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创机器人系统任务规划和控制的新技术，这些机器人系统的推进能量单独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探索的风滚草漫游车、用于空中观测的太阳能飞机以及用于海洋表面探索的无人机。通过依靠可再生资源进行推进，此类系统可以探索传统移动机器人因范围限制而无法探索的敌对和偏远地区，包括遥远行星的表面、海洋深水和北极地区。这项研究工作将侧重于创建和验证通过在空间和时间上随机变化的推进资源来控制可再生能源系统的基本工具，从而需要一套相对于传统移动机器人而言全新的控制工具。该研究的理论结果将在部署在内陆水域的小型航行无人机上得到验证。这项研究工作将得到包括自主海洋系统公司、卡罗莱纳州帆船俱乐部和北卡罗来纳州立大学在内的教育和推广机会的补充。传统的移动机器人系统通常具有范围非常有限但相对可预测的移动性的特点，其中可到达的范围机器人系统（或机器人系统团队）的领域可以在任何给定时间以高度确定性进行表征。这项研究从根本上扭转了这一范式，重点关注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，在此类系统上应用传统的能源感知控制技术通常会导致任务规划策略无效或极其保守。为了应对这一挑战，该研究项目将采用分层任务规划和控制框架，其中上层任务规划器根据推进资源的统计特征规定首选探索方向，而下层动态机动性优化器将最大限度地提高预期机动性首选方向，考虑到每个代理的动态和随机资源模型。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上层搜索方向的后退地平线优化，而随机动态规划结果将用于提取下层算法的概率时间最优路径点。理论性能限制将在统计遗憾界限的背景下进行分析。任务规划和控制算法将在两种环境下进行验证：(i) 在内陆水域测试小型仪表航海无人机机队，以及 (ii) 更大规模的模拟研究，其中航海无人机的目标是湾流资源评估该奖项反映了 NSF 的法定使命，并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，被认为值得支持。

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: 2022-06
• 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: 2021-12
• 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