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Cloud-Enhanced Robotic System for Smart City Crowd Control

机译：智慧城市人群控制的云增强机器人系统

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Cloud robotics in smart cities is an emerging paradigm that enables autonomous robotic agents to communicate and collaborate with a cloud computing infrastructure. It complements the Internet of Things (IoT) by creating an expanded network where robots offload data-intensive computation to the ubiquitous cloud to ensure quality of service (QoS). However, offloading for robots is significantly complex due to their unique characteristics of mobility, skill-learning, data collection, and decision-making capabilities. In this paper, a generic cloud robotics framework is proposed to realize smart city vision while taking into consideration its various complexities. Specifically, we present an integrated framework for a crowd control system where cloud-enhanced robots are deployed to perform necessary tasks. The task offloading is formulated as a constrained optimization problem capable of handling any task flow that can be characterized by a Direct Acyclic Graph (DAG). We consider two scenarios of minimizing energy and time, respectively, and develop a genetic algorithm (GA)-based approach to identify the optimal task offloading decisions. The performance comparison with two benchmarks shows that our GA scheme achieves desired energy and time performance. We also show the adaptability of our algorithm by varying the values for bandwidth and movement. The results suggest their impact on offloading. Finally, we present a multi-task flow optimal path sequence problem that highlights how the robot can plan its task completion via movements that expend the minimum energy. This integrates path planning with offloading for robotics. To the best of our knowledge, this is the first attempt to evaluate cloud-based task offloading for a smart city crowd control system.
机译：智慧城市中的云机器人技术是一种新兴的范例，它使自主机器人代理能够与云计算基础架构进行通信和协作。它通过创建扩展的网络来补充物联网（IoT），在该网络中，机器人将数据密集型计算卸载到无处不在的云中，以确保服务质量（QoS）。但是，由于机器人的机动性，技能学习，数据收集和决策能力的独特特性，其卸载工作非常复杂。在本文中，提出了一个通用的云机器人框架，以在考虑其各种复杂性的同时实现智能城市视觉。具体来说，我们为人群控制系统提供了一个集成框架，其中部署了云增强型机器人来执行必要的任务。任务卸载被公式化为一个约束优化问题，能够处理任何可以由直接非循环图（DAG）表征的任务流。我们分别考虑了最小化能源和时间的两种情况，并开发了一种基于遗传算法（GA）的方法来确定最佳任务卸载决策。与两个基准的性能比较表明，我们的遗传算法实现了所需的能量和时间性能。我们还通过更改带宽和移动的值来展示算法的适应性。结果表明它们对卸载有影响。最后，我们提出了一个多任务流最佳路径序列问题，该问题着重说明了机器人如何通过消耗最小能量的运动来计划其任务完成。这将路径规划与针对机器人的卸载集成在一起。据我们所知，这是评估智能城市人群控制系统基于云的任务卸载的首次尝试。

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