ABSTRACT

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.

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