Online Waypoint Recognition of Controlled Agents in Uncertain Environments

Abstract

For multi-robot teams with limited communication, the ability to rapidly recognize the intention of a teammate via its exhibited behavior is key to achieving effective collaboration. While current research on plan and goal recognition provide powerful tools, most of them rely on a high-level abstraction of the environment and of its dynamics. We propose online waypoint recognition (OWR) that incorporates knowledge about the dynamic models into the analysis of the observed agent behavior. Our algorithm takes the form of a Kalman filter and performs recognition of the agent's intended waypoint at high frequency. The approach is robust to uncertainties in dynamics and observations. Moreover, it does not require the agent to reach the next waypoint to perform recognition, which saves valuable time. Our empirical evaluation shows the ability of our proposed algorithm to expedite recognition of both simulated and real-world mobile robots.