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

该项目通过根据所需的探索水平、风险和能源限制来寻找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。高性能机载计算使无人机和地面车辆之间的实时合作导航的新的计算研究成为可能，但也提出了新的挑战，在使用获得的，但不完善的系统信息。机器人分析图像以进行自动驾驶特征检测，并通过在不中断驾驶的情况下有选择地收集数据来协助科学家。该项目在旅程中发现信息路径，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导，并在改进选择建模方面为社会提供实质性利益。这项研究将在一些道路网络断开的紧急情况下产生积极影响。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考虑能源效率。该项目引入了一套新的概率信息增益技术，通过预测未来到达位置的潜在速度分类，通过延长旅行距离，允许更多的时间进行非驾驶活动，并减少所需的太阳能电池面积来提高漫游车的生产力。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，该估计在不同位置的置信度不同。通常，图像的视觉检查揭示了有限数量的独特地形单元，其中每个单元内的可通行性可能接近均匀。因此，一旦机器人访问地形单元的一部分并对其进行成像，则减少了同一单元的其他部分的可通行性的不确定性。这种推理产生了信息理论路线规划的概念：在使命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，只有当减少不确定性的收益超过探索的成本时，这种探索行为才是合理的。具体的考虑包括1）来自单个观测相对于多个观测的顺序信息增益，2）多个单变量概率分布中的信息增益相对于多变量设置的混合，以及3）利用信息增益实现能量感知规划。当机器人在网格地图中移动时，可以通过访问未分类或不确定分类的单元格，观察这些单元格中的情况，并估计其他单元格中的熵来获得信息。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，每个代理帮助定义要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真的。地图的不确定性程度取决于代理人访问的时间动态、通过卫星图像获得的信息以及旅行时间的上限和下限。该奖项反映了NSF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。

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.