Optimizing multi-robot communication under bandwidth constraints
Optimizing multi-robot communication under bandwidth constraints
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DOI：
10.1007/s10514-019-09849-0
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发表时间：
2020-01-01
影响因子：
3.5
通讯作者：
Olson, Edwin
中科院分区：
文献类型：
--
作者：
Marcotte, Ryan J.;Wang, Xipeng;Olson, Edwin
协同工作的机器人可以与其他机器人分享观察结果，以提高团队绩效，但通信带宽有限。认识到这一点，代理必须决定传达哪些观察结果才能最好地服务于团队。准确估计单个通信的价值是昂贵的；找到一个最优的观察组合来放入信息是很难的。本文提出了一种在带宽约束下优化通信的OCBC算法。OCBC使用前向模拟来评估通信，并应用基于强盗的组合优化算法来选择消息中包含的内容。我们评估了OCBC在模拟多机器人导航任务中的性能。我们表明，OCBC比最先进的方法实现了更好的任务性能，同时通信减少了一个数量级。
Robots working collaboratively can share observations with others to improve team performance, but communication bandwidth is limited. Recognizing this, an agent must decide which observations to communicate to best serve the team. Accurately estimating the value of a single communication is expensive; finding an optimal combination of observations to put in the message is intractable. In this paper, we present OCBC, an algorithm for Optimizing Communication under Bandwidth Constraints. OCBC uses forward simulation to evaluate communications and applies a bandit-based combinatorial optimization algorithm to select what to include in a message. We evaluate OCBC's performance in a simulated multi-robot navigation task. We show that OCBC achieves better task performance than a state-of-the-art method while communicating up to an order of magnitude less.