Multi-Robot Coordination for Planning under Context Uncertainty

TL;DR

This paper introduces a two-stage multi-robot planning framework for operating under unknown context, enabling robots to infer context and optimize tasks safely and efficiently.

Contribution

It formalizes the MR-CUSSP problem and proposes CIMOP and LCBS algorithms for context inference and collision-free planning with preferences.

Findings

Algorithms perform well in simulated domains

Framework effectively infers context and plans accordingly

Practical applicability demonstrated with five robots

Abstract

Real-world robots often operate in settings where objective priorities depend on the underlying context of operation. When the underlying context is unknown apriori, multiple robots may have to coordinate to gather informative observations to infer the context, since acting based on an incorrect context can lead to misaligned and unsafe behavior. Once the underlying true context is inferred, the robots optimize their task-specific objectives in the preference order induced by the context. We formalize this problem as a Multi-Robot Context-Uncertain Stochastic Shortest Path (MR-CUSSP), which captures context-relevant information at landmark states through joint observations. Our two-stage solution approach is composed of: (1) CIMOP (Coordinated Inference for Multi-Objective Planning) to compute plans that guide robots toward informative landmarks to efficiently infer the true context, and (2) LCBS (Lexicographic Conflict-Based Search) for collision-free multi-robot path planning with lexicographic objective preferences, induced by the context. We evaluate the algorithms using three simulated domains and demonstrate its practical applicability using five mobile robots in the salp domain setup.