TY - CHAP A1 - Brooks, Alex A1 - Makarenko, Alexei A1 - Kaupp, Tobias A1 - Williams, Stefan A1 - Durrant-Whyte, Hugh T1 - Implementation of an indoor active sensor network T2 - Experimental Robotics IX: The 9th International Symposium on Experimental Robotics ; Springer Tracts in Advanced Robotics (STAR,volume 21) N2 - This paper describes an indoor Active Sensor Network, focussing on the implementation aspects of the system, including communication and the application framework. To make the system description more tangible we describe the latest in a series of indoor experiments implemented using ASN. The task is to detect and map motion of people (and robots) in an office space using a network of 12 stationary sensors. The network was operational for several days, with individual platform coming on and off line. On several occasions the network consisted of 39 components. The paper includes a section on the lessons learned during the project’s design and development which may be applicable to other heterogeneous distributed systems with data-intensive algorithms. Y1 - 2006 SN - 978-3-540-28816-9 U6 - https://doi.org/10.1007/11552246_38 SP - 397 EP - 406 PB - Springer CY - Berlin, Heidelberg ER - TY - CHAP A1 - Brooks, Alex A1 - Kaupp, Tobias A1 - Makarenko, Alexei A1 - Williams, Stefan A1 - Orebäck, Anders ED - Brugali, Davide T1 - Orca: A component model and repository T2 - Software engineering for experimental robotics ; Springer Tracts in Advanced Robotics (STAR,volume 30) N2 - This Chapter describes Orca: an open-source project which applies Component-Based Software Engineering principles to robotics. It provides the means for defining and implementing interfaces such that components developed independently are likely to be inter-operable. In addition it provides a repository of free re-useable components. Orca attempts to be widely applicable by imposing minimal design constraints. This Chapter describes lessons learned while using Orca and steps taken to improve the framework based on those lessons. Improvements revolve around middleware issues and the problems encountered while scaling to larger distributed systems. Results are presented from systems that were implemented. Y1 - 2007 SN - 978-3-540-68949-2 U6 - https://doi.org/10.1007/978-3-540-68951-5_13 SP - 231 EP - 251 PB - Springer CY - Berlin, Heidelberg ER - TY - CHAP A1 - Grocholsky, Ben A1 - Makarenko, Alexei A1 - Kaupp, Tobias A1 - Durrant-Whyte, Hugh F. ED - Zhao, Feng ED - Guibas, Leonidas T1 - Scalable control of decentralised sensor platforms T2 - Information Processing in Sensor Networks ; Lecture Notes in Computer Science (LNCS, Volume 2634) N2 - 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. Y1 - 2003 SN - 978-3-540-02111-7 SN - 978-3-540-36978-3 U6 - https://doi.org/10.1007/3-540-36978-3_7 SP - 96 EP - 112 PB - Springer CY - Berlin, Heidelberg ER - TY - CHAP A1 - Miller, Eddi A1 - Schmitt, Anna-Maria A1 - Kaupp, Tobias A1 - Batres, Rafael A1 - Schiffler, Andreas A1 - Schmitt, Jan T1 - A Peak Shaving Approach in Manufacturing Combining Machine Learning and Job Shop Scheduling T2 - Lecture Notes in Mechanical Engineering N2 - Computerized Numerical Control (CNC) plays an important role in highly autonomous manufacturing systems with multiple machine tools. The necessary Numerical Control (NC) programs to manufacture the parts are mostly written in standardized G-code. An a priori evaluation of the energy demand of CNC-based machine processes opens up the possibility of scheduling multiple jobs according to balanced energy consumption over a production period. Due to this, we present a combined Machine Learning (ML) and Job-Shop-Scheduling (JSS) approach to evaluate G-code for a CNC-milling process with respect to the energy demand of each G-command. The ML model training data are derived by the Latin hypercube sampling (LHS) method facing the main G-code operations G00, G01, and G02. The resulting energy demand for each job enhances a JSS algorithm to smooth the energy demand for multiple jobs, as peak power consumption needs to be avoided due to its expense. Y1 - 2025 SN - 9783031774287 U6 - https://doi.org/10.1007/978-3-031-77429-4_59 SN - 2195-4356 SP - 535 EP - 543 PB - Springer Nature Switzerland CY - Cham ER - TY - CHAP A1 - Mueller, Julian A1 - Endres, Felix A1 - Reinhart, Lucas A1 - Loeser, Martin A1 - Buinitskii, Stanislav A1 - Willert, Volker A1 - Kaupp, Tobias T1 - Team SWOT - Champions of RoboCup@Work 2024 T2 - Lecture Notes in Computer Science Y1 - 2025 SN - 9783031858581 U6 - https://doi.org/https://doi.org/10.1007/978-3-031-85859-8_36 SN - 0302-9743 SP - 424 EP - 435 PB - Springer Nature Switzerland CY - Cham ER -