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Abstract-Monitoring in large scale environments is a typical mission in cooperative robotics. This task requires the exploration of a huge domain by a generally small number of sensor equipped mobile robots. As time restrictions prohibit an exhaustive global search, a sampling strategy is required that allows an efficient spatial mapping of the environment. This paper proposes an adaptive sampling strategy for efficient simultaneous tracking of multiple concentration levels of an atmospheric plume by a team of cooperating unmanned aerial vehicles (UAVs). The approach combines uncertainty and correlation-based concentration estimates to generate sampling points based on already gathered data. The adaptive generation of sampling locations is coupled to a distributed modelpredictive controller for planning optimal vehicle trajectories under collision and communication constraints. Simulation results demonstrate that connectivity of all involved vehicles can be maintained and an accurate reconstruction of the plume is obtained efficiently.

Abstract-DisCoverage is a distributed strategy for frontierbased multi-robot exploration. The robots coordinate by a partition of the environment, and choose their target points by optimizing a locally decomposable objective function. In [9] DisCoverage for convex regions was proposed. In this work, we extend DisCoverage to support arbitrary non-convex realworld environments with obstacles. Therefore, we introduce a transformation of non-convex environments to robot centric star-shaped domains. This results in a general solution with broader applications for exploration and path planning. Simulations as well as experiments with real robots demonstrate the exploration progress.

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