mlsanityData to 14 Oct 2026

Deep RL-based Autonomous Navigation of Micro Aerial Vehicles (MAVs) in a complex GPS-denied Indoor Environment

arXiv 2504.05918 ↗First posted 8 Apr 2025cs.RO cs.LGcited by 1

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

Abstract (arXiv)

The Autonomy of Unmanned Aerial Vehicles (UAVs) in indoor environments poses significant challenges due to the lack of reliable GPS signals in enclosed spaces such as warehouses, factories, and indoor facilities. Micro Aerial Vehicles (MAVs) are preferred for navigating in these complex, GPS-denied scenarios because of their agility, low power consumption, and limited computational capabilities. In this paper, we propose a Reinforcement Learning based Deep-Proximal Policy Optimization (D-PPO) algorithm to enhance realtime navigation through improving the computation efficiency. The end-to-end network is trained in 3D realistic meta-environments created using the Unreal Engine. With these trained meta-weights, the MAV system underwent extensive experimental trials in real-world indoor environments. The results indicate that the proposed method reduces computational latency by 91% during training period without significant degradation in performance. The algorithm was tested on a DJI Tello drone, yielding similar results.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 7 of its 10 references that are in the map. First posted on arXiv 8 Apr 2025.

Builds on

7 of 10 references.

References · 7

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2024Depth Anything: Unleashing the Power of Large-Scale Unlabeled Datacited by 1,360
  3. 2021Learning High-Speed Flight in the Wildcited by 128
  4. 2021Autonomous Drone Racing with Deep Reinforcement Learningcited by 60
  5. 2019Autonomous Navigation via Deep Reinforcement Learning for Resource Constraint Edge Nodes using Transfer Learningcited by 19
  6. 2022Autonomous Unmanned Aerial Vehicle Navigation using Reinforcement Learning: A Systematic Reviewcited by 6
  7. 2018NAVREN-RL: Learning to fly in real environment via end-to-end deep reinforcement learning using monocular imagescited by 5

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Reinforcement Learning for Autonomous Point-to-Point UAV Navigationcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2024DRAL: Deep Reinforcement Adaptive Learning for Multi-UAVs Navigation in Unknown Indoor Environment
  2. 2024Navigation in a simplified Urban Flow through Deep Reinforcement Learning
  3. 2025Navigation in a Three-Dimensional Urban Flow using Deep Reinforcement Learning
  4. 2025Autonomous UAV Flight Navigation in Confined Spaces: A Reinforcement Learning Approach
  5. 2026Learning-Based Dynamic Obstacle Avoidance for a UAV Using Only Three Range Sensors
  6. 2026AION: Aerial Indoor Object-Goal Navigation Using Dual-Policy Reinforcement Learning
  7. 2025AgilePilot: DRL-Based Drone Agent for Real-Time Motion Planning in Dynamic Environments by Leveraging Object Detection
  8. 2025GRaD-Nav: Efficiently Learning Visual Drone Navigation with Gaussian Radiance Fields and Differentiable Dynamics