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RI: Small: Robotic Path Planning to Reveal Wireless Rays - A New Foundation for the Optimization of Networked Robotic Operations

RI：小型：揭示无线射线的机器人路径规划 - 优化网络机器人操作的新基础

基本信息

批准号：
2008449
负责人：
Yasamin Mostofi
金额：
$ 44.85万
依托单位：
University of California-Santa Barbara
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008449&HistoricalAwards=false
关键词：
RI Small Robotic Path Planning

项目摘要

A robotic network can have a tremendous impact in many different areas such as emergency response, national security, and mobile service provisioning. In such systems, a team of unmanned vehicles are tasked with information gathering and cooperation to accomplish a given mission. Wireless communication is an integral part of such a system as an unmanned vehicle needs to connect to other nodes or to remote operators in order to transfer sensing data and/or receive control commands. Since a robot’s path directly affects its link quality, an unmanned vehicle needs to take the communication quality into account during path planning. This then requires an unmanned vehicle to have an accurate prediction of its link quality at unvisited locations. Accurately predicting the channel quality, however, is a considerably challenging problem and an open one, due to the high spatial variations of the channel power. This project enables the robust cooperation of a team of unmanned vehicles by developing new predictive wireless channel models and its corresponding co-optimization with sensing and path planning, which can significantly impact the area of robotics. The project also has an educational component, leveraging appeals of both robotics and communications, to reach out to the young minds.This project develops a new multi-disciplinary paradigm for the robust networked operation of unmanned vehicles. The first research task shows it possible for the robot to reveal the makeup of the incoming wireless rays, based only on its onboard received channel power measurements. More specifically, it proposes a novel combination of path planning and information processing that can reveal the makeup of the rays, enabling the robot to learn considerably more about the channel than the measured received power. This new approach has a significant implication for robotic field operation as the project addresses. More specifically, the second major task builds on the first one in order to develop a new framework for robotic channel prediction. The third major task then develops a multi-disciplinary design that properly co-optimizes the new predictive channel models with sensing and path planning decisions, in order to enable the robust networked operation of unmanned vehicles. Finally, the overall new design paradigm is tested on a robotic testbed.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

机器人网络可以在许多不同的领域产生巨大的影响，例如应急响应、国家安全和移动服务供应。在这样的系统中，一队无人驾驶车辆负责信息收集和合作，以完成给定的任务。无线通信是这种系统的一个组成部分，因为无人驾驶车辆需要连接到其他节点或远程操作员，以便传输传感数据和/或接收控制命令。由于机器人的路径直接影响其链路质量，因此无人驾驶车辆在进行路径规划时需要考虑通信质量。这就要求无人驾驶车辆对未访问位置的链路质量有准确的预测。然而，由于信道功率的空间变化很大，准确预测信道质量是一个相当具有挑战性和开放性的问题。该项目通过开发新的预测无线信道模型及其与传感和路径规划的相应协同优化，实现无人驾驶车辆团队的强大合作，这将对机器人领域产生重大影响。该项目也有一个教育的组成部分，利用机器人和通信的吸引力，接触到年轻人的思想。该项目为无人驾驶车辆的稳健网络操作开发了一个新的多学科范式。第一个研究任务表明，仅基于机载接收信道功率测量，机器人就有可能揭示入射无线射线的组成。更具体地说，它提出了一种路径规划和信息处理的新组合，可以揭示光线的组成，使机器人能够比测量到的接收功率更多地了解通道。这种新方法对机器人现场作业具有重要意义。更具体地说，第二个主要任务建立在第一个主要任务的基础上，以开发机器人通道预测的新框架。第三个主要任务是开发一种多学科设计，适当地协同优化具有传感和路径规划决策的新预测通道模型，以实现无人驾驶车辆的强大网络化操作。最后，在机器人试验台上对整个新设计范例进行了测试。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。