@article{SchifferFerreinLakemeyer2012,
  author    = {Schiffer, Stefan and Ferrein, Alexander and Lakemeyer, Gerhard},
  title     = {Caesar: an intelligent domestic service robot},
  series = {Intelligent service robotics},
  volume    = {5},
  journal   = {Intelligent service robotics},
  number    = {4},
  publisher = {Springer},
  address   = {Berlin},
  issn      = {1861-2776},
  doi       = {10.1007/s11370-012-0118-y},
  pages     = {259 -- 276},
  year      = {2012},
  abstract  = {In this paper we present CAESAR, an intelligent domestic service robot. In domestic settings for service robots complex tasks have to be accomplished. Those tasks benefit from deliberation, from robust action execution and from flexible methods for human-robot interaction that account for qualitative notions used in natural language as well as human fallibility. Our robot CAESAR deploys AI techniques on several levels of its system architecture. On the low-level side, system modules for localization or navigation make, for instance, use of path-planning methods, heuristic search, and Bayesian filters. For face recognition and human-machine interaction, random trees and well-known methods from natural language processing are deployed. For deliberation, we use the robot programming and plan language READYLOG, which was developed for the high-level control of agents and robots; it allows combining programming the behaviour using planning to find a course of action. READYLOG is a variant of the robot programming language Golog. We extended READYLOG to be able to cope with qualitative notions of space frequently used by humans, such as "near" and "far". This facilitates human-robot interaction by bridging the gap between human natural language and the numerical values needed by the robot. Further, we use READYLOG to increase the flexible interpretation of human commands with decision-theoretic planning. We give an overview of the different methods deployed in CAESAR and show the applicability of a system equipped with these AI techniques in domestic service robotics},
  language  = {en}
}
@article{SchifferFerreinLakemeyer2015,
  author    = {Schiffer, Stefan and Ferrein, Alexander and Lakemeyer, Gerhard},
  title     = {Abstracting Away Low-Level Details in Service Robotics with Fuzzy Fluents},
  series = {Model-Driven Knowledge Engineering for Improved Software Modularity in Robotics and Automation. Workshop at European Robotics Forum 2015 Vienna, Austria, March 11-13, 2015.},
  journal   = {Model-Driven Knowledge Engineering for Improved Software Modularity in Robotics and Automation. Workshop at European Robotics Forum 2015 Vienna, Austria, March 11-13, 2015.},
  pages     = {1 -- 4},
  year      = {2015},
  language  = {en}
}
@article{SchulteTiggesFoersterNikolovskietal.2022,
  author    = {Schulte-Tigges, Joschua and F{\"o}rster, Marco and Nikolovski, Gjorgji and Reke, Michael and Ferrein, Alexander and Kaszner, Daniel and Matheis, Dominik and Walter, Thomas},
  title     = {Benchmarking of various LiDAR sensors for use in self-driving vehicles in real-world environments},
  series = {Sensors},
  volume    = {22},
  journal   = {Sensors},
  number    = {19},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {1424-8220},
  doi       = {10.3390/s22197146},
  pages     = {20 Seiten},
  year      = {2022},
  abstract  = {Abstract In this paper, we report on our benchmark results of the LiDAR sensors Livox Horizon, Robosense M1, Blickfeld Cube, Blickfeld Cube Range, Velodyne Velarray H800, and Innoviz Pro. The idea was to test the sensors in different typical scenarios that were defined with real-world use cases in mind, in order to find a sensor that meet the requirements of self-driving vehicles. For this, we defined static and dynamic benchmark scenarios. In the static scenarios, both LiDAR and the detection target do not move during the measurement. In dynamic scenarios, the LiDAR sensor was mounted on the vehicle which was driving toward the detection target. We tested all mentioned LiDAR sensors in both scenarios, show the results regarding the detection accuracy of the targets, and discuss their usefulness for deployment in self-driving cars.},
  language  = {en}
}
@article{SteinbauerFerrein2016,
  author    = {Steinbauer, Gerald and Ferrein, Alexander},
  title     = {20 Years of RoboCup},
  series = {KI - K{\"u}nstliche Intelligenz},
  volume    = {30},
  journal   = {KI - K{\"u}nstliche Intelligenz},
  number    = {3-4},
  publisher = {Springer},
  address   = {Berlin},
  issn      = {1610-1987},
  doi       = {10.1007/s13218-016-0442-z},
  pages     = {221 -- 224},
  year      = {2016},
  language  = {en}
}
@article{VorstFerreinLakemeyer2006,
  author    = {Vorst, Phillip and Ferrein, Alexander and Lakemeyer, Gerhard},
  title     = {AllemaniACs3D team description},
  pages     = {1 -- 6},
  year      = {2006},
  language  = {en}
}