I teach the Robotics Course in the Department of Computing, attended by third years and MSc students. This is a one term course which focuses on mobile robotics, and aims to cover the basic issues in this dynamic field via lectures and a large practical element where students work in groups. We implement robotics ideas using the Raspberry Pi single board computer and Python using BrickPi interface boards and Lego NXT motors and sensors. The course always finishes with a competition where the groups compete to build and program the robot which can most effectively complete a certain challenge against the clock. See the bottom of this page for pictures and videos from previous years' competitions.
Huge thanks to Jan Czarnowski, Charlie Houseago, Zoe Landgraf, Shuaifeng Zhi, Binbin Xu, Edgar Sucar and Kentaro Wada who are the current lab assistants, have helped a lot with the development of the practicals, have repaired broken kits and are another point of contact for any problems.
Thank you very much to Stefan Leutenegger who cotaught the course with me in 2015 and developed new material. Thank you to Duncan White, Geoff Bruce and other colleagues from the department's Computing Support Group, and to teaching fellow Maria ValeraEspina, who helped significantly in the transition to the Raspberry Pibased platform in 2014. Thank you to Andrea Nicastro, Tristan Laidlow, Robert Lukierski, Lukas Platinsky, Jan Jachnik, Jacek Zienkiewicz, Jindong Liu, Adrien Angeli, Ankur Handa and Simon Overell who were previous Course Support Leaders and tutorial helpers on the course and substantially helped with the preparation of materials and exercises; and to Ian Harries and Keith Clark who developed earlier material from which the current course has evolved. In particular Ian Harries deserves the credit for making this the practicallydriven course it still is today.
Lecture and practical sheets for the course will be available for printing from the links below (the links will come alive gradually throughout term). I will hand out the practical sheets each week in paper form. Extra material relevant to each week's lecture or practical which would probably be useful for you to look at is available from links below the lecture plan.
The timetabled slots for Robotics are for four hours a week, on Tuesday, from 11am to 1pm and Thursdays, from 9am to 11am. Most weeks we will have a one hour lecture at 11am on Tuesday, and three hours of compulsory practical at 12pm to 1pm on Tuesday, and from 9am to 11am on Thursday.
Tuesday lectures are in Lecture Theatre 308, Huxley Building and practicals are in the Main teaching lab 219, Huxley Building. The course runs for 8 weeks from week 2 to week 9 of college term; but we will not use all of the timetabled slots. The full plan for lectures and tutorials will be kept up to date below, and I will also announce it in lectures and via email when there are changes.
Practical exercises will be set in the lectures and assessed regularly in the labs via demonstration and discussion with me or the lab assistants, as explained clearly in the practical sheets . At the start of the course I will ask you to organise yourselves into fixed groups for the practical sessions throughout term. The number of members in each group will be confirmed in lectures depending on how many students take the course but will probably be 4. These assessed practicals form the only coursework element of the Robotics course, and no additional assessed exercises will be set. We will add up all marks from practical assessments throughout term to form a final coursework mark for each group. Each practical group will be given a Raspberry Pi/Lego NXT robotics kit to keep and be responsible for during the whole term, so you will be able to spend extra time as needed during the week to complete the practical exercises.
In the first week of term there will be no practical. Instead we will have a two hour lecture on Tuesday, and a one hour tutorial in the lecture theatre on Thursday at 9am. We will not use the second hour on Thursday this week.
Tuesday 11am  Tuesday 12pm  Thursday 9am  Thursday 10am  
Week 1 Oct 8 and 10 
Lecture (311) Introduction to Robotics 
Lecture continued (311) 
Tutorial (308) Robot Floor Cleaner Answers 

Week 2 Oct 15 and 17 
Lecture (308) Robot Motion 
Practical (219) Getting Started and Accurate Motion 
Practical continued (219)  Practical continued (219) 
Week 3 Oct 22 and 24 
Lecture (308) Sensors (Behaviours) 
Practical (219) Investigating Sensors 
Practical continued (219)  Practical continued (219) 
Week 4 Oct 29 and 31 
Lecture (308) Probabilistic Robotics 
Practical (219) Probabilistic Motion and Sensing 
Practical continued (219)  Practical continued (219) 
Week 5 Nov 5 and 7 
Lecture (308) Monte Carlo Localisation 
Practical (219) Monte Carlo Localisation 
Practical continued (219)  Practical continued (219) 
Week 6 Nov 12 and 14 
Lecture (308) Advanced Sonar Sensing 
Practical (219) Place Recognition 
Practical continued (219)  Practical continued (219) 
Week 7 Nov 19 and 21 
Lecture (308) SLAM and Planning 
Practical (219) Localisation Challenge 
Practical continued (219)  Practical continued (219) 
Week 8 Nov 26 and 28 
Revision Lecture (311)  Guest Lecture: Dr. Rob Deaves, Dyson (311) 
Practical (219)
Challenge competition 
Challenge competition continued (219) 
Here are electronic versions of extra handouts given out in paper form during the course and some additional resources.
Week 1: 
Python Tutorial The official Python Tutorial is very good and well worth reading if this is the first time you are using Python.. 
Week 2: 
WiFi and Web Interface Setup Instructions 
Webbased access to your Pi via the MAC address 
Week 3: 
Week 4: 
Modelling the World in RealTime, Andrew Davison 
Week 5: 
Monte Carlo Localization: Efficient Position Estimation for Mobile Robots, Dieter Fox, Wolfram Burgard, Frank Dellaert, Sebastian Thrun 
Week 6: 
Week 8: 
SLAM Tutorial Part 1, Hugh DurrantWhyte and Tim Bailey 
SLAM Tutorial Part 2, Tim Bailey and Hugh DurrantWhyte 
The 2018 challenge was about accurate and fast localisation against a map, with robots starting in a randomly placed location and having to visit other waypoints as quickly as possible. The winning team of Qiulin Wang, Rui Zhou, Jianqiao Cheng and Chengzhi Shi had an extremely fast and efficient robot!
This year we had a path planning challenge, where the groups had to program their robots to use their sonar sensor to detect obstacles in a crowded area and then use a local planning approach to find a smooth but fast route across. Most groups followed the Dynamic Window Approach that I had shown in the lectures (and which you can try in simulation using this python code). This video shows the incredibly fast and smooth robot from the winning team of Alessandro Bonardi, Alberto Spina and Riku Murai. Thanks to Charlie Housego for the photo, video and commentary!
The winning robot was developed by Nicolas Paglieri, Clemens Lutz, Antonio Azevedo and Francesco Giovannini and completed the challenge in a remarkably fast 21 seconds, though impressively around half of the teams completed the whole course and a couple came close to this time. The winning team's robot used the Lego light sensors cleverly as proximity sensors, allowing giving the robot an extra obstacle sensor which was particularly useful at high speed, and this together with fast planning gave them the best time.
The winners' robot was remarkably precise, and its motion included particularly nice curved entries into the waypoint spaces, all while maintaining very good speed such that it beat its nearest competitor by 8 seconds. The members of the winning group were Alexandre Vicente, Ajay Lakshmanan, Garance Bruneau, Kevin Keraudren, Axel Bonnet and Zae Kim (video courtesy of Jindong Liu).
The winning team this time consisted of Jim Li, Daniel Abebe, Robert Kopaczyk, Nicholas Heung and Cheuk Tam, and their robot's successful completion of the course in under 40 seconds is shown below (video courtesy of the team).
Again there were several teams which achieved the challenge impressively within the target time of 30 seconds, but the winners by a narrow margin were Ivan Dryanovski, Tingting Li, Wenbin Li, Edmund Noon and Ke Wang whose winning run is shown in the video below (video courtesy of the team).
Several of the teams achieved good results, and one or two even made promising progress on the more difficult problem of global localisation (the "kidnapped robot problem"), where the robot had to initialise a localisation estimate from scratch when dropped at an arbitrary position in the course. This video shows the robot of the team of David Passingham, Vincent Dedoyard, John Payce and Mengru Li in action (video courtesy of the team).
The winning robot was from the team of Philip Stennett, Nicholas Ball, Maurice Adler and Wei Chieh Soon which completed the course all three times with a total time of 36.9 seconds  this is a (very dark) video of their robot in action . The robots from the team of Si Yu Li, Henry Arnold, Shobhit Srivastava and Jonathan Dorling, and the team of Ricky Shrestha, Hussein Elgridly and Maxim Zverev also successfully completed the course three times.