1. Design and construct a robotic system to satisfy a given set of requirements, taking commer-cial and economic considerations into account
2. Demonstrate an awareness of the application of specific engineering principles and relevant professional, legal, ethical, environmental and social issues to robotic systems
3. Use mathematics to analyse and reason about a robotic system design
4. Analyse real world problems and synthesise integrated hardware and software solutions
5. Manage a well defined small scale research project
6. Apply appropriate transferable skills to document, report, analyse and evaluate a research project
7. Select, justify and apply appropriate software engineering processes to robotic systems
8. Work and study in a guided independent manner on a well defined research project
This assignment will address the Anki Cozmo mobile robot. This robot can move on tracks, has a camera for landmark and face detection, as well as a cliff sensor. You will develop against the Cozmo SDK in python, and have access to helper classes and boilerplate code specifically for this assignment.
This assignment provides a practice of representative challenges of mobile robotics. Your challenge is to navigate the robot to a known coordinate on a known map, but with unknown initial position and uncertainty of actuators and sensors on the way. In order to successfully navigate to the goal the robot has to find out where it is and continuously update its belief from sensory data as it is moving. You will work with the basic kinematics and control of wheeled or tracked motion in order to control the robot along a desired direction. You will use the kinematics in order to tackle the robot’s odometry, i.e. to predict the robot’s movement based on observed track speeds. Visual sensing and sensor modelling will help to pinpoint the robot’s location on the way. You will use the Monte-Carlo localization algorithm to integrate all of this information and make it of use for navigation. In the last module session all students’ robots will compete over the quickest navigation to the goal. May the smartest fastest robot win!
For this coursework, the physical Cozmo can be exchanged for a simulation environment provided on moodle. This code provides a qualitativesimulation driving kinematics and dynamics including acceleration and slippage, and of sensors including track speed sensing, cliff sensing, and cube sensing. All of those aspects are contain stochastic inaccuracies that simulate errors sources on the physical robot. The robot will get stuck if moved onto the solid blue area, similarly to falling off the physical map board. This simulation is not quantitatively identical to the real robot, and does not have to be for the purpose of this coursework. The above screenshots show the simulation in the context of Monte-Carlo localization (template code provided, see below).On the left, the robot (orange rectangle) has been started off without seeing a cube. On the right, it has moved and sees a cube (orange square close to blue one), which leads to particles grouping around the robot’s real position (after successful implementation of Monte Carlo localization).
All source code must be submitted through git/bitbucket. A personal repository for this use will be provided. A final report has to be submitted on moodle on 9 th July week 12. nly as a single PDF document.
A mark and comments will be given individually for each marked bullet point in the below mark breakdown for interim as well as final assessment. Every module session has at least one hour reserved for coursework help and opportunity for formative feedback.
1) Motion Model and Driving (30 marks) (Interim assessment 1, week 5, 3 page limit)
a) Determine and implement the robot’s driving motion model parameters based on the standard differential drive model (implement track_speed_to_pose_change function stub in cozmo_interface.py) (10 marks) i. Experimentally determine the model’s wheel distance parameter
b) Experimentally demonstrate the model’s accuracy (e.g. by driving a full circle with track speed ) (5 marks) i. Complete the odometry loop in run-cozmo-odometry.py ii. Demonstrate the accuracy by comparing to robot’s physical position after motion
c) Implement track-motion’s inverse kinematics (implement velocity_to_track_speed in cozmo_interface.py) (5 marks)
d) Implement a turn-approach-turn maneuver to drive the robot onto a desired target position and orientation (implement target_pose_to_velocity_linear and complete loop in cozmo-run-linear-approach.py). Evaluate the effectiveness of this maneuver. (5 marks)
e) Implement a cubic spline interpolation based maneuver to drive the robot onto a desired target position and orientation (implement target_pose_to_velocity_spline and implement cozmo-run-spline-approach.py analogous to previous maneuver). Evaluate the effectiveness of this maneuver. (5 marks)
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