SkyAgent: A lightweight LLM-driven reinforcement learning framework for adaptive cooperative path planning of two UAVs

Physics-prior-driven distributed deep reinforcement learning for multi-UAV path planning

Cooperative Multi-UAV Navigation in Complex Environments via Systematic Multi-Agent Deep Reinforcement Learning

A multi-agent deep reinforcement learning framework that addresses issues through coordinated exploration, demonstration exploitation, safe curriculum scheduling, and structure-aware generalisation is proposed, demonstrating strong performance in collaboration success rate, navigation robustness, zero-shot cross-scenario generalisation, and dynamic environment adaptability.

FALCON-MASAC: Formation-Aware Attention-Enhanced Leader-Guided Control-Barrier Optimization for Safe Multi-UAV Formation Navigation in Dynamic 3-D Environments

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.

LSTP-Nav: Lightweight Spatiotemporal Policy for Map-Free Multi-Agent Navigation With LiDAR

Safe map-free multi-robot navigation requires robots to make real-time decisions from partial and noisy local observations in dynamic, unstructured environments. Existing approaches often depend on prior maps, computationally intensive perception pipelines, or carefully tuned interaction models, which limit their robustness on resource-constrained platforms. This paper proposes LSTP-Nav, a lightweight, decentralized navigation framework built on LSTP-Net that maps stacked 2D LiDAR observations, goal information, and velocity feedback directly to action. LSTP-Net combines GRU-based temporal modeling with attention to extract interaction-critical spatiotemporal cues at low computational cost. We further introduce an HS reward to provide smooth, heading-aware safety feedback, and develop PhysReplay-SimLab to improve training effectiveness through local replay of near-failure interactions. Extensive simulation and real-world experiments show that LSTP-Nav outperforms representative baselines, achieving 98.6%–100.0% success in single-robot settings and 97.8%–99.0% success in 10-robot settings with 5–35 obstacles. The policy also remains robust to LiDAR degradation, nonconvex layouts, random seeds, and large-scale density tests, while achieving over 40 Hz onboard inference on a Raspberry Pi 3 Model B with zero-shot sim-to-real transfer. Overall, LSTP-Nav provides a practical, deployable solution for real-time, map-free multi-agent navigation on computationally constrained mobile robots. Note to Practitioners—This study addresses a pressing practical need in large-scale site inspection and outdoor search-and-rescue: enabling mobile robots with limited onboard computation to navigate safely and efficiently in complex, previously unseen environments without relying on pre-built maps. In real deployments, robots must make decisions from partial and noisy sensor observations while interacting with moving objects (e.g., pedestrians or teammates), and conventional map-based pipelines or computationally heavy planners can become unreliable or infeasible. To tackle these constraints, we propose a lightweight spatiotemporal navigation policy that directly converts raw LiDAR observations into motion commands, combining temporal memory with attention to focus computation on interaction-critical cues. In addition, a heading-stability objective and a physics-based replay-assisted training workflow improve behavior smoothness and reduce failure cases such as oscillation, collisions, and getting trapped in cluttered areas. Extensive simulation and real-world tests show that the proposed approach improves navigation success and safety over representative baselines while maintaining real-time performance on CPU-only platforms, making it suitable for practical deployment on resource-constrained robots in dynamic, unstructured environments.

Efficient Exploration-Enabled Multi-Agent Reinforcement Learning for Multi-UAV Cooperative Target Search

Multi-UAV Cooperative Target Search (MCTS) is a critical task in low-altitude sensing applications, requiring agents to efficiently explore unknown environments under complex constraints. However, traditional search methods are mostly unscalable and perform poorly in dynamic multi-UAV environments. As a promising alternative, Reinforcement Learning (RL) has emerged to overcome these limitations by enabling agents to learn adaptive policies directly from environmental interactions. A key limitation is that current RL methods lack efficient exploration, which is a critical bottleneck preventing UAVs from finding more targets. To address this limitation, we propose a novel method named AEQMIX, which integrates trajectory entropy maximization into QMIX, an advanced Multi-Agent Reinforcement Learning (MARL) method, to encourage efficient exploration. We formulate the MCTS problem as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) and design a multi-objective reward function. To mitigate the intractability of density estimation in high-dimensional spaces, we employ a nonparametric particle-based entropy estimator to quantify the spatial diversity of UAV trajectories. This entropy estimate is utilized as an intrinsic reward, incentivizing agents to maximize the distance between their trajectories and those of their neighbors. Extensive simulations demonstrate that AEQMIX significantly outperforms baseline reinforcement learning and traditional optimization methods in terms of search rate, coverage efficiency, and collision avoidance. Compared with DNQMIX, AEQMIX improves the search rate and coverage rate by 9.52% and 11.54%, respectively, while reducing the average collision count by 70.59% in the (40 × 40) environment.

Intelligent Multi-UAV Navigation in ITNTNs: A Hierarchical LLM Approach

The deployment of high-speed Uncrewed Aerial Vehicles (UAVs) in 3D aerial highways necessitates robust coordination of physical flight kinematics and multi-tier network handovers. While Deep Reinforcement Learning (DRL) offers rapid tactical control, it lacks the zero-shot strategic reasoning required to quickly adapt to dynamic Integrated Terrestrial and Non-Terrestrial Networks (ITNTNs). Conversely, Large Language Models (LLMs) excel at semantic reasoning but suffer from high inference latency, rendering them unsuitable for real-time aerodynamic control. To bridge this gap, we propose a novel Hierarchical LLM-driven control framework. A massive cloud-based LLM deployed on a High-Altitude Platform Station (HAPS) manages slow-timescale global load balancing, while lightweight edge-LLMs on individual UAVs translate local observations into tactical sub-goals. These sub-goals guide a fast-timescale physical DRL controller to execute collision-free, handover-aware trajectories. Simulation results demonstrate that our agentic architecture significantly reduces collision rates and improves aggregate system throughput compared to existing baselines.