mlsanityData to 14 Oct 2026

Reinforcement Learning for Autonomous Point-to-Point UAV Navigation

arXiv 2509.13943 ↗First posted 17 Sep 2025cs.RO cs.SY eess.SYcited by 1

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

Abstract (arXiv)

Unmanned Aerial Vehicles (UAVs) are increasingly used in automated inspection, delivery, and navigation tasks that require reliable autonomy. This project develops a reinforcement learning (RL) approach to enable a single UAV to autonomously navigate between predefined points without manual intervention. The drone learns navigation policies through trial-and-error interaction, using a custom reward function that encourages goal-reaching efficiency while penalizing collisions and unsafe behavior. The control system integrates ROS with a Gym-compatible training environment, enabling flexible deployment and testing. After training, the learned policy is deployed on a real UAV platform and evaluated under practical conditions. Results show that the UAV can successfully perform autonomous navigation with minimal human oversight, demonstrating the viability of RL-based control for point-to-point drone operations in real-world scenarios.

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 3 of its 5 references that are in the map. First posted on arXiv 17 Sep 2025.

Builds on

3 of 5 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025Deep RL-based Autonomous Navigation of Micro Aerial Vehicles (MAVs) in a complex GPS-denied Indoor Environment8 Apr

Other references · 2

  1. 2021Deep Reinforcement Learning-based UAV Navigation and Control: A Soft Actor-Critic with Hindsight Experience Replay Approachcited by 4
  2. 2020Reinforcement Learning for UAV Autonomous Navigation, Mapping and Target Detectioncited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026No Training, Better Flights: Test-Time Scaled VLMs for UAV Navigationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025SIGN: Safety-Aware Image-Goal Navigation for Autonomous Drones via Reinforcement Learning
  2. 2026Zero-Shot, Safe and Time-Efficient UAV Navigation via Potential-Based Reward Shaping, Control Lyapunov and Barrier Functions
  3. 2026Self-Predictive Representation for Autonomous UAV Object-Goal Navigation
  4. 2025LEARN: Learning End-to-End Aerial Resource-Constrained Multi-Robot Navigation
  5. 2026Lightweight Safe Reinforcement Learning for End-to-End UAV Navigation
  6. 2025Curriculum-Based Reinforcement Learning for Autonomous UAV Navigation in Unknown Curved Tubular Conduit
  7. 2025RoboRAN: A Unified Robotics Framework for Reinforcement Learning-Based Autonomous Navigation
  8. 2026AION: Aerial Indoor Object-Goal Navigation Using Dual-Policy Reinforcement Learning