Abstract
A fundamental task of robotic systems is to use on-board sensors and perception algorithms to understand high-level semantic properties of an environment. The performance of perception algorithms can be greatly improved by planning the motion of the robots to obtain high-value observations.

In this talk I will present a suite of planning algorithms we have been developing for these tasks. These methods aim to address the challenges of decentralised coordination, long planning horizons, unreliable communication, predicting plans of other agents, and exploiting characteristics of perception models. The proposed algorithms are motivated by several key ideas: Monte Carlo tree search, self-organising maps, branch and bound, optimal stopping, sweep planes, variational methods, orienteering problems, set cover, and Bayesian inference.

Speaker Bio
Graeme Best is a PhD candidate at the Australian Centre for Field Robotics (ACFR) at The University of Sydney under the supervision of A/Prof. Robert Fitch. His current research interests include planning algorithms for multi-robot teams performing coordinated perception tasks, with a particular emphasis on decentralised algorithms and probabilistic reasoning. Previously, he worked on projects involving machine learning for legged robots, marine robotics operations, and human-robot interaction. He received the B.E. (Electrical) and B.Sc. (Computer Science) from Monash University in 2014