Multi-Robot Coordination for Planning under Context Uncertainty

2026-03-14

AI Summary

This work addresses the challenge of task misalignment and unsafe behaviors in multi-robot systems operating in real-world environments with unknown contexts. The authors propose MR-CUSSP, a novel framework that integrates information gathering under context uncertainty with lexicographic multi-objective path planning. In the first phase, the Context-Informed Multi-Objective Planner (CIMOP) coordinates robots to explore information-rich landmarks for accurate context inference. In the second phase, the Lexicographic Conflict-Based Search (LCBS) algorithm generates context-aware, conflict-free paths that respect lexicographic preferences. By combining joint observation modeling with stochastic shortest-path planning, the approach is validated across three simulation domains and a five-robot Salp platform, demonstrating significant improvements in both context inference efficiency and task execution safety.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Multi-Robot Coordination
• Context Uncertainty
• Stochastic Shortest Path
• Informative Observation
• Lexicographic Preferences

Innovation

• Multi-Robot Coordination
• Context Uncertainty
• Lexicographic Planning
• Stochastic Shortest Path
• Joint Observation