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: 1910397
• 负责人: Hyoshin Park
• 金额: $ 24万
• 依托单位: North Carolina Agricultural & Technical State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910397&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.

该项目通过根据所需探索水平，风险和能量限制的水平找到自主机器人的最佳途径来促进智能导航系统的科学和工程价值。高性能在船上计算可以实时实时关于无人机和地面车辆之间的合作导航的新计算研究，但在使用已获得但不完美的系统信息方面也带来了新的挑战。机器人通过选择性收集数据而无需中断驱动器来分析图像以进行自主驾驶功能检测和协助科学家。该项目在旅途中发现了信息的途径，随着发现地形的信息的更新。模拟将提供有价值的见解，以利用地图。了解如何在整个导航中学习信息将为机器人计划者提供指南，并在改进的选择建模中为社会带来可观的好处。当某些道路网络断开连接时，这项研究将在紧急情况下产生积极的影响。未来的自动驾驶汽车驾驶将通过考虑能源效率和交通拥堵之间的权衡来纳入能源效率。该项目通过预测未来到达位置的潜在速度分类，通过扩展旅行距离来提高流动站的生产率，为非驾驶活动提供更多时间，并减少所需的太阳能电池区域，从而提高流动站的生产率，从而提高了概率信息的增益，从而介绍了一组新型技术。火星机器人的路线规划取决于基于轨道图像的遍历性估计，该图像在不同位置的置信度变化。通常，对图像的视觉检查揭示了有限数量的独特地形单元，在每个单元中，遍历性可能几乎是同质性的。因此，一旦机器人访问了地形单元的一部分并对其进行图像，则减少了同一单元其他部位的遍历性不确定性。这种推理导致信息理论路线计划的概念：在任务的早期阶段访问高度确定性区域，以解决不确定性（即信息收益）并受益于未来的路线计划。但是，只有当减少不确定性的收益超过勘探成本时，这种探索行为才是合理的。特定的考虑因素包括1）从单个观测值对多个观察结果进行的顺序信息获得，2）多个单变量概率分布中信息增益与多变量设置的混合物，以及3）实施信息，以信息增益到能源感知的计划。当机器人通过网格图传播时，可以通过访问未分类或不确定的分类细胞，观察这些细胞中的状况并估算其他细胞中的熵来获得信息。每个代理都会在移动到新的网格单元时更新其路径计划。通过共享有关网格单元状态的信息，每个代理有助于定义其他代理效用函数中要使用的最佳参数。如果另一个试剂访问了一个相同的细胞并发现与该类型的原始单元处于相同的状态，则所有代理都有确认这些细胞相关的假设更可能是正确的。地图中的不确定性程度取决于代理商访问的时间动力学，通过卫星图像获得的信息以及旅行时间的上和下限。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来支持的。

项目成果

Advancing Temporal Multimodal Learning with Physics Informed Regularization

• DOI: 10.1109/ciss56502.2023.10089632
• 发表时间: 2023-03
• 期刊: 2023 57th Annual Conference on Information Sciences and Systems (CISS)
• 作者: Niharika Deshpande;Hyoshin Park;Venktesh Pandey;Gyugeun Yoon

Multimodal Learning Models for Traffic Datasets

交通数据集的多模态学习模型

• 发表时间: 2022
• 期刊: ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022
• 作者: Anusha Neupane, Venktesh Pandey

Sequential Deep Learning for Mars Autonomous Navigation

火星自主导航的序列深度学习

• DOI: 10.1109/scc57168.2023.00011
• 发表时间: 2023
• 期刊: 2023 IEEE Space Computing Conference (SCC
• 作者: Park, Hyoshin;Ono, Masahiro
• 通讯作者: Ono, Masahiro

Physics Informed Temporal Multimodal Multivariate Learning for Short-Term Traffic State Prediction

用于短期交通状态预测的物理信息时态多模态多元学习

• 发表时间: 2023
• 期刊: INFORMS Transportation and Logistics Society Second Triennial Conference
• 作者: Deshpande, Niharika;Darko, Justice;Park, Hyoshin;Yoon, Gyugeun;Pandey, Venktesh
• 通讯作者: Pandey, Venktesh

Temporal Multimodal Multivariate Learning

• DOI: 10.1145/3534678.3539159
• 发表时间: 2022-06
• 期刊: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
• 作者: Hyoshin Park;Justice Darko;Niharika Deshpande;Venktesh Pandey;Hui Su;M. Ono;Dedrick Barkely;L. Folsom;D. Posselt;Steve Chien