@incollection{GrocholskyMakarenkoKauppetal., author = {Grocholsky, Ben and Makarenko, Alexei and Kaupp, Tobias and Durrant-Whyte, Hugh F.}, title = {Scalable control of decentralised sensor platforms}, series = {Information Processing in Sensor Networks ; Lecture Notes in Computer Science (LNCS, Volume 2634)}, booktitle = {Information Processing in Sensor Networks ; Lecture Notes in Computer Science (LNCS, Volume 2634)}, editor = {Zhao, Feng and Guibas, Leonidas}, publisher = {Springer}, address = {Berlin, Heidelberg}, isbn = {978-3-540-02111-7}, doi = {10.1007/3-540-36978-3_7}, pages = {96 -- 112}, abstract = {This paper describes an information-theoretic approach to decentralised and coordinated control of multi-robot sensor systems. It builds on techniques long established for the related problem of Decentralised Data Fusion (DDF). The DDF architecture uses information measures to communicate state estimates in a network of sensors. For coordinated control of robot sensors, the control objective becomes maximisation of these information measures. A decentralised coordinated control architecture is presented. The approach taken seeks to achieve scalable solutions that maintain consistent probabalistic sensor fusion and payoff formulations. It inherits the many benefits of the DDF method including scalability, seamless handling of sub-system activation and deactivation, and interoperability among heterogeneous units. These features are demonstrated through application to practical multi-feature localisation problems on a team of indoor robots equipped with laser range finders.}, language = {en} } @inproceedings{MakarenkoBrooksKauppetal., author = {Makarenko, Alexei and Brooks, Alex and Kaupp, Tobias and Durrant-Whyte, Hugh and Dellaert, Frank}, title = {Decentralised data fusion: A graphical model approach}, series = {2009 12th International Conference on Information Fusion}, booktitle = {2009 12th International Conference on Information Fusion}, pages = {545 -- 554}, language = {en} } @inproceedings{WangKumarRamosetal., author = {Wang, X. Rosalind and Kumar, Suresh and Ramos, Fabio and Kaupp, Tobias and Upcroft, Ben and Durrant-Whyte, Hugh}, title = {Probabilistic classification of hyperspectral images by learning nonlinear dimensionality reduction mapping}, series = {2006 9th International Conference on Information Fusion}, booktitle = {2006 9th International Conference on Information Fusion}, pages = {1 -- 8}, language = {en} } @article{KauppMakarenkoDurrantWhyte, author = {Kaupp, Tobias and Makarenko, Alexei and Durrant-Whyte, Hugh}, title = {Human-robot communication for collaborative decision making - A probabilistic approach}, series = {Robotics and Autonomous Systems}, volume = {5}, journal = {Robotics and Autonomous Systems}, number = {58}, doi = {10.1016/j.robot.2010.02.003}, pages = {444 -- 456}, abstract = {Humans and robots need to exchange information if the objective is to achieve a task collaboratively. Two questions are considered in this paper: what and when to communicate. To answer these questions, we developed a human-robot communication framework which makes use of common probabilistic robotics representations. The data stored in the representation determines what to communicate, and probabilistic inference mechanisms determine when to communicate. One application domain of the framework is collaborative human-robot decision making: robots use decision theory to select actions based on perceptual information gathered from their sensors and human operators. In this paper, operators are regarded as remotely located, valuable information sources which need to be managed carefully. Robots decide when to query operators using Value-Of-Information theory, i.e. humans are only queried if the expected benefit of their observation exceeds the cost of obtaining it. This can be seen as a mechanism for adjustable autonomy whereby adjustments are triggered at run-time based on the uncertainty in the robots' beliefs related to their task. This semi-autonomous system is demonstrated using a navigation task and evaluated by a user study. Participants navigated a robot in simulation using the proposed system and via classical teleoperation. Results show that our system has a number of advantages over teleoperation with respect to performance, operator workload, usability, and the users' perception of the robot. We also show that despite these advantages, teleoperation may still be a preferable driving mode depending on the mission priorities.}, language = {en} } @inproceedings{UpcroftRidleyOngetal., author = {Upcroft, Ben and Ridley, Matthew and Ong, Lee Ling and Douillard, Bertrand and Kaupp, Tobias and Kumar, Suresh and Bailey, Tim and Ramos, Fabio and Makarenko, Alexei and Brooks, Alex and Sukkarieh, Salah and Durrant-Whyte, Hugh F.}, title = {Multilevel state estimation in an outdoor decentralised sensor network}, series = {10th International Symposium on Experimental Robotics 2006 (ISER'06), Rio de Janeiro, Brazil}, booktitle = {10th International Symposium on Experimental Robotics 2006 (ISER'06), Rio de Janeiro, Brazil}, language = {en} } @inproceedings{WangKumarRamosetal., author = {Wang, X. Rosalind and Kumar, Suresh and Ramos, Fabio and Kaupp, Tobias and Upcroft, Ben and Durrant-Whyte, Hugh F.}, title = {Learning in hyperspectral images using a nonlinear dimensionality reduction technique}, series = {9th International Conference on Information Fusion (Fusion 2006), Florence, Italy}, booktitle = {9th International Conference on Information Fusion (Fusion 2006), Florence, Italy}, language = {en} } @inproceedings{BrooksMakarenkoKauppetal., author = {Brooks, Alex and Makarenko, Alexei and Kaupp, Tobias and Williams, Stefan and Durrant-Whyte, Hugh F.}, title = {Implementation of an indoor active sensor network}, series = {9th International Symposium on Experimental Robotics 2004, Singapore}, booktitle = {9th International Symposium on Experimental Robotics 2004, Singapore}, language = {en} }