A Knowledge-Graph Translation Layer for Mission-Aware Multi-Agent Path Planning in Spatiotemporal Dynamics

TL;DR

This paper presents a Knowledge Graph-based translation layer that bridges high-level mission goals and low-level planning in multi-agent systems, enabling flexible, mission-aware path planning in dynamic environments.

Contribution

It introduces a novel two-plane Knowledge Graph architecture that decouples mission semantics from planning, allowing easy modification of coordinated paths through declarative facts.

Findings

Demonstrated effective coordination of AUVs in Gulf of Mexico

Showed that changing KG facts alters path outcomes

Proved the framework's adaptability and performance

Abstract

The coordination of autonomous agents in dynamic environments is hampered by the semantic gap between high-level mission objectives and low-level planner inputs. To address this, we introduce a framework centered on a Knowledge Graph (KG) that functions as an intelligent translation layer. The KG's two-plane architecture compiles declarative facts into per-agent, mission-aware ``worldviews" and physics-aware traversal rules, decoupling mission semantics from a domain-agnostic planner. This allows complex, coordinated paths to be modified simply by changing facts in the KG. A case study involving Autonomous Underwater Vehicles (AUVs) in the Gulf of Mexico visually demonstrates the end-to-end process and quantitatively proves that different declarative policies produce distinct, high-performing outcomes. This work establishes the KG not merely as a data repository, but as a powerful, stateful orchestrator for creating adaptive and explainable autonomous systems.