The Robocup Standard Platform League competition is a highly competitive league, with very little separating the top teams. Winning the competition in consecutive years is particularly challenging as other teams look to counter the tactics and game play of the previous champions. As the reigning champions from 2014, team UNSW Australia was able to overcome this challenge and win the competition for a second consecutive year. Although this success is not only related to developments from this year, this paper focuses on the new innovations and development by team UNSW Australia for the 2015 Robocup Competition. These innovations include white goal detection, whistle detection, foot detection and avoidance, improved path planning and new odometry.

This paper introduces an Iterative Closest Point (ICP) inspired inverse sensor model for robot localisation given multiple simultaneous observations of aliased landmarks. Combined with a Kalman filter, the sensor model offers a robust alternative to maximum likelihood data association, or a computationally inexpensive alternative to a particle filter. The technique can also be used as a means for re-localising a kidnapped robot, or a sensor resetting method for a particle filter. In the RoboCup Standard Platform League, this sensor model is able to localise the robot from a single observation in 42% of field positions where multiple landmarks are visible.

Controllers for autonomous mobile robots that operate in rough terrain must consider the shape of the surrounding terrain and its impact on the robot's movements. For complex terrain, these interactions are extremely difficult to model in a way that allows traditional controllers to be built. We have used Behavioural Cloning, a type of learning by imitation that produces rules that clone the skills of an expert human operator. We have also developed an autonomous instructor in simulation and used it to generate training data from which we have cloned controllers. The resulting controllers perform at a level comparable to that of a human expert. The controllers behave similarly both in simulation, where they were developed, and on the physical robot without the need for further modification or training.