Persistent Mission Planning and Control for Renewably Powered Robotic Systems

这项研究的目的是为机器人系统的任务规划和控制开创新的技术，这些系统的推进能量完全或主要来自可再生资源。可再生能源机器人系统的例子包括用于远程陆地探测的风滚草漫游车，用于空中观测的太阳能飞机，以及用于海洋表面探测的航行无人机。通过依赖可再生资源的推进，这类系统可以探索由于射程限制而无法通过传统移动机器人探索的恶劣和偏远地区，包括遥远星球的表面、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，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，从而需要一套相对于传统移动机器人的全新控制工具。这项研究的理论结果将在一支小型航海无人机舰队上得到验证，这些无人机将部署在内陆水域。这项研究工作将与自主海洋系统公司、卡罗莱纳帆船俱乐部和北卡罗来纳州立大学的教育和推广机会相辅相成。传统的移动机器人系统通常具有非常有限的射程但相对可预测的移动性的特点，其中机器人系统(或机器人系统团队)的可达领域在任何给定的时间都可以具有高度确定性的特征。这项研究从根本上颠覆了这一范式，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，在这类系统上应用传统的能量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，本研究项目将追求一种分层的任务规划和控制框架，其中上层任务规划者根据推进资源的统计特征指定优选的探索方向，而底层的动态流动性优化器将考虑每个智能体的动态和随机资源模型，沿优选方向最大化预期流动性。将使用高斯过程建模来表征时空演化的资源。多项式混沌逼近和随机响应面方法将用于上层搜索方向的滚动优化，而随机动态规划结果将用于提取较低层的概率时间最优路线跟踪算法。理论性能极限将在统计后悔界限的背景下进行分析。任务规划和控制算法将在两个环境中得到验证：(I)将在内陆水域测试的小型仪表式航行无人机机队，以及(Ii)更大规模的模拟研究，其中航行无人机的目标是墨西哥湾流资源评估。这一奖项反映了NSF的法定任务，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.