mlsanityData to 14 Oct 2026

Dynamic Q-planning for Online UAV Path Planning in Unknown and Complex Environments

arXiv 2402.06297 ↗First posted 9 Feb 2024cs.ROcited by 0

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

Abstract (arXiv)

Unmanned Aerial Vehicles need an online path planning capability to move in high-risk missions in unknown and complex environments to complete them safely. However, many algorithms reported in the literature may not return reliable trajectories to solve online problems in these scenarios. The Q-Learning algorithm, a Reinforcement Learning Technique, can generate trajectories in real-time and has demonstrated fast and reliable results. This technique, however, has the disadvantage of defining the iteration number. If this value is not well defined, it will take a long time or not return an optimal trajectory. Therefore, we propose a method to dynamically choose the number of iterations to obtain the best performance of Q-Learning. The proposed method is compared to the Q-Learning algorithm with a fixed number of iterations, A*, Rapid-Exploring Random Tree, and Particle Swarm Optimization. As a result, the proposed Q-learning algorithm demonstrates the efficacy and reliability of online path planning with a dynamic number of iterations to carry out online missions in unknown and complex environments.

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 14 of its 68 references that are in the map. First posted on arXiv 9 Feb 2024.

Builds on

14 of 68 references.

References · 14

  1. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,920
  2. 2017A Brief Survey of Deep Reinforcement Learningcited by 483
  3. 2021Learning High-Speed Flight in the Wildcited by 128
  4. 2016Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learningcited by 118
  5. 2017Uncertainty-Aware Reinforcement Learning for Collision Avoidancecited by 88
  6. 2021Autonomous Drone Racing with Deep Reinforcement Learningcited by 60
  7. 2018Memory-based Deep Reinforcement Learning for Obstacle Avoidance in UAV with Limited Environment Knowledgecited by 26
  8. 2021Time-Optimal Planning for Quadrotor Waypoint Flightcited by 23
  9. 20163D Simulation for Robot Arm Control with Deep Q-Learningcited by 21
  10. 2020The MRS UAV System: Pushing the Frontiers of Reproducible Research, Real-world Deployment, and Education with Autonomous Unmanned Aerial Vehiclescited by 10
  11. 2021PathBench: A Benchmarking Platform for Classical and Learned Path Planning Algorithmscited by 6
  12. 2020Online Exploration and Coverage Planning in Unknown Obstacle-Cluttered Environmentscited by 5
2 more
  1. 2019Fast Simulation of Crowd Collision Avoidancecited by 2
  2. 2020UAV Aided Search and Rescue Operation Using Reinforcement Learningcited by 2

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  1. 2024Optimizing Search and Rescue UAV Connectivity in Challenging Terrain through Multi Q-Learning
  2. 2025Maximizing UAV Cellular Connectivity with Reinforcement Learning for BVLoS Path Planning
  3. 2025Adaptive Path-Planning for Autonomous Robots: A UCH-Enhanced Q-Learning Approach
  4. 2024Multi-Fidelity Reinforcement Learning for Time-Optimal Quadrotor Re-planning
  5. 2026Learning-Based Dynamic Obstacle Avoidance for a UAV Using Only Three Range Sensors
  6. 2025Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning
  7. 2024Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning
  8. 2025Path Planning through Multi-Agent Reinforcement Learning in Dynamic Environments