Work

CogMUS: A Soar-Based Cognitive Framework for Mission Understanding in Multi-UAV Cooperative Operation

Year: 2025

Type: article

Abstract: The cooperative operation of multiple Unmanned Aerial Vehicles (multi-UAV) is emerging as a pivotal trend in future complex autonomous systems. To enable accurate mission understanding and efficient collaboration among UAVs in complex, dynamic, and uncertain operational environments, this paper introduces CogMUS, a novel cooperative mission understanding framework based on the Soar cognitive architecture. We first construct a mission understanding framework for UAV operations centered around five typical mission categories. Building on this foundation, we design a distributed cognitive model where each UAV is equipped with a Soar agent. This model leverages the synergy of working memory (WM), long-term memory (LTM), and the decision cycle (DC) to achieve key functionalities, including hierarchical mission decomposition, dynamic task allocation, and proactive airspace conflict detection and resolution. Through comprehensive simulation experiments, we validate the performance of the proposed CogMUS framework across key metrics, including task understanding accuracy, cooperative efficiency, and overall task completion rate. The results demonstrate that CogMUS exhibits superior adaptability to diverse scenarios, as well as remarkable scalability and robustness.

Source: Drones

DOI: 10.3390/drones9120813

Authors: Jiaxin Hu, Tao Wang, Hongrun Wang, Jingshuai Cao

Institutions: Sun Yat-sen University, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)

Language: English

Topic: AI-based Problem Solving and Planning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: National Key Research and Development Program of China