The optimism principle: A unified framework for optimal robotic network deployment in an unknown obstructed environment
The optimism principle: A unified framework for optimal robotic network deployment in an unknown obstructed environment
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乐观原则：在未知障碍环境中优化机器人网络部署的统一框架
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Nora Ayanian
中科院分区：
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作者：
Shangxing Wang;B. Krishnamachari;Nora Ayanian
我们考虑在一个未知的、受阻的环境中部署一组机器人来形成一个多跳通信网络的问题。作为解决方案，我们提出了一个统一的框架，在线机器人网络形成（LEONA），它足以在非凸环境中为不同的实用函数优化通信网络。LEONA采用“面对不确定性的乐观”原则，使机器人团队能够高效快速地形成最佳网络配置，而无需绘制整个区域的链路质量。我们在固定端点之间形成多跳通信路径的两种特定场景中演示并评估了该框架：一种是最小化总路径成本，另一种是最大化瓶颈通信速率。我们基于模拟的评估表明，在网络优化之前，使用乐观原则可以显著减少在探索和绘制整个区域所花费的资源。我们还提出了在每种情况下搜索区域如何使用各种相关参数的数学模型。
We consider the problem of deploying a team of robots in an unknown, obstructed environment to form a multi-hop communication network. As a solution, we present a unified framework, onLinE rObotic Network formAtion (LEONA), that is general enough to permit optimizing the communication network for different utility functions in non-convex environments. LEONA adopts the principle of “optimism in the face of uncertainty” to allow the team of robots to form optimal network configurations efficiently and rapidly without having to map link qualities in the entire area. We demonstrate and evaluate this framework on two specific scenarios concerning the formation of a multi-hop communication path between fixed end-points: one minimizing the total path cost, and another maximizing the bottleneck communication rate. Our simulation-based evaluation shows that the use of the optimism principle can significantly reduce resources spent in exploring and mapping the entire region prior to network optimization. We also present a mathematical modeling of how the searched area scales with various relevant parameters in each case.