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）

基本信息

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

项目摘要

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的探索水平、风险和能量限制找到自主机器人的最佳路线，从而提升智能导航系统的科学和工程价值。高性能机载计算实现了无人机和地面车辆之间实时协作导航的全新计算研究，但也对使用已获取但不完善的系统信息提出了新的挑战。机器人分析图像以进行自动驾驶功能检测，并在不中断驾驶的情况下选择性地收集数据来协助科学家。该项目在旅途中发现信息丰富的路径，并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。了解如何在整个导航过程中学习信息将为机器人规划者提供指南，并通过改进的选择模型为社会带来巨大的好处。这项研究将对部分道路网络中断的紧急情况产生积极影响。未来的自动驾驶汽车将通过考虑能源效率和拥堵之间的权衡来纳入能源效率。该项目引入了一套用于概率信息增益的新技术，通过预测未来到达位置的潜在速度分类，通过延长行驶距离、允许更多时间用于非驾驶活动以及减少所需的太阳能电池面积来提高流动站生产力。火星机器人的路线规划取决于基于轨道图像的可穿越性估计，不同位置的置信水平有所不同。通常，图像的目视检查会揭示有限数量的独特地形单元，其中每个单元内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，同一单元的其他部分的可穿越性的不确定性就会减少。这种推理产生了信息理论路线规划的概念：在任务的早期阶段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，只有当不确定性降低带来的收益超过探索成本时，这种探索行为才是合理的。特别考虑的因素包括 1) 来自单个观察与多个观察的顺序信息增益，2) 多个单变量概率分布中的信息增益与多变量设置的混合，以及 3) 实施具有信息增益的能源感知规划。当机器人穿过网格地图时，可以通过访问未分类或不确定分类的单元、观察这些单元中的状况并估计其他单元中的熵来获取信息。每个代理每次移动到新的网格单元时都会更新其路径计划。通过共享有关网格单元状态的信息，每个代理有助于定义在其他代理的效用函数中使用的最佳参数。如果另一个代理访问了相同的单元，并且发现该单元处于与该类型的原始单元相同的状态，则所有代理都确认这些单元相关的假设更有可能是正确的。地图中的不确定性程度取决于特工访问的时间动态、通过卫星图像获得的信息以及旅行时间的上下限。该奖项反映了 NSF 的法定使命，经评估认为值得支持利用基金会的智力优势和更广泛的影响审查标准。

项目成果

期刊论文数量（16）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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)
影响因子：
0
作者：
Niharika Deshpande;Hyoshin Park;Venktesh Pandey;Gyugeun Yoon
通讯作者：
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

DOI：
发表时间：
2022
期刊：
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022
影响因子：
0
作者：
Anusha Neupane, Venktesh Pandey
通讯作者：
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
影响因子：
0
作者：
Park, Hyoshin;Ono, Masahiro
通讯作者：
Ono, Masahiro

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
影响因子：
0
作者：
Hyoshin Park;Justice Darko;Niharika Deshpande;Venktesh Pandey;Hui Su;M. Ono;Dedrick Barkely;L. Folsom;D. Posselt;Steve Chien
通讯作者：
Hyoshin Park;Justice Darko;Niharika Deshpande;Venktesh Pandey;Hui Su;M. Ono;Dedrick Barkely;L. Folsom;D. Posselt;Steve Chien

Scalable information-theoretic path planning for a rover-helicopter team in uncertain environments