Multi-robot collision-free and deadlock-free navigation in cluttered environments with static and dynamic obstacles is a fundamental problem for many applications. We introduce MRNAV, a framework for planning and control to effectively navigate in such environments. Our design utilizes short, medium, and long horizon decision making modules with qualitatively different properties, and defines the responsibilities of them. The decision making modules complement each other and provide the effective navigation capability. MRNAV is the first hierarchical approach combining these three levels of decision making modules and explicitly defining their responsibilities. We implement our design for simulated multi-quadrotor flight. In our evaluations, we show that all three modules are required for effective navigation in diverse situations. We show the long-term executability of our approach in an eight hour long wall time (six hour long simulation time) uninterrupted simulation without collisions or deadlocks.

翻译：在包含静态和动态障碍物的拥挤环境中实现多机器人无碰撞和死锁导航是许多应用的基础性问题。我们提出MRNAV——一个用于在如此环境中有效导航的规划与控制框架。我们的设计利用具有定性差异的短、中、长三种时间尺度决策模块，并明确了各模块的职责。这些决策模块相互补充，提供有效的导航能力。MRNAV是首个结合这三种决策层级并明确其职责的分层方法。我们针对多四旋翼仿真飞行实现了该设计。评估结果表明，所有三个模块对于不同场景下的有效导航均不可或缺。我们通过一项持续八小时物理时间（六小时仿真时间）、未发生任何碰撞或死锁的无中断仿真实验，验证了该方法的长时可行性。