mlsanityData to 14 Oct 2026

Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning

arXiv 2502.05996 ↗First posted 9 Feb 2025cs.RO cs.AIcited by 2

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

Abstract (arXiv)

This paper investigates the application of Deep Reinforcement (DRL) Learning to address motion control challenges in drones for additive manufacturing (AM). Drone-based additive manufacturing promises flexible and autonomous material deposition in large-scale or hazardous environments. However, achieving robust real-time control of a multi-rotor aerial robot under varying payloads and potential disturbances remains challenging. Traditional controllers like PID often require frequent parameter re-tuning, limiting their applicability in dynamic scenarios. We propose a DRL framework that learns adaptable control policies for multi-rotor drones performing waypoint navigation in AM tasks. We compare Deep Deterministic Policy Gradient (DDPG) and Twin Delayed Deep Deterministic Policy Gradient (TD3) within a curriculum learning scheme designed to handle increasing complexity. Our experiments show TD3 consistently balances training stability, accuracy, and success, particularly when mass variability is introduced. These findings provide a scalable path toward robust, autonomous drone control in additive manufacturing.

Key facts. Cited by 2 papers 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 6 of its 33 references that are in the map. First posted on arXiv 9 Feb 2025.

Builds on

6 of 33 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Human-Centric Aware UAV Trajectory Planning in Search and Rescue Missions Employing Multi-Objective Reinforcement Learning with AHP and Similarity-Based Experience Replay28 Feb

Other references · 5

  1. 2022Challenging Common Assumptions in Convex Reinforcement Learningcited by 30
  2. 2022Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Surveycited by 28
  3. 2020A Survey on Activation Functions and their relation with Xavier and He Normal Initializationcited by 13
  4. 2023UAV Path Planning Employing MPC- Reinforcement Learning Method Considering Collision Avoidancecited by 7
  5. 2024PPO-based Dynamic Control of Uncertain Floating Platforms in the Zero-G Environmentcited by 4

Built on by

Cited by year (2026 to 14 Oct)

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

2 papers.

  1. 2025MPC-based Deep Reinforcement Learning Method for Space Robotic Control with Fuel Sloshing Mitigationcited by 2
  2. 2026Rule-based High-Level Coaching for Goal-Conditioned Reinforcement Learning in Search-and-Rescue UAV Missions Under Limited-Simulation Trainingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Digital Twin Synchronization: Bridging the Sim-RL Agent to a Real-Time Robotic Additive Manufacturing Control
  2. 2025Towards Task-Oriented Flying: Framework, Infrastructure, and Principles
  3. 2024Non-Prehensile Aerial Manipulation using Model-Based Deep Reinforcement Learning
  4. 2025GRaD-Nav: Efficiently Learning Visual Drone Navigation with Gaussian Radiance Fields and Differentiable Dynamics
  5. 2025AgilePilot: DRL-Based Drone Agent for Real-Time Motion Planning in Dynamic Environments by Leveraging Object Detection
  6. 2024The Power of Input: Benchmarking Zero-Shot Sim-To-Real Transfer of Reinforcement Learning Control Policies for Quadrotor Control
  7. 2025Dynamics-Invariant Quadrotor Control using Scale-Aware Deep Reinforcement Learning
  8. 2024Improving Generalization in Aerial and Terrestrial Mobile Robots Control Through Delayed Policy Learning