mlsanityData to 14 Oct 2026

Continuous World Coverage Path Planning for Fixed-Wing UAVs using Deep Reinforcement Learning

arXiv 2505.08382 ↗First posted 13 May 2025cs.RO cs.LG cs.SYcited by 0

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

Abstract (arXiv)

Unmanned Aerial Vehicle (UAV) Coverage Path Planning (CPP) is critical for applications such as precision agriculture and search and rescue. While traditional methods rely on discrete grid-based representations, real-world UAV operations require power-efficient continuous motion planning. We formulate the UAV CPP problem in a continuous environment, minimizing power consumption while ensuring complete coverage. Our approach models the environment with variable-size axis-aligned rectangles and UAV motion with curvature-constrained B'ezier curves. We train a reinforcement learning agent using an action-mapping-based Soft Actor-Critic (AM-SAC) algorithm employing a self-adaptive curriculum. Experiments on both procedurally generated and hand-crafted scenarios demonstrate the effectiveness of our method in learning energy-efficient coverage strategies.

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 7 of its 23 references that are in the map. First posted on arXiv 13 May 2025.

Builds on

7 of 23 references.

References · 7

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2023State-wise Safe Reinforcement Learning: A Surveycited by 55
  3. 2024Action Mapping for Reinforcement Learning in Continuous Environments with Constraintscited by 5
  4. 2022Challenges to Solving Combinatorially Hard Long-Horizon Deep RL Taskscited by 4
  5. 2024Equivariant Ensembles and Regularization for Reinforcement Learning in Map-based Path Planningcited by 4
  6. 2023Learning to Recharge: UAV Coverage Path Planning through Deep Reinforcement Learningcited by 3
  7. 2023Learning to Generate All Feasible Actionscited by 2

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  1. 2024Sim-to-Real Transfer of Deep Reinforcement Learning Agents for Online Coverage Path Planning
  2. 2026Reinforcement Learning-Based Energy-Aware Coverage Path Planning for Precision Agriculture
  3. 2024Dual Agent Learning Based Aerial Trajectory Tracking
  4. 2026Learning-Based Dynamic Obstacle Avoidance for a UAV Using Only Three Range Sensors
  5. 2025Enhancing UAV Path Planning Efficiency Through Accelerated Learning
  6. 2025Autonomous UAV Flight Navigation in Confined Spaces: A Reinforcement Learning Approach
  7. 2025The Emergence of Deep Reinforcement Learning for Path Planning
  8. 2025Safe and Economical UAV Trajectory Planning in Low-Altitude Airspace: A Hybrid DRL-LLM Approach with Compliance Awareness