mlsanityData to 13 Oct 2026

Deep Reinforcement Learning based Autonomous Decision-Making for Cooperative UAVs: A Search and Rescue Real World Application

arXiv 2502.20326 ↗First posted 27 Feb 2025cs.RO cs.AIcited by 3

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

This paper presents the first end-to-end framework that combines guidance, navigation, and centralised task allocation for multiple UAVs performing autonomous search-and-rescue (SAR) in GNSS-denied indoor environments. A Twin Delayed Deep Deterministic Policy Gradient controller is trained with an Artificial Potential Field (APF) reward that blends attractive and repulsive potentials with continuous control, accelerating convergence and yielding smoother, safer trajectories than distance-only baselines. Collaborative mission assignment is solved by a deep Graph Attention Network that, at each decision step, reasons over the drone-task graph to produce near-optimal allocations with negligible on-board compute. To arrest the notorious Z-drift of indoor LiDAR-SLAM, we fuse depth-camera altimetry with IMU vertical velocity in a lightweight complementary filter, giving centimetre-level altitude stability without external beacons. The resulting system was deployed on two 1m-class quad-rotors and flight-tested in a cluttered, multi-level disaster mock-up designed for the NATO-Sapience Autonomous Cooperative Drone Competition. Compared with prior DRL guidance that remains largely in simulation, our framework demonstrates an ability to navigate complex indoor environments, securing first place in the 2024 event. These results demonstrate that APF-shaped DRL and GAT-driven cooperation can translate to reliable real-world SAR operations.

Key facts. Cited by 3 papers in the map (data through 13 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 20 of its 60 references that are in the map. First posted on arXiv 27 Feb 2025.

Builds on

20 of 60 references.

References · 20

  1. 2017Proximal Policy Optimization Algorithmscited by 14,592
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,318
  3. 2017Graph Attention Networkscited by 8,888
  4. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,991
  5. 2015Continuous control with deep reinforcement learningcited by 3,290
  6. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,422
  7. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,310
  8. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,525
  9. 2017AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehiclescited by 518
  10. 2017Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewardscited by 304
  11. 2018Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networkscited by 54
  12. 2019Multi-Agent Actor-Critic with Hierarchical Graph Attention Networkcited by 33
8 more
  1. 2022Robust Adversarial Attacks Detection based on Explainable Deep Reinforcement Learning For UAV Guidance and Planningcited by 13
  2. 2022Visual SLAM: What are the Current Trends and What to Expect?cited by 10
  3. 2023GA-DRL: Graph Neural Network-Augmented Deep Reinforcement Learning for DAG Task Scheduling over Dynamic Vehicular Cloudscited by 10
  4. 2022DL-DRL: A double-level deep reinforcement learning approach for large-scale task scheduling of multi-UAVcited by 9
  5. 2021Graph Attention Network-based Multi-agent Reinforcement Learning for Slicing Resource Management in Dense Cellular Networkcited by 7
  6. 2023Sim-to-Real Deep Reinforcement Learning based Obstacle Avoidance for UAVs under Measurement Uncertaintycited by 5
  7. 2019Control of Cooperative Unmanned Aerial Vehicles: Review of Applications, Challenges, and Algorithmscited by 3
  8. 2021Obstacle Avoidance for UAS in Continuous Action Space Using Deep Reinforcement Learningcited by 2

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025UAVs Meet Agentic AI: A Multidomain Survey of Autonomous Aerial Intelligence and Agentic UAVscited by 13
  2. 2026ESARBench: A Benchmark for Agentic UAV Embodied Search and Rescuecited by 4
  3. 2026Shared Voxel-Map-Based Cooperative Indoor UAV Guidance with a Multi-Agent Soft Actor-Critic Controllercited by 0