Summary Details

This assessment (Construction of a Navigating Mobile Robot) contributes 100% of the assessment for this module.

Clearly indicate your Name on every separate piece of work submitted.

Submission is via VLE and is due by 12:00 on 13 January 2025 (Semester 1 Common Assessment Period, Monday) as found on the Statement of Assessment. Please try and submit early as any late submissions will be penalised. Assessment information including on late penalties is given in the Statement of Assessment.

Construction of a Navigating Mobile Robot

You are required to create a robot that can map and efficiently make its way through an indoor   environment.  You will also need to prepare and deliver a 5-minute presentation on your robot design and the algorithm you have implemented for navigating and obstacle avoidance.  Test mazes will be available with which you can develop and improve your algorithms, but one of the mazes that your robot will be evaluated on will not be revealed until the demonstration session after the assessment submission.  This will also test the generality of your approach.

First, you will need to build a differential-drive robot with a camera that can find its way through  a maze-like environment.  You have the use of the York Robotics Laboratory Kit that includes a chassis with motor and camera mounts, a Raspberry Pi 4, an Arduino Robotics Board (ARB) that provides easy-to-use sensor and actuator interfaces, a Pololu TB6612FNG dual motor driver, a set of infrared and ultrasonic range sensors, a set of micro-metal gear motors with encoders, wheels that fit on the motors, and other parts that can be used for constructing a small mobile robot..  To assemble and program your robot please refer to the laboratory documentation and resources for assembly guidance and tips.

You are permitted to customize your robot as much as you want as time permits before the assessment.  You can change the body design and add additional microcontrollers, actuators, sensors, and other components from those available in the robot laboratories to improve the performance of your robot.  However, you must ask the technical staff for access to additional components beyond those available in the robot kit and fabrication of additional parts beyond those required for basic functionality, and your final robot must conform to any and all safety or cost requirements imposed by the technical staff to be eligible for credit in the demonstration.  The project is split into four phases plus a presentation and demonstration of your robot’s performance, and the practical lab sessions during the module are designed so as to be part of your work in completing this navigating robot, so it is important that you complete all of the lab exercises.  Please note that your program code must be submitted along with other materials and is part of the assessment grading, so make sure to adhere to good coding practices: comment your code so as to be clear and easy for others to understand, and if you have used code from other sources e.g. github or online forums include complete references to where you obtained the code in your comments as well.

A navigable course environment with ArUco tags and geometric tags with different shapes and colours will be set up in the P/T/410 Robot Lab for your use.  An example layout of a course with tags of various kinds to aid in navigation is provided in Figure 1 below.  You are expected to show your robot's performance on both the course provided as an example in the module laboratory sessions, and also a new course which will not be available until the assessment demonstration date.  Your robot’s build quality and performance and your presentation together will be assessed after the hand-in date and graded out of 100 points.

Phase 1: Construction of a Differential-Drive Robot Chassis

You will need to construct a mobile robot chassis using the kit materials on which to mount the Raspberry Pi and electronics, battery pack, two motors, and a camera.  The quality and functionality of your robot chassis will be assessed at the demonstration after submission and will constitute 20% of the assessment grade, broken down into 10% for assembly (the robot is a clean build and does not fall apart) and 10% for functionality (the robot moves as desired and does not appear to have any incorrect or nonfunctional build elements).

Phase 2: Localization using Machine Vision

You will need to write a vision algorithm to allow your robot to navigate through an indoor environment.  The more information you can gather about the environment with respect to the  robot and the speed of the robot, the better your navigation will perform, so it is suggested that you use the range sensor fused in concert with the camera to predict where obstacles are.  The accuracy and real-time capability of your vision algorithm will be assessed in the presentation and demonstration and will constitute 20% of the assessment grade, broken down into 10% for  accuracy of sensing (things are where they are predicted to be) and 10% for quality of coding    and capability (well thought out use of algorithms/libraries with some originality in your process, runs fast enough to enable navigation, and provides data in a useful format).

Phase 3: Robot Control and Navigation

You will now need to write a controller that uses the camera and range sensor to travel from a start point to a destination point.  Your navigation will need to use wheel odometry, visual, and range feedback to increase accuracy.  You should also track the approximate locations of objects found by your vision system in a graph, map, or other structure for later use in planning.  You can use any algorithm you feel is appropriate from the course content.  The performance of your controller in traveling between the start and endpoints in the environment will be assessed in the presentation and demonstration and will constitute 20% of the assessment grade, broken  down into 10% for the quality of the control of your robot and 10% for the quality of code, clarity  of writing/commenting, and originality of your algorithm.

Phase 4: Mapping and Optimal Path Planning

The last task for your robot control system will be to traverse a path back to the start point, but this time by using an optimal path automatically determined by analyzing the map that your controller has generated on the way there.  The speed and accuracy with which your robot traverses optimal path back through the maze will be assessed in the presentation and demonstration and will constitute 20% of the assessment grade, broken down into 10% for optimality and efficiency of the path found and traversed (is it a good solution?), and 10% for the quality of critical thinking, originality, and practical implementation of your optimal path following algorithm.

Phase 5: Presentation and Technical Assessment

A presentation and demonstration session will beheld shortly after the hand-in date.  You will need to present your design and approach that you have implemented in a 5 minute presentation per person with 2 minutes for questions afterwards.  The performance of your robot and navigation will be assessed in a demonstration session following the presentation.

Your presentation will constitute 20% of the assessment grade, broken down into 5% for presentation quality (slide visuals and organization), 5% for ambition (the complexity and depth  of thought into your approach), and 10% for technical quality and critical thinking (algorithm and robot appropriate, algorithm implemented correctly, good design methods employed).

Deliverables:

To receive full credit in this assessment, you must submit the following items in a single .zip file, to the “Assignment Submission Point” provided on the module VLE site, by the deadline date and time given on the cover sheet of this assessment:

.    Functioning and commented code files for performing visual localization in Phase 2

.    Functioning and commented code files for performing control and navigation in Phase 3