Grading Scheme

The grading scheme for the course, as well as links to specific requirements for each assignment/deliverable and evaluation sheets, are given in the table below. Due dates for each assignment/deliverable can be found on the course Calendar. Please note:

There is a 25% penalty per day for any late submissions. "Late" means handed in after the deadline. Thus, if the deadline is 5pm and you hand in an assignment at 5:01pm, you will be penalized. The penalties are cumulative. If an assignment is due at 5pm on Monday and you hand it in at 5:01pm on Tuesday (two days late), your grade will be (1-25%)^2 of the grade you would have received had you turned it in on time.

Some assignments are "Individual" and team members are individually responsible for completing the assignment on time and will receive an individual grade. Many assignments are "Team" assignments and a single deliverable is handed in by the team. In most cases, all team members will receive the same grade on these assignments. However, the course staff reserves the right to "break up" any group's work and grade individually. This will be done if we feel the work or work quality has not been evenly distributed between group members.

The evaluation sheets provide a sense of what we are looking for with each deliverable. You should keep in mind, though, that the evaluation is not strictly binary. In other words, just because you have "checked off" each component described in the evaluation sheet does not ensure that you will receive a perfect score.

Below is the points breakdown for all assignments/deliverables for the course, sorted chronologically:

* Grades for these will be the average of the TA and Instructor grades; peer review grades will be used to provide feedback.
** Evaluation Sheets are subject to minor changes.

Featured Project

## Team Members

- [Yuhao Ge], [yuhaoge2],

- [Hao Chen], [haoc8],

- [Junyan Li], [junyanl3],

- [Han Yang], [hany6].

## Project Title

Remote Robot Car Control System with RGBD Camera for 3D Reconstruction

## Problem

We aim to build a user-friendly control system for assisting users to remotely control a robot car equipped with an RGBD camera in complex indoor environments. The car should be able to build the environment based on the point cloud scanned by the camera, and the remote computer will reconstruct the point cloud to gain the map of the environment.

## Solution Overview

Our solution consists of a Robot Car Subsystem, Camera Subsystem, Remote Control Subsystem, and Human-Robot Interaction Interface. The Robot Car Subsystem includes a robot car and a rotating base for the RGBD camera. The Camera Subsystem captures RGBD images of the surrounding environment and performs real-time 3D reconstruction. The Remote Control Subsystem allows users to control the robot car remotely via a joystick. The Human-Robot Interaction Interface provides a third-person perspective view of the reconstructed environment and allows users to interact with the robot car in real-time.

## Solution Components

- Robot Car Subsystem: Includes a robot car and a rotating base for the RGBD camera.

- Camera Subsystem: Captures RGBD images of the surrounding environment and performs real-time 3D reconstruction using image signal processing software.

- Remote Control Subsystem: Allows users to control the robot car remotely via a joystick.

- Human-Robot Interaction Interface: Provides a third-person perspective view of the reconstructed environment and allows users to interact with the robot car in real-time.

## Criterion for Success

- The remote robot car control system can navigate and avoid obstacles in complex indoor environments.

- The Camera Subsystem can perform real-time 3D reconstruction with high accuracy and reliability.

- The Remote Control Subsystem provides a smooth and responsive control experience for the user.

- The Human-Robot Interaction Interface provides an intuitive and user-friendly way for users to interact with the robot car and view the reconstructed environment.

## Distribution of Work

- Han Yang (EE): Camera Subsystem design and implementation

- Hao Chen (ECE): Remote Control Subsystem design and implementation

- Junyan Li (ECE): Human-Robot Interaction Interface design and implementation

- Yuhao GE (ECE): Robot Car Subsystem design and implementation

## Justification of Complexity

We believe that our team has the necessary skills and knowledge to handle the mechanical and electrical complexity of our project.

Specifically, Han Yang has experience in image signal processing and Hao Chen has experience in remote control systems. Junyan Li has experience in human-robot interaction design, and Yuhao Ge has experience in robotics and mechanical design. Additionally, we plan to use readily available off-the-shelf components and design our system in a modular and scalable way to minimize the complexity and facilitate the development process.

Item	Team / Individual Score	Points	Evaluation Sheet**
Project Selection Form	Individual	5	None
Lab Notebook	Individual	50	PDF
Weekly TA Meetings	Team	N/A	None
Weekly Team Update Meeting	Team	3/session	None
Team Contract	Team	5
Project Proposal	Team	25	PDF
PCB Design Exercise	Individual	10	PDF
Soldering Exercise	Individual	10	PDF
Design Document Requirements and Verification	Team	40	PDF
Individual Progress Report	Individual	25	PDF
Mock Demo	Individual	5	None
Final Demo *	Team	150	PDF
Final Presentation *	Individual	50	PDF
Final Report: Technical	Team	30	PDF
Final Report: English/Format	Team	20	PDF
Peer Reviews (2 total)	Individual	40 (total)	None
Teamwork & Participation	Individual	20	None