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: 2423131
• 负责人: Negar Mehr
• 金额: $ 44.56万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2423131&HistoricalAwards=false
• 关键词: Collaborative; Research; Interaction; aware; Planning; 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.

该项目旨在在拥挤，动态的环境（例如城市街道和繁忙的人行道）中导致机器人导航。例如，考虑几个小的地面交付机器人，必须导航到特定的目标位置，同时避免多个行人。目前，决策算法遵循一种“预测计划”方法，在这种方法中，机器人预测了场景中代理的未来运动，并随后计划回避动作。但是，实际上，每个代理人的当前决定都会影响他人面临的未来观察和决策问题。动态游戏理论的形式主义自然表达了最佳计划的这种结合。但是，不幸的是，通用动态游戏的实用和高效解决方案方法长期以来难以捉摸。该项目开发了理论和算法技术，以应对一些基本挑战，还将支持多个博士生的跨机构指导，开发本科课程的材料以及向当地代表性不足的社区推广。该项目的具体目标是三倍。第一个目标是算法，旨在构建新的算法，以在几种常见的动态游戏类别中找到近似平衡解决方案，以建模人类机器人相互作用的不同模式。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板上实施。该项目的第二个目标解决了“反向”问题：人群中的最佳计划取决于人类目标的预见。尽管现有技术孤立地推断了代理的目标，但该项目旨在为战略耦合设置提供新的方法。第三个也是最终目标是通过计算并行化和权力下放来加速多机器人的互动感知计划。该算法将在人群导航的环境中，使用四方和地面移动机器人进行广泛评估。该项目得到了机器人计划中的跨领域基础研究的支持，并通过工程和信息科学和工程（CISE）进行了反映的统计，并由NESF进行了评估，并与之共同管理和资助。基金会的智力优点和更广泛的影响评论标准。