Robotics Lecture Course (course code 333)

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 co-taught 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 Valera-Espina, who helped significantly in the transition to the Raspberry Pi-based 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 practically-driven 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.

Autumn 2019 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
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)
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)
Practical (219)
Object Finding 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)

Additional Handouts

The directory RoboticsResources/ contains all the lecture notes, tutorial sheets, additional handouts below and also bits of code that I point to in the practicals.

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
Web-based access to your Pi via the MAC address
Week 3:
Week 4:
Modelling the World in Real-Time, 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 Durrant-Whyte and Tim Bailey
SLAM Tutorial Part 2, Tim Bailey and Hugh Durrant-Whyte

Localisation Challenge in 2018

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!

Path Planning Challenge in 2017

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!

Object Finding Challenge in 2016

This year we tried quite a different challenge, but as ever with the emphasis on sensing, accurate localisation and speed. Three bottles were randomly placed within the course area which we have previously used for Monte Carlo Localisation, and the goal was to find them, proving this by bumping into each in turn, and then returning to the starting point within the course to prove that the robot was still localised. Several teams did very well, and the challenge was won by the team of Saurav Mitra, Andrew Li, Daniel Grumberg and Mohammad Karamlou who had a very fast and innovative strategy.

Localisation Challenge in 2015

This year we again had a large class (150) and introduced new custom motor controllor software by Stefan Leutenegger and Jan Jachnik to run on the Raspberry Pi/BrickPi kits. The groups had to tune their own PID controllers to make their robots move smoothly and repeatably. The final challenge was localisation in a course next to a wall, where three locations had to be visited as quickly as possible. The challenge was won by the team of Arnas Kaminskas, Baisheng Song, Jiayun Ding, Siqi Wei and Yiming Lin who overcame various hardware difficulties during to course to build a very well calibrated and precise robot.

Localisation Challenge in 2014

In 2014, with more students than ever on the course (140+), for the first time we used a new robotics platform with the combination of Raspberry Pi/BrickPi/Python/Lego NXT motors and sensors. For this year's challenge the groups had to build robots which used odometry and sonar to solve a tricky localisation and navigation problem as fast as possible. Within a walled area for which the teams were given a map in advance, the robot was placed in one of five known locations chosen randomly and at a random orientation. It had to recognise its location by taking a sonar scan and matching this against saved templates, then set off to navigate to each of the other four locations in turn before returning to its start point, using continuous Monte Carlo Localisation or other techniques of the group's choice. Accurate motor control proved to be tricky via BrickPi (something we have hopefully improved substantially for 2015 with new low level control software), but some groups still achieved very good solutions. This is the very impressive winning robot from the team of Christos Karamanos, Panos Evagorou, Pamela Cruz and Andrew Jack which used the sonar to full advantage during navigation by deliberately turning it to point orthogonally at walls for high quality measurements.

Localisation Challenge in 2013

We returned to a localisation challenge similar to the one in 2011, with the groups' robots racing against the clock to reach three waypoints in a course laid out close to the wall of the lab. A wall-following approach was generally the most successful. This year there were a very large number of students doing the course, and we had 29 groups competing, of which around 10 managed to complete the course. Several groups got very good times, but with a fast time in their second run the group of Udara Gamalath, Alejandro Garcia Ochoa, Tristan Pollitt, Shailesh Ghimire and Maxwell Popescu clinched victory in 51 seconds. Sorry that I don't have a video of their robot's winning run!

Mapping and Navigation Challenge in 2012

This year we had a completely new challenge where the teams had to build robots able to cross an area filled with obstacles without bumping into any of them. The robots had to rely on good odometry for localisation, and then use sonar to detect obstacles and plan a path between them. Marks were given depending on how far the robots managed to advance, with extra time points for those teams that made it all the way across. The starting point was the techniques for occupancy grid mapping we had learnt in the course, but the desire for speed meant that many teams switched to simpler methods due to the high computational cost of occupancy mapping.

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.


Localisation Challenge in 2011

We used quite a different layout for this year's localisation challenge, with the robots needing to recognise their location and orientation in one of three randomly chosen places and then navigate fast along a long corridor to pre-determined waypoints. Marks were given both for accuracy and 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).


Localisation Challenge in 2010

The challenge this year was similar to 2009, but in a new course and with a marking scheme which emphasized accuracy over speed. Again, the robots were placed randomly by me at one of five pre-learned waypoints and had to determine their locations and navigate autonomously to all of the other waypoints --- within 5cm accuracy to gain full marks, which was a challenging problem.

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).

Localisation Challenge in 2009

This year's competition challenge required the robots to first localise from scratch at one of five pre-learned locations, and then to localise continuously to navigate around a route of waypoints. The whole process was timed so there was some need to trade off accuracy for speed in order to win.

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).


Monte Carlo Localisation in 2008

The challenge at the end of the course in 2008 was to implement a probabilistic localisation algorithm based on particle filtering, using the Lego Mindstorms NXT kits with motor odometry, sonar and compass sensors. The groups were given a "map" of a small enclosed area, indicating the measured locations of walls relative to a fixed coordinate system. The goal was to use Monte Carlo Localisation to keep a continuous estimate of the moving robot's position which was good enough to accurately follow a pre-defined path through a sequence of waypoints.

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).

Robot Obstacle Course in 2007

At the end of the course in 2007 we had a robot obstacle course challenge where the students in groups of three or four designed Lego robots to reach a light beacon while bouncing off and avoiding obstacles. The robots used light sensors to detect the direction of the bright light and bump sensors to detect collisions. Each of the robots was timed over three runs from different start points on a course constructed by Simon Overell.

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.

Robot Racing in 2006

Last year we finished the course with a time-trial competition between Lego line-following robots designed and programmed by the students in groups of three. The robots used downward-looking light sensors to observe the line, and ran programs written in the C-like NQC language. The robots were timed over three laps of this course, which the groups saw for the first time on the day of the races:

The easy winner of the time-trial was the simple but very slick robot from Jiefei Ma, Winnie Xu Zheng and Nan Wang which you can see in action in this movie. Another interesting robot featuring articulation from Steven Lovegrove, Alex Lamaison and Folabi Ogunkoya performed very well in practice but didn't quite make it round three laps of the race track. Also see this movie of several of the robots on track at once. (Thanks to Jiefei Ma and Steven Lovegrove for providing the videos).
Andrew Davison