论文标题
可插入的分布式资源分配器（PDRA）：用于移动机器人网络中分布式计算的中间件
The Pluggable Distributed Resource Allocator (PDRA): a Middleware for Distributed Computing in Mobile Robotic Networks
论文作者
论文摘要
我们介绍可插入的分布式资源分配器（PDRA），这是一种用于异质移动机器人网络中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，例如本地化和路径计划。它位于现有的单代理计划者/执行程序和现有的计算资源（例如ROS软件包）之间，拦截了执行者的请求，并且（如果需要）将它们透明地路由到其他机器人进行执行。 PDRA是可插入的：它可以集成到具有最小修改的现有单机器人自主堆栈中。任务分配决策是由以共享世界方式解决的混合组合编程算法执行的，该算法对CPU资源，延迟需求和多跳，周期性，带宽限制网络通信进行建模；该算法可以最大程度地减少整体能源使用情况或最大程度地完成完成可选任务的奖励。仿真结果表明，与天真的调度程序相比，PDRA可以将能源和CPU使用量减少超过50％。在嵌入式平台上运行；并在延迟和破坏耐受性网络（DTN）中表现良好。 PDRA可根据开源许可向社区提供。
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.
