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Multi-robot collision-free and deadlock-free navigation in clutteredenvironments with static and dynamic obstacles is a fundamental problem formany applications. We introduce MRNAV, a framework for planning and control toeffectively navigate in such environments. Our design utilizes short, medium,and long horizon decision making modules with qualitatively differentproperties, and defines the responsibilities of them. The decision makingmodules complement each other and provide the effective navigation capability.MRNAV is the first hierarchical approach combining these three levels ofdecision making modules and explicitly defining their responsibilities. Weimplement our design for simulated multi-quadrotor flight. In our evaluations,we show that all three modules are required for effective navigation in diversesituations. We show the long-term executability of our approach in an eighthour long wall time (six hour long simulation time) uninterrupted simulationwithout collisions or deadlocks.

MRNAV: Multi-Robot Aware Planning and Control Stack for Collision and Deadlock-free Navigation in Cluttered Environments