This presentation addresses the problem of localising a signal source using a team of mobile agents that can only detect the presence or absence of the signal. Background false detection rates and missed detection probabilities are incorporated into the framework. A Bayesian estimation algorithm is proposed that discretises the search environment into cells. Analytical convergence results for this are then presented. Finally, distributed control strategies are discussed that drive the agents to search for the source, while guaranteeing specific inter-agent distances are maintained for the sake of communication connectivity. Simulation results demonstrate the effectiveness of this combined approach to estimation and control.
Daniel Selvaratnam is a PhD candidate at the Department of Electrical and Electronic Engineering, and is part of MIDAS Laboratory at the University of Melbourne. He received his Bachelor of Aerospace Engineering (Hons.) from Monash University in 2014, and was a visiting PhD student at the Automatic Control Lab, KTH Royal Institute of Technology in Stockholm, 2016. Daniel's research interests include control and estimation theory, and their application to planning and exploration algorithms for robotics.