mlsanityData to 14 Oct 2026

Dual Agent Learning Based Aerial Trajectory Tracking

arXiv 2410.23571 ↗First posted 31 Oct 2024cs.ROcited by 0

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

Abstract (arXiv)

This paper presents a novel reinforcement learning framework for trajectory tracking of unmanned aerial vehicles in cluttered environments using a dual-agent architecture. Traditional optimization methods for trajectory tracking face significant computational challenges and lack robustness in dynamic environments. Our approach employs deep reinforcement learning (RL) to overcome these limitations, leveraging 3D pointcloud data to perceive the environment without relying on memory-intensive obstacle representations like occupancy grids. The proposed system features two RL agents: one for predicting UAV velocities to follow a reference trajectory and another for managing collision avoidance in the presence of obstacles. This architecture ensures real-time performance and adaptability to uncertainties. We demonstrate the efficacy of our approach through simulated and real-world experiments, highlighting improvements over state-of-the-art RL and optimization-based methods. Additionally, a curriculum learning paradigm is employed to scale the algorithms to more complex environments, ensuring robust trajectory tracking and obstacle avoidance in both static and dynamic scenarios.

Key facts. No citing papers in the map yet (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 11 of its 37 references that are in the map. First posted on arXiv 31 Oct 2024.

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11 of 37 references.

References · 11

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2015Continuous control with deep reinforcement learningcited by 3,407
  3. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  4. 2024Agile But Safe: Learning Collision-Free High-Speed Legged Locomotioncited by 73
  5. 2020AlphaPilot: Autonomous Drone Racingcited by 30
  6. 2022Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Trackingcited by 18
  7. 2019Learning Vision-based Flight in Drone Swarms by Imitationcited by 13
  8. 2022Deep-PANTHER: Learning-Based Perception-Aware Trajectory Planner in Dynamic Environmentscited by 11
  9. 2021A Meta-Learning-based Trajectory Tracking Framework for UAVs under Degraded Conditionscited by 2
  10. 2019Barrier Functions in Cascaded Controller: Safe Quadrotor Controlcited by 1
  11. 2022Optimization-based Trajectory Tracking Approach for Multi-rotor Aerial Vehicles in Unknown Environmentscited by 1

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  3. 2024Multi-UAV Formation Control with Static and Dynamic Obstacle Avoidance via Reinforcement Learning
  4. 2020Deep Reinforcement Learning based Local Planner for UAV Obstacle Avoidance using Demonstration Data
  5. 2026Cooperative Multi-UAV Navigation in Complex Environments via Systematic Multi-Agent Deep Reinforcement Learning ↗
  6. 2025The Emergence of Deep Reinforcement Learning for Path Planning
  7. 2025CORB-Planner: Corridor as Observations for RL Planning in High-Speed Flight
  8. 2024Improving Generalization in Aerial and Terrestrial Mobile Robots Control Through Delayed Policy Learning