Communication- and Computation-Efficient Distributed Submodular Optimization in Robot Mesh Networks

IF 9.4 1区 计算机科学 Q1 ROBOTICS
Zirui Xu;Sandilya Sai Garimella;Vasileios Tzoumas
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Abstract

In this article, we provide a communication- and computation-efficient method for distributed submodular optimization in robot mesh networks. Submodularity is a property of diminishing returns that arises in active information gathering such as mapping, surveillance, and target tracking. Our method, resource-aware distributed greedy (RAG), introduces a new distributed optimization paradigm that enables scalable and near-optimal action coordination. To this end, RAG requires each robot to make decisions based only on information received from and about their neighbors. In contrast, the current paradigms allow the relay of information about all robots across the network. As a result, RAG’s decision-time scales linearly with the network size, while state-of-the-art near-optimal submodular optimization algorithms scale cubically. We also characterize how the designed mesh-network topology affects RAG’s approximation performance. Our analysis implies that sparser networks favor scalability without proportionally compromising approximation performance: while RAG’s decision-time scales linearly with network size, the gain in approximation performance scales sublinearly. We demonstrate RAG’s performance in simulated scenarios of area detection with up to 45 robots, simulating realistic robot-to-robot (r2r) communication speeds such as the 0.25 Mb/s speed of the Digi XBee 3 Zigbee 3.0. In the simulations, RAG enables real-time planning, up to three orders of magnitude faster than competitive near-optimal algorithms, while also achieving superior mean coverage performance. To enable the simulations, we extend the high-fidelity and photo-realistic simulator AirSim by integrating a scalable collaborative autonomy pipeline to tens of robots and simulating r2r communication delays.
机器人Mesh网络中通信和计算效率高的分布式子模块优化
在本文中,我们为机器人网状网络中的分布式子模块优化提供了一种通信和计算效率高的方法。子模块化是一种收益递减的特性,它出现在主动信息收集中,如制图、监视和目标跟踪。我们的方法,资源感知分布式贪婪(RAG),引入了一种新的分布式优化范例,使可扩展和接近最优的动作协调成为可能。为此,RAG要求每个机器人仅根据从邻居那里接收到的信息和关于邻居的信息做出决策。相比之下,当前的范例允许在整个网络中传递关于所有机器人的信息。因此,RAG的决策时间随网络规模呈线性增长,而最优的次模优化算法则呈三次增长。我们还描述了所设计的网格网络拓扑如何影响RAG的近似性能。我们的分析表明,稀疏网络有利于可扩展性,而不会按比例损害近似性能:虽然RAG的决策时间随网络大小线性扩展,但近似性能的增益是次线性扩展的。我们在多达45个机器人的区域检测模拟场景中演示了RAG的性能,模拟了真实的机器人对机器人(r2r)通信速度,例如Digi XBee 3 Zigbee 3.0的0.25 Mb/s速度。在模拟中,RAG实现了实时规划,比竞争对手的近最优算法快三个数量级,同时也实现了优越的平均覆盖性能。为了实现仿真,我们通过将可扩展的协作自治管道集成到数十个机器人并模拟r2r通信延迟,扩展了高保真度和逼真的模拟器AirSim。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Robotics
IEEE Transactions on Robotics 工程技术-机器人学
CiteScore
14.90
自引率
5.10%
发文量
259
审稿时长
6.0 months
期刊介绍: The IEEE Transactions on Robotics (T-RO) is dedicated to publishing fundamental papers covering all facets of robotics, drawing on interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, and beyond. From industrial applications to service and personal assistants, surgical operations to space, underwater, and remote exploration, robots and intelligent machines play pivotal roles across various domains, including entertainment, safety, search and rescue, military applications, agriculture, and intelligent vehicles. Special emphasis is placed on intelligent machines and systems designed for unstructured environments, where a significant portion of the environment remains unknown and beyond direct sensing or control.
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