cs.AIOct 7, 2026
SaveLLM-Enabled UAV Dispatch: A System-Level Survey and Taxonomy
Organizations: School of Computer Science, Zhejiang University of Technology, China
Abstract
Unmanned aerial vehicle (UAV) dispatch is beginning to move beyond isolated path planning and optimization-driven resource allocation toward system-level coordination supported by semantic reasoning and LLM-based interfaces. This survey provides a unified characterization of LLM-enabled UAV dispatch systems that bridges semantic intent, symbolic decision-making, and physical UAV execution. Rather than treating LLMs as standalone add-ons, we conceptualize them as a cross-layer semantic orchestration layer connecting human instructions, external solvers, and distributed control modules. We organize the literature into four representative dispatch paradigms: pipeline dispatch, global assignment dispatch, decentralized agentic dispatch, and divide-and-conquer dispatch. For each paradigm, we analyze its decision logic, system structure, control flow, representative methods, and potential LLM roles. We further examine how LLMs support semantic parsing, retrieval-grounded planning, solver orchestration, local agent reasoning, multi-agent coordination, safety assessment, and human-facing explanation. We discuss the implications of these paradigms for scalability, robustness, coordination burden, and verification requirements, and identify open challenges including latency-aware reasoning, grounding reliability, physical feasibility guarantees, edge deployment, privacy protection, and distributed consistency. This survey provides a system-level taxonomy and design perspective for integrating LLMs into safety-critical UAV dispatch systems.
Figures & tables
Fig. 1: Evolution and scope of LLM-enabled UAV dispatch.
| Surveys | Focus & Scope | Key Findings / Application Categories |
| [ 1 ] | Reviews low-altitude airspace management for uncrewed aircraft and advanced air mobility. | Summarizes airspace concepts, operational requirements, BVLOS integration, regulatory constraints, and traffic-management challenges. |
| [ 31 ] | Surveys trajectory-prediction techniques for UAVs across modeling, data-driven learning, and motion-pattern analysis. | Classifies trajectory-prediction methods and discusses their role in collision avoidance, motion forecasting, and safe UAV operation. |
| [ 32 ] | Reviews UAV path-planning techniques with emphasis on planning algorithms, environment representation, and motion constraints. | Categorizes path-planning approaches by optimization strategy, search mechanism, and constraint handling, with limited attention to end-to-end dispatch logic. |
| [ 33 ] | Surveys computational-intelligence algorithms for UAV swarm networking and collaboration. | Reviews communication, cooperative control, formation, search, learning-based collaboration, and future swarm-networking directions. |
| [ 34 ] | Reviews swarm-intelligence algorithms for multi-UAV collaboration. | Summarizes distributed cooperation, formation, task coordination, search, and collective decision-making mechanisms for UAV swarms. |
| [ 35 ] | Reviews privacy and confidentiality issues in drone operations. | Identifies privacy leakage, data exposure, trust, authentication, and secure communication challenges in UAV deployment. |
TABLE I: Comparison with representative surveys related to UAV dispatch and LLM-enabled aerial intelligence.
Fig. 2: Pipeline dispatch decomposes a mission into ordered processing stages, where an LLM layer can parse intent, call tools, and trigger verification.
| Pipeline Transition | Classical Stage Role | LLM-Era Orchestration Role | Representative Methods |
| Spatial or task decomposition | Semantic scene parsing and RAG-grounded task graph construction | Li et al. [ 42 ] , Xiong et al. [ 43 ] | |
| Hierarchical mission decomposition | Natural-language mission breakdown and role prompting | Luan et al. [ 44 ] , Cheng et al. [ 45 ] , Guo et al. [ 46 ] , Li et al. [ 42 ] , Jünger et al. [ 47 ] | |
| Rolling or adaptive clustering | Context-aware reclustering from execution feedback | Yanmaz et al. [ 48 ] , Ren et al. [ 5 ] | |
| Task or role assignment | Tool-call handoff from task graph to assignment solver | Yanmaz et al. [ 48 ] , Guo et al. [ 46 ] , Zhao et al. [ 49 ] , Li et al. [ 42 ] , Gong et al. [ 6 ] | |
| Hierarchical task allocation | Constraint generation and priority explanation | Luan et al. [ 44 ] , Cheng et al. [ 45 ] , Guo et al. [ 46 ] | |
| Battery or inspection-task matching | Resource-state summarization and repair hints | Alyassi et al. [ 50 ] , Ren et al. [ 5 ] |
TABLE II: Taxonomy of representative pipeline dispatch methods and their LLM-era orchestration roles aligned with Eq. ( 2 ).
Fig. 3: Global assignment dispatch uses a central dispatcher to jointly determine tasks, routes, schedules, and resources, with an LLM layer supporting intent interpretation and solver selection.
| Central Dispatch Dimension | Coupled Decision Role | LLM-Era Assignment Role | Representative Methods |
| Mobility–communication | Trajectory and link scheduling | Global-state summarization and link-aware prompt grounding | Liu and Zheng [ 68 ] , He et al. [ 69 ] , Hu et al. [ 70 ] , Jing et al. [ 52 ] , Zhang et al. [ 53 ] , Wu et al. [ 71 ] |
| Data collection, offloading, and MEC | Service-intent parsing and resource constraint extraction | Nguyen et al. [ 72 ] , Zhu et al. [ 73 ] , Liu and Zheng [ 68 ] , He et al. [ 69 ] , Hu et al. [ 70 ] , Shi et al. [ 74 ] | |
| Association, wireless power, and ISAC | Multi-objective tradeoff explanation and search guidance | Jing et al. [ 52 ] , Zhang et al. [ 53 ] , Wu et al. [ 71 ] , Li et al. [ 28 ] | |
| Routing–scheduling | TSP/VRP-style UAV routing | Natural-language service request to routing constraints | Montemanni and Dell’Amico [ 75 ] , Joo et al. [ 76 ] |
| Truck, vehicle, or mothership coordination | Cross-platform synchronization explanation | Joo et al. [ 76 ] | |
| Delivery, monitoring, inspection, and rescue | Priority-aware dispatch briefing and schedule repair | Yang et al. [ 77 ] , Rigas et al. [ 78 ] , Zhao et al. [ 79 ] , Mao et al. [ 80 ] , Pei et al. [ 81 ] , Betti Sorbelli et al. [ 82 ] |
TABLE III: Taxonomy of representative global assignment dispatch methods and their LLM-era assignment roles.
Fig. 4: Decentralized agentic dispatch lets local UAV agents select tasks from observations, utilities, short-term memory, and limited peer messages.
| Agentic Layer | Local Dispatch Role | LLM-Agent Role | Representative Methods |
| Local decision layer | Reinforcement-learning trajectory control | Perception–memory–action loop for local intent interpretation | Ebrahimi et al. [ 108 ] , Nguyen et al. [ 72 ] , Zhu et al. [ 73 , 86 ] |
| Active perception under uncertainty | Multimodal observation summarizer and next-view planner | Zhu and Chen [ 60 ] , Zhu et al. [ 61 ] , Kirillov et al. [ 63 ] | |
| Tracking, avoidance, and covert motion | Local safety critic and intent-aware motion proposal | Xu et al. [ 109 ] , Zhang et al. [ 110 ] , Huang et al. [ 111 ] | |
| Swarm coordination layer | Decentralized coverage and exploration | Peer-agent message compression and local consensus prompting | blueZhao et al. [ 112 ] , Huang et al. [ 113 ] |
| Relative-state UAV interaction | Relative-observation grounding for semantic communication | Hasan et al. [ 114 ] | |
| MARL and distributed reinforcement learning | Role-aware multi-agent reasoning with bounded messages | Venturini et al. [ 115 ] , Qin and Pournaras [ 90 ] , Hu et al. [ 87 ] , Alkahtani et al. [ 116 ] , Luo et al. [ 29 ] |
TABLE IV: Taxonomy of representative decentralized agentic UAV dispatch methods and their LLM-agent roles.
Fig. 5: Divide-and-conquer dispatch partitions a large mission into local subproblems, solves them separately, and reconciles them through a coordinator.
| Decomposition Principle | Dispatch Role | LLM-Era Orchestration Role | Representative Methods |
| Decision and objective decomposition | Trajectory, energy, and link tradeoff | Objective decomposer and Pareto-search guide | Javed et al. [ 125 ] , Meng et al. [ 126 ] , Jing et al. [ 52 ] |
| Secure trajectory, power, and robustness | Security-rule retrieval and feasibility critic | Kang et al. [ 91 ] , Zhang et al. [ 92 ] , Dang-Ngoc et al. [ 93 ] | |
| Temporal, connectivity, and ISAC objectives | Communication–sensing tradeoff explanation | Meng et al. [ 126 ] , Jing et al. [ 52 ] , Zhang et al. [ 53 ] , Li et al. [ 28 ] | |
| Spatial and regional decomposition | Three-dimensional and multi-region coverage | Semantic map and region partitioning | Xiong et al. [ 43 ] , Li et al. [ 42 ] |
| Emergency waypoints and rescue paths | Disaster-scene grounding and priority partitioning | Liu et al. [ 127 ] , Liu et al. [ 30 ] , Lin et al. [ 24 , 56 ] , Xiong et al. [ 43 ] | |
| Inspection and agriculture scene modeling | Multimodal region labeling and task extraction | Liu et al. [ 127 ] , Liu et al. [ 30 ] , Zhang et al. [ 9 ] , Zhu and Chen [ 60 ] , Kirillov et al. [ 63 ] |
TABLE V: Taxonomy of representative divide-and-conquer dispatch methods and their LLM-era orchestration roles.
| Hotspot | Dispatch Focus | Key Research Issues |
| RAG-grounded dispatch knowledge bases | Retrieve maps, regulations, weather, mission logs, and communication context for planning | Source freshness, retrieval faithfulness, traceable constraint generation |
| LLM-tool-solver co-design | Connect LLM reasoning with optimizers, simulators, planners, and safety monitors | Stable interfaces, feasibility checking, repairable plan representations |
| Decentralized agentic dispatch | Support local semantic interpretation, task claiming, peer negotiation, and replanning | Latency, compact protocols, prompt safety, distributed verification |
| Semantic divide-and-conquer | Partition missions by regions, roles, objectives, knowledge sources, and model placement | Boundary conflicts, load balancing, cross-agent consistency |
| Multimodal aerial reasoning | Fuse aerial images, videos, maps, text instructions, and sensor summaries | Scene grounding, hallucination control, sim-to-real transfer |
| Edge multi-LLM orchestration | Assign reasoning tasks across onboard, edge, and cloud models | Model selection, fallback policies, energy and bandwidth limits |
TABLE VI: Future research directions for LLM-enabled UAV dispatch.