Persistent Mission Planning and Control for Renewably Powered Robotic Systems

这项研究的目的是开拓任务规划和控制机器人系统的新技术，这些系统的推进能量要么完全要么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测的风滚草漫游者，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。通过依靠可再生资源推进，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，海洋的深水和北极地区。这项研究工作将集中在通过空间和时间随机变化的推进资源来控制可再生动力系统的基本工具的创建和验证上，因此需要一套相对于传统移动机器人的全新控制工具。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船队上得到验证。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莱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范围，但相对可预测的移动性，其中机器人系统（或机器人系统团队）的可达域可以在任何给定时间具有高度确定性的特征。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机动性的机器人系统。由于可再生资源的随机性、时空变异性，传统的能量感知控制技术在这类系统上的应用通常会导致任务规划策略无效或极度保守。为了应对这一挑战，本研究项目将采用分层任务规划和控制框架，其中上层任务规划人员根据推进资源的统计特征规定优选的探索方向，下层动态机动性优化器将考虑每个智能体的动态和随机资源模型，沿着优选方向最大化预期机动性。高斯过程建模将用于表征时空演化的资源。在上层将使用多项式混沌近似和随机响应面方法来促进搜索方向的后退水平优化，而在下层将使用随机动态规划结果来提取概率时间最优航点跟踪算法。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情况下进行验证：(i)在内陆水域测试的小型仪表航行无人机船队；（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。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.