Abstract. Exploring an unknown environment with multiple robots requires an efficient coordination method to minimize the total duration. A standard method to discover new areas is to assign frontiers (boundaries between unexplored and explored accessible areas) to robots. In this context, the frontier allocation method is paramount. This paper introduces a decentralized and computationally efficient frontier allocation method favoring a well balanced spatial distribution of robots in the environment. For this purpose, each robot evaluates its relative rank among the other robots in term of travel distance to each frontier. Accordingly, robots are allocated to the frontier for which it has the lowest rank. To evaluate this criteria, a wavefront propagation is computed from each frontier giving an interesting alternative to path planning from robot to frontiers. Comparisons with existing approaches in computerized simulation and on real robots demonstrated the validity and efficiency of our algorithm.
For its own safety, a robot system should never find itself in a state where there is no feasible trajectory to avoid collision with an obstacle. Such a state is an Inevitable Collision State (ICS). The ICS concept is particularly useful for navigation in dynamic environments because it takes into account the future behaviour of the moving objects. Accordingly it requires a model of the future evolution of the environment. In the real-world, the future trajectories of the obstacles are generally unknown and only estimates are available. This paper introduces a probabilistic formulation of the ICS concept which incorporates uncertainty in the model of the future trajectories of the obstacles. It also presents two novel probabilistic ICSchecking algorithms that are compared with their deterministic counterpart.
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