SLM-A* is designed as a language-native planning agent that can interoperate with multi-agent LLM frameworks as a callable planning sub-module, thus providing an architectural capability not available to conventional planners that operate on numerical graph representations.
Abstract
Multi-UAV path planning in threat-aware environments requires generating trajectories that reach mission objectives while maintaining obstacle clearance, inter-agent separation, and computational efficiency. This paper presents SLM-A*, a hybrid language-to-waypoints planning framework that fine-tunes compact transformer models to generate discrete multi-UAV waypoint sequences from structured mission descriptions. The generated trajectories are treated as candidate plans and are subsequently evaluated using a deterministic geometric safety checker that verifies obstacle avoidance, risk exposure, inter-UAV separation, edge-level conflicts, and motion feasibility. We construct a dataset of 3,000 static threat-aware scenarios consisting of three UAVs, four threats, one objective, and a $40\times 40$ grid using a prioritized time-expanded A* planner with clearance and turn penalties. Six small language models, including Gemma-2B, Fox-1-1.6B, Qwen2.5-1.5B, Phi-2-2.7B, StableLM-3B, and TinyLlama-1.1B, are adapted using QLoRA with 4-bit NF4 quantization. Among these models, TinyLlama-1.1B achieves the strongest overall text-level performance and inference throughput and is selected as the SLM-A* planning module. On a 315-scenario held-out benchmark, SLM-A* achieves the best average values on four of five evaluated trajectory metrics, including makespan, sum of costs, conflict count, and risk exposure, while CBS maintains the largest minimum-separation margin. Gazebo-ArduPilot-MAVROS simulations further demonstrate that the generated waypoint sequences can be executed by a realistic UAV control stack. The results suggest that compact language models, when combined with explicit geometric validation, can serve as efficient candidate planners for multi-UAV waypoint generation under resource-constrained deployment settings. Beyond trajectory performance, SLM-A* is designed as a language-native planning agent that can interoperate with multi-agent LLM frameworks as a callable planning sub-module, thus providing an architectural capability not available to conventional planners that operate on numerical graph representations.
Maritime unmanned aerial vehicle (UAV) missions such as ship inspection, search and rescue, environmental monitoring, and emergency response often involve multi-wave task releases, time-sensitive deadlines, constrained support vessel positions, and spatially heterogeneous risk. These factors couple task allocation with path planning and make fixed dispatching rules fragile under changing mission profiles. This study develops a hierarchical cooperative planning framework for multiple UAVs over a maritime risk field. A risk-cost A* layer generates feasible routes from support vessels to task points and estimates path length, risk exposure, and sortie duration. A rolling scheduler constructs feasible UAV task candidates, while a scenario-switching-aware LinUCB hyper-heuristic selects online among deadline-first, distance-first, risk-aware, and endurance-balancing rules. A forgetting-update, one-step look-ahead, scenario memory, and lightweight switching detection are used to improve adaptation to mission profile changes. Simulations on a 28 × 40 maritime grid with two support vessels, six UAVs, 40 tasks, and nine release waves show that the proposed framework achieves the highest average effective reward (370.18), the lowest average value regret (0.61), and a best reward ratio of 0.46 over 24 random scenarios. The results should be interpreted as evidence from an idealized simulation benchmark. The main benefit is improved reward robustness under non-stationary and high-risk profiles, rather than uniform gains across all metrics or direct field-deployment validation.
Jian Wu, Shengchang Liu, Wenxi Ni et al.· Drones· 0 citations
Safe cooperative navigation of unmanned aerial vehicle (UAV) formations through three-dimensional environments with dense obstacles, dynamic threats, and unstructured terrain requires jointly addressing goal-directed navigation, formation keeping, hard safety constraints, and formation-topology consistency. We present FALCON-MASAC, a safety-integrated multi-agent reinforcement learning framework that decomposes this task into four complementary layers: (1) a hierarchical leader-follower paradigm that pairs a pre-trained virtual leader with followers learning a distributed cooperative policy; (2) a dual-scale entity-risk attention encoder (DSER-AE) that structures heterogeneous observations into entity-scale and risk-scale semantic tokens and fuses them through intra-scale self-attention and inter-scale full-sequence self-attention over the concatenated tokens; (3) a safety shield built on signed distance functions and high-order control barrier functions (SDF-HOCBF) that maintains the conditional safety certificate while the robust quadratic program remains feasible and the high-order admissibility conditions hold, and switches to a bounded best-effort fallback otherwise; and (4) a bypass-side commitment coordination layer that suppresses trajectory chattering and mitigates crossing conflicts among neighboring UAVs. The safety analysis explicitly quantifies the SDF linearization error and dynamic-obstacle prediction uncertainty and describes the bounded fallback used when certified execution is unavailable. Under the centralized-training-with-decentralized-execution (CTDE) paradigm, simulation experiments show that FALCON-MASAC substantially outperforms representative baselines: it attains a 97.3% success rate (21.7 percentage points above the strongest pure-MARL baseline and 13.0 percentage points above a post-hoc CBF-RL variant), a success-conditional steady-tracking formation error of 1.524 m, and a minimum clearance of 2.732 m. The framework also generalizes well along two out-of-distribution dimensions: obstacle density and dynamic-obstacle speed.
Urban multi-UAV navigation in dense building environments requires not only collision-free geometric paths but also executable flight processes under motion constraints and inter-UAV safety requirements. A static path that is feasible in a geometric map may still fail during closed-loop execution because of velocity limits, acceleration constraints, local path-association errors, and coupled multi-UAV interactions. Meanwhile, end-to-end reinforcement learning often suffers from unstable training, weak geometric interpretability, poor early-stage safety, and high sample complexity. To address these issues, this paper proposes a hierarchical planning-and-learning framework that connects static reference path generation, executable reference tracking, successful demonstration distillation, and conflict-aware residual reinforcement learning. First, three-dimensional reference paths are generated offline in an OpenStreetMap-based urban scene represented by cuboid buildings. Second, a damped reference-tracking mechanism transforms these static paths into closed-loop executable reference processes through local path association, monotonic progress updating, path recapture, look-ahead guidance, and bounded action construction. Third, successful pure-reference executions are distilled for behavior-cloning initialization. Finally, a bounded residual TD3 module is introduced as a local conflict-correction mechanism around the verified executable reference baseline. Experiments in an urban scene containing 754 buildings show that simplified tracking strategies fail to execute the static paths reliably, whereas the proposed full-damped reference-tracking controller achieves a 91.67% all-success rate and eliminates building collision episodes in the tracking-ablation test. Speed-sensitivity experiments at 10, 15, and 20 m/s show the same 91.67% all-success rate, indicating that the conclusion is not dependent on a single speed setting. In constructed conflict-stress tests, the conflict-aware residual TD3 module increases the all-success rate from 33.33% to 80.09%, reduces inter-UAV collision episodes from 66.67% to 11.57%, and improves the hard-safety satisfaction rate from 33.33% to 87.04%. These results show that the main contribution of the proposed framework lies in converting static geometric paths into executable reference trajectories and further enabling bounded residual correction under inter-UAV conflict conditions.
Unmanned Aerial Vehicle Path Planning (UAVPP) in obstacle-rich environments requires trajectories that are collision-free, threat-aware, and feasible under practical flight constraints. This study proposes an Intelligent Cooperative Differential Evolution approach, reffered to as MuCDEA, to improve the adaptability and robustness of conventional Differential Evolution (DE) for UAVPP. MuCDEA integrates complementary mechanisms from JADE, CoDE, EPSDE, SaDE, MIDE, and SHADE through adaptive strategy selection and cooperative evolution. The optimization model combines path-length (fuel) cost and threat exposure with explicit pitch and yaw constraints that enforce actuator-feasible maneuvering bounds. The proposed framework is evaluated on 20 benchmark UAVPP cases covering 2D and 3D scenarios with varying obstacle distributions and pathh discretization levels, and it is compared against 11 state-of-the-art DE variants and several widely used optimization methods using the CEC-2022 ranking methodology. Results show that the cooperative configuration MuCDEA24 achieves the best overall ranking and consistently produces feasible trajectories across the tested cases, indicating that cooperative DE strategies provide an effective and controller-compatible solution for constrained UAVPP.
H. Bouchekara, Y. A. Sha’aban, M. S. Shahriar et al.· Actuators· 0 citations
Deploying Uncrewed Aerial Vehicles (UAVs) for dynamic target search in disaster response scenarios can reduce losses. This paper investigates multi-UAV cooperative trajectory planning for dynamic target search in a three-dimensional environment with static obstacles, aiming to maximize the number of searched targets and minimize the average uncertainty of the search area, while ensuring collision avoidance between UAVs and obstacles. Existing Multi-Agent Reinforcement Learning (MARL) based methods face the sparse reward problem in dynamic target search, which hinders planning feasible multi-UAV trajectories. Notably, Large Language Models (LLMs), with extensive pre-trained knowledge and powerful semantic reasoning capabilities, exhibit potential for designing high-quality reward functions to alleviate the sparse reward problem. Therefore, we propose an LLM-guided Multi-Agent Proximal Policy Optimization (LLM-MAPPO) algorithm, which leverages LLMs’ reasoning capabilities to guide MARL policy learning and plans multi-UAV trajectories for efficient dynamic target search. Specifically, we design an offline LLM reward shaping scheme that generates dense reward signals to mitigate the sparse reward problem. Moreover, we propose a dual-mode pheromone-based search mechanism to guide UAVs to respond promptly to changes in target positions. Experimental results demonstrate that LLM-MAPPO significantly outperforms compared algorithms in terms of the number of searched targets and average area uncertainty, while successfully avoiding collisions. In particular, LLM-MAPPO reduces the target search time by 71.4%.
Yifei Liu, Xiaoshuai Li, Xia Jiang et al.· IEEE Transactions on Cogniti...· 0 citations
This work presents a prioritized Safe Interval Path Planning algorithm (SIPP-PP) with a novel limited goal reservation strategy to prevent goal-blocking conflicts while allowing shared goal regions, and demonstrates a multi-robot planner capable of real-time operation in dense scenarios, satisfying the stringent requirements of industrial applications such as drive units in fulfillment centers.
Rajat Kumar, Kristin Predeck, Ken Meszaros et al.· 0 citations