Collaborative Research: Interaction-aware Planning and Control for Robotic Navigation in the Crowd

协作研究：人群中机器人导航的交互感知规划和控制

• 批准号: 2211548
• 负责人: David Fridovich-Keil
• 金额: $ 44.7万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2211548&HistoricalAwards=false
• 关键词: Collaborative; Research; Interaction; aware; Planning

This project aims to enable robot navigation in crowded, dynamic environments such as urban streets and busy walkways. For example, consider several small ground delivery robots which must navigate to specific goal positions while avoiding multiple pedestrians. Currently, decision-making algorithms follow a "predict then plan" approach, in which robots predict the future motion of agents in a scene and subsequently plan avoidance maneuvers. In reality, however, each agent's current decision affects the future observations and decision problems faced by others. This coupling of optimal planning through time is naturally expressed in the formalism of dynamic game theory; unfortunately, however, practical and efficient solution methods for general dynamic games have long been elusive. This project develops theoretical and algorithmic techniques to address some of the underlying challenges, and will also support cross-institution mentoring of multiple PhD students, development of undergraduate course material, and outreach to local underrepresented communities.The specific goals of this project are threefold. The first goal is algorithmic, and aims to construct new algorithms to find approximate equilibrium solutions in several common classes of dynamic games which model distinct modes of human-robot interaction. As these algorithms solve robotic navigation problems, they must also be amenable to embedded, onboard implementation. The second goal of this project addresses the "inverse" problem: optimal planning in a crowd depends upon foreknowledge of humans' objectives. Whereas existing techniques infer agents' objectives in isolation, this project aims to derive novel methods for the strategically-coupled setting. The third and final goal is to accelerate interaction-aware planning in multi-robot, crowd scenarios via computational parallelization and decentralization. The algorithms will be extensively evaluated with human subjects in the setting of crowd navigation, using quadcopters and ground mobile robots.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).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.

该项目旨在使机器人能够在拥挤、动态的环境（例如城市街道和繁忙的人行道）中进行导航。例如，考虑几个小型地面运输机器人，它们必须导航到特定的目标位置，同时避开多个行人。目前，决策算法遵循“预测然后计划”的方法，其中机器人预测场景中代理的未来运动，并随后计划回避动作。然而，实际上，每个智能体当前的决策都会影响其他智能体未来面临的观察和决策问题。这种最优规划随时间的耦合自然地以动态博弈论的形式表达出来。然而不幸的是，长期以来，针对一般动态博弈的实用且高效的解决方法一直难以实现。该项目开发理论和算法技术来解决一些潜在的挑战，还将支持多名博士生的跨机构指导、本科课程材料的开发以及对当地代表性不足的社区的推广。该项目的具体目标有三个。第一个目标是算法，旨在构建新的算法，以在几个常见的动态游戏类别中找到近似均衡解，这些动态游戏模拟了人机交互的不同模式。由于这些算法解决了机器人导航问题，因此它们还必须适合嵌入式机载实现。该项目的第二个目标解决了“逆”问题：人群中的最佳规划取决于对人类目标的预知。尽管现有技术孤立地推断智能体的目标，但该项目旨在为战略耦合环境导出新方法。第三个也是最后一个目标是通过计算并行化和去中心化来加速多机器人、人群场景中的交互感知规划。这些算法将在人群导航环境中使用四轴飞行器和地面移动机器人对人类受试者进行广泛评估。该项目得到了机器人学跨部门基础研究项目的支持，由工程理事会 (ENG) 共同管理和资助该奖项反映了 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。