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

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约束的无人机（UV）网络所需的新算法。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，如灾害管理、边境监视、民用基础设施监测，包括输油管道、电网、港口、内陆水道和智能运输系统，在这些系统中，GPS信号很容易被有意或无意地干扰。拟议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施提供了丰富的机会。该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。使自主无人驾驶车辆能够在GPS拒绝的环境中导航，可以帮助有效地监测大型基础设施系统，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，减少财产损失和生命损失。该建议解决了在部署无人驾驶飞行器时出现的以下基本问题，拒绝GPS的环境：给定一组要访问的车辆和目标，为每个车辆找到一条路径，使得每个目标至少被某个车辆访问一次，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，并且取决于行程和感测成本的目标最小化。该项目的具体技术目标是：确定最低限度的要求，使系统的车辆可观察的一段时间内，开发新的近似和精确的算法，使用切割平面，舍入和拉格朗日对偶方法的优化问题，并通过实验证实性能的拟议算法，使用大规模和硬件在环仿真，和现场演示。预计该项目将显着推进在可观测性分析领域的合作定位车辆的团队，并在该地区的易于处理的，近似和精确的算法与资源和定位约束的车辆布局和路径规划问题的最先进的。新的切割平面，舍入，拉格朗日对偶方法，预计将提供新的见解，有效的方法分解的困难，在车辆放置和路径规划问题，并会导致良好的可行的解决方案与近似界。建议的大规模仿真和实验结果将提供一个新的理解不同的参数（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，和每辆车的速度）对车辆定位/路径规划系统的性能的影响。

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.