arXiv 2003.138132020-03cs.ROcs.MA

移动机器人网络分布式计算中间件--可插拔分布式资源分配器（PDRA）

The Pluggable Distributed Resource Allocator (PDRA): a Middleware for Distributed Computing in Mobile Robotic Networks

Jet Propulsion Laboratory（喷气推进实验室）；California Institute of Technology（加州理工学院）

Federico Rossi, Tiago Stegun Vaquero, Marc Sanchez Net, Maíra Saboia da Silva, Joshua Vander Hook

备注：: Extended version of manuscript presented at IROS 2020. In v2, numerical results are updated and parts of the paper are rewritten and expanded for clarity. In v3, a minor author metadata error is fixed. All code is available under Apache license at https://github.com/nasa/mosaic

摘要：

我们提出了可插拔分布式资源分配器（PDRA），异构移动机器人网络中的分布式计算的中间件。PDRA使自主机器人代理能够共享计算资源，用于计算昂贵的任务，如定位和路径规划。它位于现有的单代理计划器/执行器和现有的计算资源（例如ROS包）之间，拦截执行器的请求，如果需要，透明地将它们路由到其他机器人执行。PDRA是可插拔的：它可以集成到现有的单机器人自治堆栈中，只需进行最小的修改。任务分配决策由混合整数规划算法执行，以共享世界的方式解决，该算法对CPU资源，延迟要求和多跳，周期性，带宽有限的网络通信进行建模;该算法可以最大限度地减少整体能源使用或最大限度地提高完成可选任务的奖励。仿真结果表明，PDRA可以减少能源和CPU的使用率超过50%，在代表性的多机器人场景相比，一个天真的调度器;运行在嵌入式平台上;并表现良好的延迟和中断容忍网络（DTNs）。PDRA在开源许可证下向社区提供。

英文摘要：

We present the Pluggable Distributed Resource Allocator (PDRA), a middleware for distributed computing in heterogeneous mobile robotic networks. PDRA enables autonomous robotic agents to share computational resources for computationally expensive tasks such as localization and path planning. It sits between an existing single-agent planner/executor and existing computational resources (e.g. ROS packages), intercepts the executor's requests and, if needed, transparently routes them to other robots for execution. PDRA is pluggable: it can be integrated in an existing single-robot autonomy stack with minimal modifications. Task allocation decisions are performed by a mixed-integer programming algorithm, solved in a shared-world fashion, that models CPU resources, latency requirements, and multi-hop, periodic, bandwidth-limited network communications; the algorithm can minimize overall energy usage or maximize the reward for completing optional tasks. Simulation results show that PDRA can reduce energy and CPU usage by over 50% in representative multi-robot scenarios compared to a naive scheduler; runs on embedded platforms; and performs well in delay- and disruption-tolerant networks (DTNs). PDRA is available to the community under an open-source license.

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