In the context of robotics coverage describes the deployment of mobile sensors or agents in a target area. This paper presents a new solution to coverage in nonconvex environments. We apply a modification to the coverage approach in (Cortes, et.al., 2004), making use of visibility sets and a contraction of the environment, leading to a change in the bounds of integration. The resulting feedback control is fully distributed, has guaranteed, inherent collision avoidance without separate path planning and reduced communication and computation requirements compared to similar approaches. We also formulate an extension to agents with limited sensing range. Our method works in unknown environments, if all regions are of equal interest. We provide simulation results demonstrating the advantageous properties of the approach.

DisCoverage is a distributed strategy for frontier-based 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 real-world 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.

The main aspect in multi-robot exploration is the efficient coordination of a group of robots. Inspired by previous results on the coverage problem, we propose a novel, frontier-based approach for multi-robot exploration. This approach merges the step of choosing appropriate target points with the step of planning a collision-free path. This is achieved by optimizing an objective function consisting of distance and orientation costs as well as an estimated information gain. The optimization yields motion control laws directly solving the exploration task. Using a Voronoi partition of the environment ensures, that each robot autonomously creates and optimizes the objective function to obtain a collision-free path in a distributed fashion. Simulations demonstrate the effectiveness of our approach.