Multi-Robot Coordination for Planning under Context Uncertainty
This addresses safety and efficiency challenges for multi-robot systems in uncertain real-world environments, though it is incremental as it builds on existing planning methods.
The paper tackles the problem of multi-robot coordination when robots must infer unknown operational contexts to avoid unsafe behavior, proposing a two-stage solution that efficiently guides robots to gather information and then plan paths with context-specific preferences, demonstrating applicability in simulated domains and with five mobile robots.
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.