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.

这项研究的目的是开拓新技术的使命规划和控制的机器人系统，获得他们的推进能量完全或主要来自可再生资源。可再生动力机器人系统的例子包括用于远程陆地勘探的风滚草漫游车，用于空中观测的太阳能飞机，以及用于海洋表面勘探的航行无人机。通过依靠可再生资源进行推进，这种系统可以探索由于范围限制而无法通过传统移动的机器人探索的敌对和偏远地区，包括遥远行星的表面，海洋的深层沃茨和北极地区。这项研究工作将集中在创建和验证的基本工具，通过推进资源，在空间和时间上的变化随机控制可再生动力系统，从而需要一套全新的控制工具，相对于传统的移动的机器人。这项研究的理论结果将在一个小型无人驾驶帆船舰队上得到验证，该舰队将部署在内陆沃茨。研究工作将辅之以教育和推广机会，涉及自主海洋系统公司，卡罗莱纳帆船俱乐部和北卡罗来纳州州立大学。传统的移动的机器人系统通常可以由非常有限的范围但相对可预测的移动性来表征，其中机器人系统（或机器人系统组）的可达域可以在任何给定时间以高度的确定性来表征。这项研究从根本上扭转了这种范式，专注于具有无限范围但随机移动性的机器人系统。由于可再生资源的随机时空变化，传统的能量感知控制技术在此类系统上的应用通常会导致无效或极其保守的使命规划策略。为了应对这一挑战，本研究项目将追求一个分层的使命规划和控制框架，其中上层使命规划者规定的推进资源的统计特征的基础上的首选探索方向，和较低级别的动态移动优化将最大限度地提高预期的移动性沿着首选方向，考虑到每个代理和随机资源模型的动态。高斯过程建模将用于表征时空演变的资源。多项式混沌近似和随机响应面方法将用于促进在上层的搜索方向的滚动时域优化，而随机动态规划结果将用于在下层提取概率时间最优航点跟踪算法。理论上的性能极限将在统计遗憾界限的背景下进行分析。使命规划和控制算法将在两种情况下得到验证：（i）在内陆沃茨测试的小型仪表化无人驾驶帆船舰队;（ii）大规模模拟研究，其中无人驾驶帆船的目标是墨西哥湾流资源评估。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

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