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: 1527748
• 负责人: Sivakumar Rathinam
• 金额: $ 24.13万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1527748&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）拒绝环境中部署具有资源限制的无人驾驶车辆（UV）网络所需的新算法。本项目开发的方法将在具有国家重要性的各种应用中发挥重要作用，例如灾害管理、边境监视、民用基础设施（包括石油管道、电网、港口、内河航道和智能交通系统）的监测，其中GPS信号很容易被有意或无意地干扰。提出的研究跨越几个领域，包括控制，估计，传感，机器人和优化。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现提供了丰富的机会。这个项目让少数民族学生参与小型研究项目，以激发他们对工程和科学的兴趣。使无人驾驶汽车具备在无GPS环境中导航的能力，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、减少财产损失和减少生命损失。该方案解决了在gps拒绝环境中部署无人驾驶车辆时出现的以下基本问题：给定一组要访问的车辆和目标，为每辆车辆找到一条路径，使每个目标至少被某些车辆访问一次，每辆车辆在任何时刻的位置估计误差在给定范围内，并且依赖于旅行和传感成本的目标最小。该项目的具体技术目标是：确定在一段时间内使车辆系统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的近似和精确算法来优化问题，并通过大规模和硬件在环模拟和现场演示来实验证实所提出算法的性能。预计该项目将显著提高协作定位车辆团队的可观察性分析领域的技术水平，以及在资源和定位约束下车辆放置和路径规划问题的可处理、近似和精确算法领域的技术水平。新的切割平面、圆整和拉格朗日对偶方法有望为有效分解车辆安置和路径规划问题的困难提供新的见解，并将导致具有近似边界的良好可行解。所提出的大规模仿真和实验结果将为不同参数（地标/车辆/目标数量、可接受位置误差范围、车载传感器类型、不同操作环境和每辆车的速度）对车辆定位/路径规划系统性能的影响提供新的理解。