Abstract
In this paper, we present an approach to automatically allocate a set of exploration tasks between a fleet of mobile robots. Our approach combines a RoadMap technique and Markovian Decision Processes (MDPs).We are interested in the problem of exploring an area where several robots need to visit a set of points of interest. This problem induces a long term horizon motion planning with a combinatorial explosion. The RoadMap allows us to represent spatial knowledge as a graph of paths. It can be modified during the exploration mission requiring the robots to use on-line computation. By decomposing the RoadMap into regions, an MDP allows the leader robot to evaluate the interest of each robot in every single region. Using those values, the leader can assign the exploration tasks to the robots.
Supported by the Nationnal Reserch Agency of France (ANR) through the R-Discover project.
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Lozenguez, G., Adouane, L., Beynier, A., Martinet, P., Mouaddib, AI. (2011). Map Partitioning to Approximate an Exploration Strategy in Mobile Robotics. In: Demazeau, Y., Pěchoucěk, M., Corchado, J.M., Pérez, J.B. (eds) Advances on Practical Applications of Agents and Multiagent Systems. Advances in Intelligent and Soft Computing, vol 88. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19875-5_8
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DOI: https://doi.org/10.1007/978-3-642-19875-5_8
Publisher Name: Springer, Berlin, Heidelberg
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