RI: Small: Information-theoretic Multiagent Paths for Anticipatory Control of Tasks (IMPACT)

RI：小：用于任务预期控制的信息论多智能体路径（IMPACT）

• 批准号: 2409731
• 负责人: Hyoshin Park
• 金额: $ 24万
• 依托单位: Old Dominion University Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-11-15 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2409731&HistoricalAwards=false
• 关键词: RI; Small; Information; theoretic; Multiagent

This project promotes the scientific and engineering value of intelligent navigation systems by finding the best routes of autonomous robots based on the desired level of exploration, risk, and energy constraints. High-performance onboard computing enables fundamentally new computational research on cooperative navigation between unmanned aerial and ground vehicles in real-time but also raises new challenges in using acquired, but imperfect information of the system. Robots analyze images for autonomous driving feature detection and assist scientists by selectively collecting data without interrupting drives. This project discovers informative paths during the journey, updated as information about the terrain is discovered. The simulation will provide valuable insights into utilizing the map. Understanding how information can be learned throughout navigation will produce a guide to robotics planners, and offer substantial benefits to society in improved choice modeling. This research will have a positive impact in emergency situations when some of the road networks are disconnected. Future autonomous vehicle driving will incorporate energy efficiency by considering the tradeoff between energy efficiency and congestion. This project introduces a set of novel techniques for probabilistic information gain by predicting potential speed classification of future arrival locations to improve rover productivity by extending travel distance, allowing more time for non-driving activities, and reducing the required solar cell area. Route planning of Mars robots depends on a traversability estimate, based on orbital imagery, which varies in confidence level at different locations. Typically, a visual inspection of images reveals a finite number of distinctive terrain units, where the traversability is likely near-homogeneous within each unit. Therefore, once a robot visits a part of a terrain unit and images it, the uncertainty in traversability of the other parts of the same unit is reduced. This reasoning results in the concept of information-theoretic route planning: visiting high-uncertainty areas at the early stage of a mission to resolve uncertainty (i.e., information gain) and benefit the future route planning. However, such exploratory behavior is justified only when the benefit from uncertainty reduction exceeds the cost of exploration. Particular considerations include 1) a sequential information gain from single observation against multiple observations, 2) a mixture of information gain in multiple univariate probability distributions against the multi-variate setting, and 3) implementation to energy-aware planning with information gain. When a robot travels through a grid map, information can be gained by visiting unclassified or uncertainly classified cells, observing the condition in those cells, and estimating the entropy in other cells. Each agent updates its path plan every time it moves to a new grid cell. By sharing information about the state of the grid cells, each agent helps to define the optimal parameters to be used in other agents' utility functions. If an identical cell is visited by another agent and found to be in the same state as the original cell of that type, then all agents have confirmation that the assumption that these cells are correlated is more likely to be true. The degree of uncertainty in the map depends on the time-dynamics of the agents' visits, information obtained through satellite imagery, and the upper and lower bound of travel times.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.

该项目通过根据期望的探索、风险和能源约束水平找到自主机器人的最佳路线，提升了智能导航系统的科学和工程价值。高性能机载计算从根本上为无人机和地面车辆之间的实时协同导航提供了新的计算研究，但也对系统获取的不完全信息的使用提出了新的挑战。机器人分析图像以进行自动驾驶特征检测，并在不中断驾驶的情况下有选择地收集数据，以协助科学家。这个项目在旅途中发现信息丰富的路径，随着地形信息的发现而更新。模拟将为利用地图提供有价值的见解。了解如何在导航过程中学习信息将为机器人规划者提供指导，并在改进选择建模方面为社会提供实质性的好处。这项研究将在一些道路网络中断的紧急情况下产生积极影响。未来的自动驾驶汽车将考虑能源效率和拥堵之间的权衡，将能源效率纳入其中。该项目引入了一套新的概率信息获取技术，通过预测未来到达地点的潜在速度分类，通过延长行驶距离、允许更多的非驾驶活动时间和减少所需的太阳能电池面积来提高漫游者的生产率。火星机器人的路线规划依赖于基于轨道图像的可穿越性估计，而轨道图像在不同位置的置信度是不同的。通常情况下，对图像进行视觉检查会发现有限数量的独特地形单元，其中每个单元的可穿越性可能接近均匀。因此，一旦机器人访问地形单元的一部分并对其进行成像，则减少了同一单元其他部分可穿越性的不确定性。这种推理产生了信息论路线规划的概念：在任务的早期访问高不确定性区域，以解决不确定性（即信息增益），并有利于未来的路线规划。然而，只有当减少不确定性的收益超过勘探成本时，这种勘探行为才是合理的。特别要考虑的因素包括：1)单次观测对多次观测的顺序信息增益，2)多个单变量概率分布对多变量设置的信息增益混合，以及3)实现具有信息增益的能量感知规划。当机器人在网格地图上移动时，可以通过访问未分类或不确定分类的单元，观察这些单元中的情况，并估计其他单元中的熵来获得信息。每个代理每次移动到一个新的网格单元时都会更新其路径规划。通过共享关于网格单元状态的信息，每个代理都有助于定义在其他代理的效用函数中使用的最佳参数。如果一个相同的细胞被另一个代理访问，并且发现与该类型的原始细胞处于相同的状态，那么所有代理都确认这些细胞相关的假设更有可能是正确的。地图的不确定性程度取决于代理人访问的时间动力学、通过卫星图像获得的信息以及旅行时间的上限和下限。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。