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

arXiv: https://arxiv.org/abs/2502.05996 · HTML: https://mlsanity.com/paper/2502.05996/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 6 of 6)

- [Challenging Common Assumptions in Convex Reinforcement Learning](https://mlsanity.com/paper/2202.01511/) (2022)
- [Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey](https://mlsanity.com/paper/2211.04324/) (2022)
- [A Survey on Activation Functions and their relation with Xavier and He Normal Initialization](https://mlsanity.com/paper/2004.06632/) (2020)
- [UAV Path Planning Employing MPC- Reinforcement Learning Method Considering Collision Avoidance](https://mlsanity.com/paper/2302.10669/) (2023)
- [Human-Centric Aware UAV Trajectory Planning in Search and Rescue Missions Employing Multi-Objective Reinforcement Learning with AHP and Similarity-Based Experience Replay](https://mlsanity.com/paper/2402.18487/) (2024)
- [PPO-based Dynamic Control of Uncertain Floating Platforms in the Zero-G Environment](https://mlsanity.com/paper/2407.03224/) (2024)

## Built on by (most cited first, 2 of 2)

- [MPC-based Deep Reinforcement Learning Method for Space Robotic Control with Fuel Sloshing Mitigation](https://mlsanity.com/paper/2509.21045/) (2025)
- [Rule-based High-Level Coaching for Goal-Conditioned Reinforcement Learning in Search-and-Rescue UAV Missions Under Limited-Simulation Training](https://mlsanity.com/paper/2604.26833/) (2026)

## 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.