Persistent Mission Planning and Control for Renewably Powered Robotic Systems

可再生能源机器人系统的持续任务规划和控制

• 批准号: 2012103
• 负责人: Christopher Vermillion
• 金额: $ 36.55万
• 依托单位: North Carolina State University
• 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: 2020-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2012103&HistoricalAwards=false
• 关键词: Persistent; Mission; Planning; Control; Renewably

The objective of this research is to pioneer new techniques for the mission planning and control of robotic systems that derive their propulsive energy either solely or primarily from renewable resources. Examples of renewably powered robotic systems include tumbleweed rovers for remote terrestrial exploration, solar powered aircraft for aerial observation, and sailing drones for oceanographic surface exploration. By relying on renewable resources for propulsion, such systems can explore hostile and remote regions that cannot be explored through traditional mobile robots due to range limitations, including the surfaces of distant planets, deep waters of the ocean, and the Arctic region. This research effort will focus on the creation and validation of fundamental tools for controlling renewably powered systems through a propulsive resource that varies stochastically in space and time, thereby necessitating a fundamentally new set of control tools relative to traditional mobile robots. The theoretical results from the research will be validated on a small fleet of sailing drones, to be deployed in inland waters. The research effort will be complemented with educational and outreach opportunities involving Autonomous Marine Systems, Inc., the Carolina Sailing Club, and North Carolina State University.Traditional mobile robotic systems can typically be characterized by very limited range but relatively predictable mobility, wherein the reachable domain of the robotic system (or team of robotic systems) can be characterized with a high degree of certainty at any given time. This research fundamentally reverses that paradigm, focusing on robotic systems with unlimited range but stochastic mobility. Due to the stochastic, spatiotemporal variation in the renewable resource, the application of traditional energy-aware control techniques on such systems will typically result in either ineffective or extremely conservative mission planning strategies. To address this challenge, this research project will pursue a hierarchical mission planning and control framework in which an upper-level mission planner prescribes preferred exploration directions based on statistical characterizations of the propulsive resource, and lower-level dynamic mobility optimizers will maximize expected mobility along preferred directions, taking into account the dynamics of each agent and the stochastic resource model. Gaussian Process modeling will be used to characterize the spatiotemporally evolving resource. Polynomial chaos approximations and stochastic response surface methods will be used to facilitate a receding horizon optimization of search directions at the upper level, whereas stochastic dynamic programming results will be used to extract probabilistically time-optimal waypoint following algorithms at the lower level. Theoretical performance limits will be analyzed in the context of statistical regret bounds. Mission planning and control algorithms will be validated in two settings: (i) a small fleet of instrumented sailing drones to be tested in inland waters and (ii) a larger-scale simulation study wherein the goal of the sailing drones is Gulf Stream resource assessment.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.

这项研究的目的是为机器人系统的任务计划和控制提供新技术，这些技术仅来自或主要来自可再生资源。更新动力的机器人系统的示例包括用于远程陆地勘探的滚滚滚动漫游者，太阳能飞机用于航空观察以及用于海洋表面勘探的帆船无人机。通过依靠可再生资源的推进资源，由于范围限制，这些系统可以通过传统的移动机器人探索敌对和远程区域，包括遥远的行星，海洋深水和北极地区。这项研究工作将集中于创建和验证基本工具，以通过在空间和时间上随机变化的推进资源来控制更新动力的系统，从而需要相对于传统移动机器人提供根本上新的控制工具。该研究的理论结果将在一小架帆船无人机舰队中进行验证，该帆船将在内陆水域部署。研究工作将与涉及自动海洋系统，Inc.，卡罗来纳州航行俱乐部和北卡罗来纳州立大学的教育和外展机会补充。传统的移动机器人系统通常可以以非常有限的范围但相对可预测的范围来表征，但相对可预测的范围，其中机器人系统的可及时（或机器人系统的触手可及的范围）都可以在任何一定程度上都具有一定程度的特征。这项研究从根本上扭转了范式，重点是具有无限范围但随机迁移率的机器人系统。由于可再生资源的随机，时空变化，传统能源感知控制技术在此类系统上的应用通常会导致无效或非常保守的任务计划策略。为了应对这一挑战，该研究项目将追求一个等级任务计划和控制框架，在该框架中，高级任务计划者根据推进资源的统计特征规定了首选的勘探方向，而下层动态移动性优化器将最大程度地沿着优选方向提高预期的移动性。高斯工艺建模将用于表征时空发展的资源。多项式混乱近似值和随机响应表面方法将用于促进上层搜索方向的恢复范围优化，而随机动态编程结果将用于提取在较低级别的算法之后概率地提取概率的时间优势。理论绩效限制将在统计遗憾范围的背景下进行分析。任务计划和控制算法将在两种情况下进行验证：（i）在内陆水域进行测试的一小批帆船无人机和（ii）一项较大的模拟研究，其中帆船无人机的目标是海湾资源评估的目标。这是NSF的法定任务，反映了值得通过的Intelliqu and Intelliqu and Infectiac and Infectiac and Infectiac and Infectiac and Infectiac and Infectiac and Infectiac and Intelliqu and Infectiac and Infectial。

项目成果

Coverage-Maximizing Solar-Powered Autonomous Surface Vehicle Control for Persistent Gulf Stream Observation

覆盖范围最大化的太阳能自主地面车辆控制，用于持续的湾流观测

• DOI: 10.23919/acc53348.2022.9867746
• 发表时间: 2022
• 期刊: American Control Conference
• 作者: Govindarajan, Kavin;Haydon, Ben;Mishra, Kirti;Vermillion, Chris
• 通讯作者: Vermillion, Chris

Dynamic Coverage Meets Regret: Unifying Two Control Performance Measures for Mobile Agents in Spatiotemporally Varying Environments

动态覆盖遇到遗憾：在时空变化的环境中统一移动代理的两种控制性能测量

• DOI: 10.1109/cdc45484.2021.9682826
• 发表时间: 2021
• 期刊: IEEE Conference on Decision and Control
• 作者: Haydon, Ben;Mishra, Kirti D.;Keyantuo, Patrick;Panagou, Dimitra;Chow, Fotini;Moura, Scott;Vermillion, Chris
• 通讯作者: Vermillion, Chris