@conference {38,
	title = {Fast task-sequence allocation for heterogeneous robot teams with a human in the loop},
	booktitle = {Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems {(IROS)},
	year = {2013},
	abstract = {Efficient task allocation with timing constraints to a team of possibly heterogeneous robots is a challenging problem with application, e.g., in search and rescue. In this paper a mixed-integer linear programming (MILP) approach is proposed for assigning heterogeneous robot teams to the simultaneous completion of sequences of tasks with specific requirements such as completion deadlines. For this purpose our approach efficiently combines the strength of state of the art Mixed Integer Linear Programming (MILP) solvers with human expertise in mission scheduling. We experimentally show that simple and intuitive inputs by a human user have substantial impact on both computation time and quality of the solution. The presented approach can in principle be applied to quite general missions for robot teams with human supervision.},
	keywords = {computation time, Computational modeling, fast task sequence allocation, heterogeneous robot teams, heterogeneous robots, human in the loop, human supervision, human user, integer programming, linear programming, Mathematical model, MILP solvers, mission scheduling, mixed integer linear programming, Multi-robot systems, Resource management, Robot kinematics, Schedules, task allocation},
	doi = {10.1109/IROS.2013.6696570},
	author = {Petersen, K. and Kleiner, A. and Von Stryk, O.}
}
@article {48,
	title = {Hierarchical Visibility for Guaranteed Search in Large-Scale Outdoor Terrain},
	journal = {JAAMAS},
	volume = {26},
	year = {2013},
	pages = {1{\textendash}36},
	abstract = {Searching for moving targets in large environments is a challenging task that is relevant in several problem domains, such as capturing an invader in a camp, guarding security facilities, and searching for victims in large-scale search and rescue scenarios. The guaranteed search problem is to coordinate the search of a team of agents to guarantee the discovery of all targets. In this paper we present a self-contained solution to this problem in 2.5D real-world domains represented by digital elevation models (DEMs). We introduce hierarchical sampling on DEMs for selecting heuristically the close to minimal set of locations from which the entire surface of the DEM can be guarded. Locations are utilized to form a search graph on which search strategies for mobile agents are computed. For these strategies schedules are derived which include agent paths that are directly executable in the terrain. Presented experimental results demonstrate the performance of the method. The practical feasibility of our approach has been validated during a field experiment at the Gascola robot training site where teams of humans equipped with iPads successfully searched for adversarial and omniscient evaders. The field demonstration is the largest-scale implementation of a guaranteed search algorithm to date.},
	keywords = {Exploration, guaranteed search, HRI, Human{\textendash}robot-interaction, Moving target search, path planning, pursuit-evasion, task allocation},
	issn = {1387-2532},
	doi = {10.1007/s10458-011-9180-7},
	url = {http://dx.doi.org/10.1007/s10458-011-9180-7},
	author = {Kleiner, A. and Kolling, A. and Lewis, M. and Sycara, K.}
}
