Knowledge Graph-Based Multi-Agent Path Planning in Dynamic Environments using WAITR

TL;DR

This paper introduces WAITR, a knowledge graph-based multi-agent path planning framework that enhances data collection efficiency and safety in dynamic environments by balancing immediate and long-term objectives.

Contribution

We propose WAITR, a novel path-planning approach combining knowledge graphs and pathlet segmentation for adaptive, coordinated multi-agent navigation in uncertain environments.

Findings

WAITR achieves up to 27.1% greater event coverage than greedy methods.

The framework improves safety by reducing hazard exposure.

Experimental results validate the effectiveness of the knowledge graph-based planning.

Abstract

This paper addresses the challenge of multi-agent path planning for efficient data collection in dynamic, uncertain environments, exemplified by autonomous underwater vehicles (AUVs) navigating the Gulf of Mexico. Traditional greedy algorithms, though computationally efficient, often fall short in long-term planning due to their short-sighted nature, missing crucial data collection opportunities and increasing exposure to hazards. To address these limitations, we introduce WAITR (Weighted Aggregate Inter-Temporal Reward), a novel path-planning framework that integrates a knowledge graph with pathlet-based planning, segmenting the environment into dynamic, speed-adjusted sub-regions (pathlets). This structure enables coordinated, adaptive planning, as agents can operate within time-bound regions while dynamically responding to environmental changes. WAITR's cumulative scoring mechanism balances immediate data collection with long-term optimization of Points of Interest (POIs), ensuring safer navigation and comprehensive data coverage. Experimental results show that WAITR substantially improves POI coverage and reduces exposure to hazards, achieving up to 27.1\% greater event coverage than traditional greedy methods.