Online Resynthesis of High-Level Collaborative Tasks for Robots With Changing Capabilities

Given a collaborative high-level task and a team of heterogeneous robots with behaviors to satisfy it, this work focuses on the challenge of automatically adjusting the individual robot behaviors at runtime such that the task is still satisfied. We specifically address scenarios when robots encounter changes to their abilities–either failures or additional actions they can perform. We aim to minimize global teaming reassignments (and as a result, local resynthesis) when robots' capabilities change. The tasks are encoded in LTL$^\psi$, an extension of LTL introduced in our prior work. We increase the expressivity of LTL$^\psi$ by including additional types of constraints on the overall teaming assignment that the user can specify, such as the minimum number of robots required for each assignment. We demonstrate the framework in a simulated warehouse scenario.