+- Begin designing robot
+	- Electronics mount plate
+	- 3D print camera mount
+	- 3D print single board computer case
+- DonkeyCar DonkeySim
+	- 3 autonomous laps running on student host computer **(due Tuesday of Week 2)**
+- Linux and Python traning modules
+- ROS2 Basics in 5 Days (Python) Section: Introduction
+
+## Week 2
+### Lecture and Lab
+- Team members review and adjust. Let’s lock the teams. Starting this week it will be very hard for people to join and catch.
+- Deep Learning
+	- Review of Virtual Machines and Host Machines
+		- We use a virtual machine image from a hypervisor company called VMware
+		- The instructions to use our virtual machine is here
+	- DonkeyCar - running on students host computer
+	- DonkeySim - running on the external server
+- Embedded Linux on a low power single board computer (SBC)
+	- How can we install software without a computer monitor, keyboard, and mouse connected to an embedded computer (single board computer)?
+- Installing the software into the SBC
+- Jetson Nano Single Board Computer (SBC) Hands-on
+	- Remote access without a monitor, keyboard, mouse
+		- Initial connection using a USB cable
+		- Connect to a local WiFi access point e.g., UCSDRoboCar
+	- Multi-user on a low power SBC
+	- User Security
+	- Installing software using Secure Shell (SSH)
+	- Remote Desktop
+- Robot Components and Electronics
+- GPS Based Navigation
+	- GNSS (GPS) 3D Localization
+	- RTK GNSS Error Correction
+		- Base Station
+		- Services
+			- Open source
+			- Paid services
+- Robot design completed, and major components in place
+	- Completed
+		- Electronics mount plate
+		- 3D printed camera mount
+		- 3D printed case for the single board computer (SBC)
+	- Incorporate
+		- GNSS unit and antenna
+		- Electronics wiring
+- CBT / e-book
+	- ROS2 Basics: Topics, Launch files
+
+### Assignments Due
+- Donkey Car DonkeySim 3 autonomous laps - sim running on the external server **(due Thursday of Week 2)**
+- Basic software setup on Jetson (Jetpack, WiFi, hostname, DonkeyCar, etc.) **(due Tuesday of Week 3)**
+- Robot components ready **(Thursday of Week 3)**
+	- Mechanical Components
+	- Electrical Components
+	- Software - Linux, Jetpack, OpenCV GPU Accelerated, DonkeyCar 
+	- Deep Learning laps
+	- DonkeyCar GNSS Navigation
+- ROS2 Basics in 5 Days (Python) Sections: Basic Concepts, Topics
+
+## Week 3
+### Lecture and Lab
+- UCSD’s SuperComputer GPU Cluster Deep Learning acceleration
+- Hands-on GPS/GNSS Based Navigation - putting all together - In font of EBU I
+	- 3 outdoors autonomous laps using GNSS
+- 3 Autonomous Laps Deep Learning on EBUII outdoor track
+
+### Assignments Due
+- 3 Autonomous Laps GNSS / GPS - EBU I **(Tuesday of Week 4)**
+- 3 Autonomous Laps Deep Learning - EBU II **(Thursday of Week 4)**
+- ROS2 Basics in 5 Days (Python) Section: Services
+
+## Week 4
+### Lecture
+- Introduction to Docker and Git
+- Introduction to UCSD ROS 2 Robocar Framework 
+- Demo of Python Camera Based Navigation
+- Introduction to Class Final Project requirements
+- Introduction to Project Management
+
+### Assignments Due
+- Class Final Project Proposal **(Tuesday Week 5)**
+- 3 Autonomous Laps ROS2 **(Thursday Week 5)**
+- ROS2 Basics in 5 Days (Python) Section: Actions
+- ROS2 Guidebook
+
+## Week 5
+### Lecture
+- Neural Network
+- Car Dynamics
+- Final Project Status Update
+
+### Assignments Due
+- Weekly progress report on final project
+
+## Weeks 6 - 10
+### Lecture
+- As needed/per request
+	- Suggested topics:
+		- Filtering/state estimation
+		- Path planning: GPS waypoint, LiDAR, SLAM
+		- PID control
+		- ROSBAGS, rviz, rqt
+
+### Assignments Due
+- Weekly presentation progress report on final project
+- **Bonus**: ROS2 NAV Course