@article {45,
	title = {A Frontier-Void-Based Approach for Autonomous Exploration in 3D},
	journal = {Advanced Robotics},
	volume = {27},
	year = {2013},
	pages = {459{\textendash}468},
	abstract = {We consider the problem of an autonomous robot searching for objects in unknown 3d space. Similar to the well known frontier-based exploration in 2d, the problem is to determine a minimal sequence of sensor viewpoints until the entire search space has been explored. We introduce a novel approach that combines the two concepts of voids, which are unexplored volumes in 3d, and frontiers, which are regions on the boundary between voids and explored space. Our approach has been evaluated on a mobile platform using a 5-DOF manipulator searching for victims in a simulated USAR setup. The results indicate the real-world capability and search efficiency of the proposed method.},
	author = {C. Dornhege and A. Kleiner}
}
@conference {42,
	title = {Multi-UAV Motion Planning for Guaranteed Search},
	booktitle = {Proc. of the 12th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2013)},
	year = {2013},
	address = {St. Paul, MN, USA},
	abstract = {We consider the problem of detecting all moving and evading targets in 2.5D environments with teams of UAVs. Targets are assumed to be fast and omniscient while UAVs are only equipped with limited range detection sensors and have no prior knowledge about the location of targets. We present an algorithm that, given an elevation map of the environment, computes synchronized trajectories for the UAVs to guarantee the detection of all targets. The approach is based on coordinating the motion of multiple UAVs on sweep lines to clear the environment from contamination, which represents the possibility of an undetected target being located in an area. The goal is to compute trajectories that minimize the number of UAVs needed to execute the guaranteed search. This is achieved by converting 2D strategies, computed for a polygonal representation of the environment, to 2.5D strategies. We present methods for this conversion and consider cost of motion and visibility constraints. Experimental results demonstrate feasibility and scalability of the approach. Experiments are carried out on real and artificial elevation maps and provide the basis for future deployments of large teams of real UAVs for guaranteed search.},
	keywords = {guaranteed search, multi-robot, multi-UAV, pursuit-evasion},
	isbn = {978-1-4503-1993-5},
	url = {http://dl.acm.org/citation.cfm?id=2484920.2484936},
	author = {A. Kolling and A. Kleiner}
}
@conference {43,
	title = {RMASBench: Benchmarking Dynamic Multi-Agent Coordination in Urban Search and Rescue},
	booktitle = {Proc. of the 12th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2013)},
	year = {2013},
	abstract = {We propose RMASBench, a new benchmarking tool based on the RoboCup Rescue Agent simulation system, to easily compare coordination approaches in a dynamic rescue scenario. In particular, we offer simple interfaces to plug-in coordination algorithms without the need for implementing and tuning low-level agents behaviors. Moreover, we add to the realism of the simulation by providing a large scale crowd simulator, which exploits GPUs parallel architecture, to simulate the behavior of thousands of agents in real time. Finally, we focus on a specific coordination problem where fire fighters must combat fires and prevent them from spreading across the city. We formalize this problem as a Distributed Constraint Optimization Problem and we compare two state-of-the art solution techniques: DSA and MaxSum. We perform an extensive empirical evaluation of such techniques considering several standard measures for performance (e.g. damages to buildings) and coordination overhead (e.g., message exchanged and non concurrent constraint checks). Our results provide interesting insights on limitations and benefits of DSA and MaxSum in our rescue scenario and demonstrate that RMASBench offers powerful tools to compare coordination algorithms in a dynamic environment.},
	keywords = {agent coordination, agent simulation, DCOP, performance metrics},
	isbn = {978-1-4503-1993-5},
	url = {http://dl.acm.org/citation.cfm?id=2484920.2485139},
	author = {A. Kleiner and A. Farinelli and S. Ramchurn and B. Shi and F. Maffioletti and R. Reffato}
}
@article {40,
	title = {RoboCup Rescue Robot and Simulation Leagues},
	journal = {AI Magazine},
	volume = {34},
	year = {2013},
	pages = {78{\textendash}87},
	abstract = {The RoboCup Rescue Robot and Simulation competitions have been held since 2000. The experience gained during these competitions has increased the maturity level of the field, which allowed deploying robots after real disasters (e.g. Fukushima Daiichi nuclear disaster). This article provides an overview of these competitions and highlights the state of the art and the lessons learned.},
	author = {H. L. Akin and N. Ito and A. Jacoff and A. Kleiner and J. Pellenz and A. Visser}
}
@conference {47,
	title = {ARMO: Adaptive Road Map Optimization for Large Robot Teams},
	booktitle = {IROS},
	year = {2011},
	address = {San Fransisco, USA},
	abstract = {Autonomous robot teams that simultaneously dispatch transportation tasks are playing more and more an important role in present logistic centers and manufacturing plants. In this paper we consider the problem of robot motion planning for large robot teams in the industrial domain. We present adaptive road map optimization (ARMO) that is capable of adapting the road map in real time whenever the environment has changed. Based on linear programming, ARMO computes an optimal road map according to current environmental constraints (including human whereabouts) and the current demand for transportation tasks from loading stations in the plant. For detecting dynamic changes, the environment is describe by a grid map augmented with a hidden Markov model (HMM). We show experimentally that ARMO outperforms decoupled planning in terms of computation time and time needed for task completion.},
	isbn = {978-1-61284-454-1},
	doi = {10.1109/IROS.2011.6048339},
	author = {A. Kleiner and D. Sun and D. Meyer-Delius}
}
