@inproceedings{BrooksMakarenkoKauppetal., author = {Brooks, Alex and Makarenko, Alexei and Kaupp, Tobias and Durrant-Whyte, Hugh and Dellaert, Frank}, title = {Decentralised Data Fusion with Dynamic Topologies-A Graphical Model Approach}, series = {IFAC Proceedings Volumes}, volume = {42}, booktitle = {IFAC Proceedings Volumes}, number = {20}, pages = {222 -- 227}, language = {en} } @inproceedings{KauppMakarenko, author = {Kaupp, Tobias and Makarenko, Alexei}, title = {Decision-theoretic human-robot communication}, series = {Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction}, booktitle = {Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction}, doi = {https://doi.org/10.1145/1349822.1349835}, pages = {89 -- 96}, abstract = {Humans and robots need to exchange information if the objective is to achieve a task cooperatively. Two questions are considered in this paper: what type of information to communicate, and how to cope with the limited resources of human operators. Decision-theoretic human-robot communication can provide answers to both questions: the type of information is determined by the underlying probabilistic representation, and value-of-information theory helps decide when it is appropriate to query operators for information. A robot navigation task is used to evaluate the system by comparing it to conventional teleoperation. The results of a user study show that the developed system is superior with respect to performance, operator workload, and usability.}, language = {en} } @inproceedings{DouillardUpcroftKauppetal., author = {Douillard, Bertrand and Upcroft, Ben and Kaupp, Tobias and Ramos, Fabio and Durrant-Whyte, Hugh}, title = {Bayesian filtering over compressed appearance states}, series = {Proceedings of the Australasian Conference on Robotics and Automation (ACRA)}, booktitle = {Proceedings of the Australasian Conference on Robotics and Automation (ACRA)}, abstract = {This paper presents a framework for performing real-time recursive estimation of landmarks' visual appearance. Imaging data in its original high dimensional space is probabilistically mapped to a compressed low dimensional space through the definition of likelihood functions. The likelihoods are subsequently fused with prior information using a Bayesian update. This process produces a probabilistic estimate of the low dimensional representation of the landmark visual appearance. The overall filtering provides information complementary to the conventional position estimates which is used to enhance data association. In addition to robotics observations, the filter integrates human observations in the appearance estimates. The appearance tracks as computed by the filter allow landmark classification. The set of labels involved in the classification task is thought of as an observation space where human observations are made by selecting a label. The low dimensional appearance estimates returned by the filter allow for low cost communication in low bandwidth sensor networks. Deployment of the filter in such a network is demonstrated in an outdoor mapping application involving a human operator, a ground and an air vehicle.}, language = {en} } @inproceedings{MakarenkoKauppGrocholskyetal., author = {Makarenko, Alexei and Kaupp, Tobias and Grocholsky, Ben and Durrant-Whyte, Hugh}, title = {Human-robot interactions in active sensor networks}, series = {Proceedings 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium (Cat. No. 03EX694)}, volume = {1}, booktitle = {Proceedings 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium (Cat. No. 03EX694)}, doi = {10.1109/CIRA.2003.1222097}, pages = {247 -- 252}, abstract = {This paper considers the modes of interaction between one or several human operators and an active sensor network-a fully decentralized network of sensors some or all of which have actuators and are in that sense active. The primary goal of this study is to investigate the conditions under which the human involvement will not jeopardize scalability of the overall system. Two aspects of human-robot interaction are considered: the ways in which the global view of the system may be conveyed to the operators, and how the operators may influence the behavior of the system during the course of its operation. The results of analysis favor peer-to-peer environment-centric interactions between the operators and the network whereby the humans act as extended sensors and communication nodes of the network itself. Experiments on an indoor active sensor network are described.}, language = {en} } @inproceedings{KauppBrooksUpcroftetal., author = {Kaupp, Tobias and Brooks, Alex and Upcroft, Ben and Makarenko, Alexei}, title = {Building a software architecture for a human-robot team using the orca framework}, series = {Proceedings 2007 IEEE International Conference on Robotics and Automation}, booktitle = {Proceedings 2007 IEEE International Conference on Robotics and Automation}, publisher = {IEEE}, doi = {10.1109/ROBOT.2007.364051}, pages = {3736 -- 3741}, abstract = {This paper considers the problem of building a software architecture for a human-robot team. The objective of the team is to build a multi-attribute map of the world by performing information fusion. A decentralized approach to information fusion is adopted to achieve the system properties of scalability and survivability. Decentralization imposes constraints on the design of the architecture and its implementation. We show how a component-based software engineering approach can address these constraints. The architecture is implemented using Orca - a component-based software framework for robotic systems. Experimental results from a deployed system comprised of an unmanned air vehicle, a ground vehicle, and two human operators are presented. A section on the lessons learned is included which may be applicable to other distributed systems with complex algorithms. We also compare Orca to the player software framework in the context of distributed systems.}, 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} } @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}, 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} } @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{MakarenkoBrooksKaupp, author = {Makarenko, Alexei and Brooks, Alex and Kaupp, Tobias}, title = {Orca: Components for robotics}, series = {International Conference on Intelligent Robots and Systems (IROS) - Workshop for Robotic Standardization at IROS'06}, booktitle = {International Conference on Intelligent Robots and Systems (IROS) - Workshop for Robotic Standardization at IROS'06}, pages = {163 -- 168}, abstract = {This paper describes Orca: an open-source software project which applies Component-Based Software Engineering principles to robotics. The paper focuses on the technical aspects of the framework which set it apart from other similar efforts. Among them are the adaption of a commercial middleware package, minimalist approach to framework design, and a commitment to multi-language multi-platform support. The paper explains our decision to use Ice -- a relatively recent entry in the field of general-purpose middleware packages.}, language = {en} } @inproceedings{MakarenkoBrooksKaupp, author = {Makarenko, Alexei and Brooks, Alex and Kaupp, Tobias}, title = {On the benefits of making robotic software frameworks thin}, series = {International Conference on Intelligent Robots and Systems - Workshop for Measures and Procedures for the Evaluation of Robot Architectures and Middleware at IROS'07}, volume = {2}, booktitle = {International Conference on Intelligent Robots and Systems - Workshop for Measures and Procedures for the Evaluation of Robot Architectures and Middleware at IROS'07}, abstract = {Today's reusable robotics software is provided by several self-contained open-source projects with virtually no software reuse between them. Such partitioning leads to problems with software quantity, quality, ease of evaluation and, ultimately, to poor end user experience. By reviewing several of the projects we observe that all of them contain a mix of three types of software: 1) driver and algorithm implementations, 2) communication middleware, and 3) robotic software framework. We show that more than half of the combined code base contains software which could be highly reusable but only a small fraction of it actually is. We argue that formal separation of the three groups in the existing and future software projects would offer several potential advantages. Availability of framework-independent code would enable community-wide library-based software reuse in addition to the existing framework-wide component- based reuse. Another important benefit is related to evaluation procedures. The three software types are very different and should be evaluated separately, using different criteria. The first two types allow quantitative comparisons which are well documented in the literature. The last one is largely qualitative and, therefore, more subjective. Practically, we recommend today's projects to refactor and eject the driver and algorithm implementation code and focus on the task of software integration. Thin frameworks can be numerous, increasing the diversity of options available to robotics practitioners. Finally, we discuss distribution options for the refactored framework-independent code including the option of creating a Common Robotic Project.}, language = {en} } @inproceedings{SpiessFriesslichBluemmetal., author = {Spieß, Florian and Friesslich, Jonas and Bluemm, Daniel and Mast, Fabio and Vinokour, Dmitrij and Kounev, Samuel and Kaupp, Tobias and Strobel, Norbert}, title = {Towards a Mobile Robot Localization Benchmark with Challenging Sensordata in an Industrial Environment}, series = {2021 20th International Conference on Advanced Robotics (ICAR)}, booktitle = {2021 20th International Conference on Advanced Robotics (ICAR)}, doi = {10.1109/ICAR53236.2021.9659355}, pages = {857 -- 864}, abstract = {To arrive at a realistic assessment of localization methods in terms of their performance in an industrial environment under various challenging conditions, we provide a benchmark to evaluate algorithms both for individual components as well as multi-sensor systems. For several sensor types, including wheel odometry, RGB cameras, RGB-D cameras, and LIDAR, potential issues were identified. The accuracy of wheel odometry, for example, when there are bumps on the track. For each sensor type, we explicitly chose a track for the benchmark dataset containing situations where the sensor fails to provide adequate measurements. Based on the acquired sensor data, localization can be achieved either using a single sensor information or sensor fusion. To help evaluate the output of associated localization algorithms, we provide a software to evaluate a set of metrics as part of the paper. An example application of the benchmark with state-of-the-art algorithms for each sensor is also provided.}, language = {en} } @inproceedings{KauppMakarenko, author = {Kaupp, Tobias and Makarenko, Alexei}, title = {Measuring human-robot team effectiveness to determine an appropriate autonomy level}, series = {2008 IEEE International Conference on Robotics and Automation}, booktitle = {2008 IEEE International Conference on Robotics and Automation}, publisher = {IEEE}, doi = {10.1109/ROBOT.2008.4543524}, pages = {2146 -- 2151}, abstract = {This paper proposes a methodology to measure the effectiveness of a human-robot team as part of an adjustable autonomy system. The effectiveness measure is aimed at determining an appropriate autonomy level prior to the system's deployment. Two competing goals need to be traded off: maximising robot performance while minimising the amount of human input. The relative importance of the two goals depend on the mission priorities and constraints which are taken into account. The proposed methodology is applied to a human-robot communication system developed for task- oriented information exchange. The robot uses a decision- theoretic framework to act autonomously and to decide when to request input from human operators. The latter is achieved by computing the value-of-information an operator is able to provide which is compared to the cost of obtaining the information. For our system, the cost parameter represents the autonomy level to be determined. We demonstrate how an appropriate autonomy level can be found experimentally using a navigation task. In our experiment, the robot navigates through a set of simulated worlds with human input being generated by a software component. The results are used to find appropriate autonomy levels for three example missions and a subsequent user study.}, language = {en} } @inproceedings{WangKumarKauppetal., author = {Wang, X. Rosalind and Kumar, Suresh and Kaupp, Tobias and Upcroft, Ben and Durrant-Whyte, Hugh}, title = {Applying ISOMAP to the learning of hyperspectral image}, series = {Australian Conference on Robotics and Automation (ACRA'05)}, booktitle = {Australian Conference on Robotics and Automation (ACRA'05)}, editor = {Sammut, C.}, pages = {1 -- 8}, abstract = {In this paper, we present the application of a non-linear dimensionality reduction technique for the learning and probabilistic classification of hyperspectral image. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. It gives much greater information content per pixel on the image than a normal colour image. This should greatly help with the autonomous identification of natural and manmade objects in unfamiliar terrains for robotic vehicles. However, the large information content of such data makes interpretation of hyperspectral images time-consuming and userintensive. We propose the use of Isomap, a non-linear manifold learning technique combined with Expectation Maximisation in graphical probabilistic models for learning and classification. Isomap is used to find the underlying manifold of the training data. This low dimensional representation of the hyperspectral data facilitates the learning of a Gaussian Mixture Model representation, whose joint probability distributions can be calculated offline. The learnt model is then applied to the hyperspectral image at runtime and data classification can be performed.}, language = {en} } @inproceedings{KauppMakarenkoRamosetal., author = {Kaupp, Tobias and Makarenko, Alexei and Ramos, Fabio and Upcroft, Ben and Williams, Stefan and Durrant-Whyte, Hugh}, title = {Adaptive human sensor model in sensor networks}, series = {2005 7th International Conference on Information Fusion}, volume = {1}, booktitle = {2005 7th International Conference on Information Fusion}, publisher = {IEEE}, doi = {10.1109/ICIF.2005.1591929}, abstract = {This paper presents the design of a probabilistic model of human perception as an integral part of a decentralized data fusion system. The system consists of a team of human operators and robotic platforms, together forming a heterogeneous sensor network. Human operators are regarded as information sources submitting raw observations. The observations are converted into a probabilistic representation suitable for fusion with the system's belief. The conversion is performed by a human sensor model (HSM). The initial HSM is built offline based on an average of multiple human subjects conducting a calibration experiment. Since individual human operators may vary in their performance, an online adaptation of the HSM is required. The network estimate is used for adaptation because the true feature state is unknown at runtime. Results of an outdoor calibration experiment using range and bearing observations are presented. Simulations show the feasibility of efficient online adaptation.}, language = {en} } @inproceedings{KauppDouillardUpcroftetal., author = {Kaupp, Tobias and Douillard, Bertrand and Upcroft, Ben and Makarenko, Alexei}, title = {Hierarchical environment model for fusing information from human operators and robots}, series = {2006 IEEE/RSJ International Conference on Intelligent Robots and Systems}, booktitle = {2006 IEEE/RSJ International Conference on Intelligent Robots and Systems}, publisher = {IEEE}, doi = {10.1109/IROS.2006.282398}, pages = {5837 -- 5842}, abstract = {This paper considers the problem of building environment models by fusing information gathered by robotic platforms with human perceptual information. Rich environment models are required in real applications for both autonomous operation of robots and to support human decision making. Hierarchical models are well suited to represent complex environments because they: offer multiple abstractions of the available information to support analysis and decision-making, and permit the incorporation of higher-level human observations. The contributions of this paper are two-fold: (1) development of a probabilistic three-level environment model for distributed information gathering, and (2) experimental demonstration of fully decentralized, cooperative human-robot information gathering using an outdoor sensor network comprised of an unmanned air vehicle, a ground vehicle, and two human operators. Several information exchange patterns are presented which qualitatively demonstrate human-robot information fusion}, language = {en} } @inproceedings{BrooksKauppMakarenko, author = {Brooks, Alex and Kaupp, Tobias and Makarenko, Alexei}, title = {Randomised MPC-based motion-planning for mobile robot obstacle avoidance}, series = {2009 IEEE International Conference on Robotics and Automation}, booktitle = {2009 IEEE International Conference on Robotics and Automation}, doi = {10.1109/ROBOT.2009.5152240}, pages = {3962 -- 3967}, abstract = {This paper presents an algorithm for real-time sensor-based motion planning under kinodynamic constraints, in unknown environments. The objective of the trajectory-generation algorithm is to optimise a cost function out to a limited time horizon. The space of control trajectories is searched by expanding a tree using randomised sampling, in a manner similar to an RRT. The algorithm is improved by seeding the tree using the best control trajectory from the previous iteration, and by pruning branches based on a bound to the cost function and the best trajectory found so far. Performance of the algorithm is analysed in simulation. In addition, the algorithm has been implemented on two kinds of vehicles: the Segway RMP and a four-wheel-drive. The algorithm has been used to drive autonomously for a combined total on the order of hundreds of hours.}, language = {en} } @inproceedings{KauppMakarenkoKumaretal., author = {Kaupp, Tobias and Makarenko, Alexei and Kumar, Suresh and Upcroft, Ben and Williams, Stefan}, title = {Operators as information sources in sensor networks}, series = {2005 IEEE/RSJ International Conference on Intelligent Robots and Systems}, booktitle = {2005 IEEE/RSJ International Conference on Intelligent Robots and Systems}, publisher = {IEEE}, doi = {10.1109/IROS.2005.1545015}, pages = {936 -- 941}, abstract = {This paper presents an approach of integrating human operators into a sensor network formed by a heterogeneous team of unmanned air and ground vehicles. Several objectives of human-network interaction are identified. The main focus of this work is on human-to-network information flow, i.e. human operators are regarded as information sources. It is argued that operators should make raw observations which are converted into the sensor network's common representation by a probabilistic model. The concepts are discussed in the context of an outdoor sensor network under development. Human operators contribute geometric feature information in the form of range and bearing observations. Visual feature properties are specified via meaningful class labels. A sensor model, represented as a Bayesian network, translates label observations into the system's representation. The model is also used to classify features as observed by robotic sensors.}, language = {en} } @inproceedings{BrooksKauppMakarenkoetal., author = {Brooks, Alex and Kaupp, Tobias and Makarenko, Alexei and Williams, Stefan and Oreback, Anders}, title = {Towards component-based robotics}, series = {2005 IEEE/RSJ International Conference on Intelligent Robots and Systems}, booktitle = {2005 IEEE/RSJ International Conference on Intelligent Robots and Systems}, pages = {163 -- 168}, abstract = {This paper gives an overview of component-based software engineering (CBSE), motivates its application to the field of mobile robotics, and proposes a particular component model. CBSE is an approach to system-building that aims to shift the emphasis from programming to composing systems from a mixture of off-the-shelf and custom-built software components. This paper argues that robotics is particularly well-suited for and in need of component-based ideas. Furthermore, now is the right time for their introduction. The paper introduces Orca - an open-source component-based software engineering framework proposed for mobile robotics with an associated repository of free, reusable components for building mobile robotic systems.}, language = {en} } @article{KauppDouillardRamosetal., author = {Kaupp, Tobias and Douillard, Bertrand and Ramos, Fabio and Makarenko, Alexei and Upcroft, Ben}, title = {Shared environment representation for a human-robot team performing information fusion}, series = {Journal of Field Robotics: Special Issue on Teamwork}, volume = {24}, journal = {Journal of Field Robotics: Special Issue on Teamwork}, number = {11-12}, doi = {10.1002/rob.20201}, pages = {911 -- 942}, abstract = {This paper addresses the problem of building a shared environment representation by a human-robot team. Rich environment models are required in real applications for both autonomous operation of robots and to support human decision-making. Two probabilistic models are used to describe outdoor environment features such as trees: geometric (position in the world) and visual. The visual representation is used to improve data association and to classify features. Both models are able to incorporate observations from robotic platforms and human operators. Physically, humans and robots form a heterogeneous sensor network. In our experiments, the human-robot team consists of an unmanned air vehicle, a ground vehicle, and two human operators. They are deployed for an information gathering task and perform information fusion cooperatively. All aspects of the system including the fusion algorithms are fully decentralized. Experimental results are presented in form of the acquired multi-attribute feature map, information exchange patterns demonstrating human-robot information fusion, and quantitative model evaluation. Learned lessons from deploying the system in the field are also presented. © 2007 Wiley Periodicals, Inc.}, language = {en} } @inproceedings{DurrantWhyteSukkariehRamosetal., author = {Durrant-Whyte, Hugh and Sukkarieh, Salah and Ramos, Fabio and Bailey, Tim and Kumar, Suresh and Ong, Lee-Ling and Ridley, Matthew and Kaupp, Tobias and Douillard, Bertrand and Upcroft, Ben}, title = {Non-gaussian state estimation in an outdoor decentralised sensor network}, series = {Proceedings of the 45th IEEE Conference on Decision and Control}, booktitle = {Proceedings of the 45th IEEE Conference on Decision and Control}, publisher = {IEEE}, address = {Piscataway}, doi = {10.1109/CDC.2006.377533}, pages = {366 -- 372}, abstract = {This paper presents the development and demonstration of non-Gaussian, decentralised state estimation using an outdoor sensor network consisting of an autonomous air vehicle, a manual ground vehicle, and two human operators. The location and appearance of landmarks were estimated using bearing only observations from monocular cameras. We show that inclusion of visual and identity information aids validation gating for data association when geometric information alone cannot discriminate individual landmarks. The combination of geometric, appearance, and identity information provided a common description (or map) of natural features for each of the nodes in the network. We also show the final map from the live demonstration which includes position estimates and classification labels of the observed features.}, language = {en} } @article{MakarenkoKauppDurrantWhyte, author = {Makarenko, Alexei A. and Kaupp, Tobias and Durrant-Whyte, Hugh F.}, title = {Scalable human-robot interactions in active sensor networks}, series = {IEEE Pervasive Computing}, volume = {2}, journal = {IEEE Pervasive Computing}, number = {4}, doi = {10.1109/MPRV.2003.1251170}, pages = {63 -- 71}, abstract = {Decentralized sensor networks promise virtually unlimited scalability and can tolerate individual component failures. Interaction with human operators plays an important role in the overall system design. The authors describe an experimental active sensor network and identify modes of human-robot interaction that keep up with the network's arbitrary growth.}, language = {en} } @article{SpiessFriesslichKauppetal., author = {Spiess, Florian and Friesslich, Jonas and Kaupp, Tobias and Kounev, Samuel and Strobel, Norbert}, title = {Survey and Experimental Comparison of RGB-D Indoor Robot Navigation Methods Supported by ROS and Their Expansion via Fusion with Wheel Odometry and IMU Data}, series = {International Journal of Mechanical Engineering and Robotics Research}, volume = {9}, journal = {International Journal of Mechanical Engineering and Robotics Research}, number = {12}, doi = {10.18178/ijmerr.9.12.1532-1540}, pages = {1532 -- 1540}, 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} } @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} }