RI: Small: Collaborative Research: Cooperative Autonomous Vehicle Routing under Resource and Localization Constraints

RI：小型：协作研究：资源和本地化约束下的协作自主车辆路由

• 批准号: 1736087
• 负责人: Rajnikant Sharma
• 金额: $ 21.55万
• 依托单位: University of Cincinnati Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1736087&HistoricalAwards=false
• 关键词: RI; Small; Collaborative; Research; Cooperative

This project aims to develop novel algorithms required to deploy Unmanned Vehicle (UV) networks with resource constraints in Global Positioning System (GPS) denied environments. The methods developed in this project will be useful in a wide variety of applications of national importance such as disaster management, border surveillance, monitoring of civilian infrastructure including oil pipelines, power grids, harbors, inland waterways, and intelligent transportation systems where GPS signals can be easily jammed either intentionally or unintentionally. The proposed research spans several areas including control, estimation, sensing, robotics and optimization. This project provides a rich opportunity for involving undergraduate and graduate students in the development of vehicle platforms, sensor networks, and in the implementation of the control and optimization algorithms. This project engages minority students in small research projects to motivate their interest in engineering and science. Enabling autonomous unmanned vehicles with a capability of navigating in GPS denied environments can aid in effectively monitoring large infrastructure systems, protect their structural integrity and functional reliability as well as provide ecological, societal and economic benefits, including better preservation of natural resources, reduced property damage and reduced loss of life.This proposal addresses the following fundamental problem that arises while deploying unmanned vehicles in GPS-denied environments: Given a set of vehicles and targets to visit, find a path for each vehicle such that each target is visited at least once by some vehicle, the error in the position estimate of each vehicle at any time instant is within a given bound and an objective which depends on the travel and sensing costs is minimized. The specific technical objectives of this project are to: determine the minimal set of requirements that would render the system of vehicles observable over a time period, develop novel approximation and exact algorithms using cutting plane, rounding and Lagrangian dual methods for the optimization problems, and experimentally corroborate the performance of the proposed algorithms using large scale and hardware-in-the-loop simulations, and field demonstrations. It is anticipated that this project will significantly advance the state of art in the area of observability analysis for a team of cooperatively localizing vehicles, and in the area of tractable, approximation and exact algorithms for vehicle placement and path planning problems with resource and localization constraints. Novel cutting plane, rounding, and Lagrangian dual methods are expected to provide new insights into efficient ways of decomposing the difficulties in the vehicle placement and path planning problems, and will lead to good feasible solutions with approximation bounds. The proposed large scale simulation and experimental results will provide a new understanding of the influence of the different parameters (number of landmarks/vehicles/targets, bounds on acceptable position errors, onboard sensor type, different operational environments, and the speed of each vehicle) on the performance of the vehicle localization/path planning system.

该项目旨在开发在全球定位系统（GPS）无法使用的环境中部署具有资源限制的无人驾驶车辆（UV）网络所需的新颖算法。该项目开发的方法将在具有国家重要性的各种应用中发挥作用，例如灾害管理、边境监视、民用基础设施监测，包括石油管道、电网、港口、内陆水道和 GPS 信号可以使用的智能交通系统。很容易有意或无意地被卡住。拟议的研究涵盖控制、估计、传感、机器人和优化等多个领域。该项目为本科生和研究生参与车辆平台、传感器网络的开发以及控制和优化算法的实施提供了丰富的机会。该项目让少数族裔学生参与小型研究项目，以激发他们对工程和科学的兴趣。使自主无人驾驶车辆能够在 GPS 缺失的环境中导航，有助于有效监控大型基础设施系统，保护其结构完整性和功能可靠性，并提供生态、社会和经济效益，包括更好地保护自然资源、减少财产损失该提案解决了在 GPS 无法识别的环境中部署无人驾驶车辆时出现的以下基本问题：给定一组车辆和要访问的目标，为每辆车找到一条路径，使得每个目标至少被访问一次由一些人车辆，每个车辆在任何时刻的位置估计的误差都在给定的范围内，并且取决于行进和传感成本的目标被最小化。该项目的具体技术目标是：确定使车辆系统在一段时间内可观察的最小要求集，使用切割平面、舍入和拉格朗日对偶方法开发新颖的近似和精确算法来解决优化问题，以及使用大规模硬件在环仿真和现场演示来实验证实所提出算法的性能。预计该项目将显着提高协作定位车辆团队的可观测性分析领域的技术水平，以及针对具有资源和定位约束的车辆放置和路径规划问题的易处理、近似和精确算法领域的技术水平。新颖的切割平面、舍入和拉格朗日对偶方法有望为分解车辆放置和路径规划问题中的困难的有效方法提供新的见解，并将产生具有近似边界的良好可行解决方案。所提出的大规模模拟和实验结果将为不同参数（地标/车辆/目标的数量、可接受的位置误差的界限、车载传感器类型、不同的操作环境以及每辆车的速度）的影响提供新的理解。车辆定位/路径规划系统的性能。