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Distance optimal target assignment in robotic networks under communication and sensing constraints

机译:通信和传感约束下机器人网络中距离最佳目标分配

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We study the problem of minimizing the total distance incurred in assigning a group of mobile robots to an equal number of static targets. Assuming that the robots have limited, range-based communication and target-sensing capabilities, we present a necessary and sufficient condition for ensuring distance optimality when robots and targets are uniformly randomly distributed. We then provide an explicit, non-asymptotic formula for computing the number of robots needed for guaranteeing optimality in terms of the robots' sensing and communication capabilities with arbitrarily high probabilities. The bound given in the formula is also asymptotically tight. Due to the large number of robots needed for high-probability optimality guarantee, we continue to investigate strategies for cases in which the number of robots cannot be freely chosen. We show that a properly designed strategy can be asymptotically optimal or suboptimal with constant approximation ratios.
机译:我们研究了最小化将一组移动机器人分配给相等数量的静态目标所引起的总距离的问题。假设机器人具有有限的基于距离的通信和目标感测能力,当机器人和目标均匀地随机分布时,我们提出了确保距离最优的必要和充分条件。然后,我们提供了一个明确的非渐近公式,用于计算具有任意高概率的机器人的感知和通信能力,以保证最优性所需的机器人数量。公式中给出的界限也渐近严格。由于高概率最优性保证需要大量的机器人,因此我们继续研究无法自由选择机器人数量的情况下的策略。我们表明,采用恒定的近似比率,正确设计的策略可以是渐近最优或次优的。

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