NRI: FND: COLLAB: Distributed Semantically-Aware Tracking and Planning for Fleets of Robots

在快节奏密集的城市环境中跟踪、预测和推理行人和车辆的能力对于确保自动驾驶汽车能够安全可靠地运行至关重要。该项目的重点是向自动驾驶汽车和无人驾驶飞机车队提供这一能力，使这些自动驾驶系统实现其承诺的社会效益，例如有可能在减少交通拥堵和提高安全性的同时提高人员和货物的流动性。此外，这项技术还可以为其他应用定制，例如大型制造作业，甚至是小型家用机器人应用。这个项目的方法和结果将包括在课程课程和使用的推广计划和活动中。该项目通过几种方式解决这一挑战：(I)将机器视觉的分类算法与来自多目标贝叶斯过滤器的对象的运动跟踪结合到新的过滤体系结构中；(Ii)使用基于Voronoi的分布式覆盖控制工具生成新的在线、分布式细分算法，以利用多机器人团队固有的并行性来动态划分周围环境；(Iii)使用此分区来创建具有带宽高效更新并在面对系统错误和恶意代理时确保数据完整性的分布式内存架构；(Iv)开发语义感知的路径规划算法，以快速、在线地优化机器人运动，其考虑到其他对象的可能反应行为的范围；以及(V)设计基于APP的界面，其促进人类操作员和多机器人团队之间的双向信息交换。基于这些进展的原型系统将在高度仪器化的实验室环境中进行测试和评估。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The ability to track, predict and reason about pedestrians and vehicles in a fast-paced dense urban environment is crucial to ensuring that autonomous vehicles can operate safely and dependably. This project focuses on providing that capability to fleets of autonomous cars and delivery drones, allowing these autonomous systems to realize their promised societal benefits, such as the potential for greater mobility of people and goods while reducing traffic congestion and increasing safety. This technology can moreover be customized for other applications such as large manufacturing operations and even small household robotic applications. The methods and results from this project will be included in course curricula and in used outreach programs and events.The project approaches this challenge in several ways: (i) combine classification algorithms from machine vision with the motion tracking of the objects from multi-target Bayesian filters into a new filtering architecture; (ii) generate new online, distributed tessellation algorithms, using tools from Voronoi-based distributed coverage control, to dynamically partition the surrounding environment in a way that leverages the innate parallelism of teams of multi-robots; (iii) use this partition to create a distributed memory architecture that has bandwidth-efficient updates and ensures data integrity in the face of system errors and malicious agents; (iv) develop semantically-aware path planning algorithms for fast, online optimization of robot motion that account for the range of possible reactionary behaviors of other objects; and (v) design an app-based interface that facilitates two-way information exchange between a human operator and the multi-robot team. A prototype system based on these advances will be tested and evaluated in a highly instrumented laboratory environment.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.