A Novel UAV Swarm Decision-Making System Under Unified Task and Spatial View

Le résumé fourni par la source

In existing UAV swarm task planning methods, task allocation and path planning are usually solved in separate two modules. Although the complexity of problem solving is reduced, the coupling relationship between task allocation and path optimization is not considered in actual tasks, which leads to the problem of reduced collaborative efficiency in UAV swarms. To address the above problems, this paper proposes a novel UAV swarm decision-making system under unified task and spatial view. In the task part, the system uses a large language model (LLM) for high-level reasoning and Chain of Thought (CoT) task decomposition in the high-level task allocation decision module. The LLM planner iteratively decomposes the task into subtasks and dynamically optimizes the reward function of the agent as conditions change. At the same time, the multimodal representation learning module based on Retrieval Augmented Generation (RAG) knowledge integration fuses heterogeneous inputs (such as vision, radar, depth map) into a compact latent state, while the RAG-based module continuously retrieves relevant past experiences from the UAV knowledge base, and the retrieved contextual information is combined with real-time sensor data to provide information for the current UAV decision. These components are tightly coupled with a closed-loop reinforcement learning (RL) agent for the spatial part, where real-time feedback from the LLM and RAG modules adaptively shapes the reward signals. Experiments on challenging cooperative tasks demonstrate that our system significantly outperforms traditional RL baselines in coordination efficiency and adaptability.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.