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

arXiv: https://arxiv.org/abs/2505.08382 · HTML: https://mlsanity.com/paper/2505.08382/

## 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.

## 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, 7 of 7)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [State-wise Safe Reinforcement Learning: A Survey](https://mlsanity.com/paper/2302.03122/) (2023)
- [Action Mapping for Reinforcement Learning in Continuous Environments with Constraints](https://mlsanity.com/paper/2412.04327/) (2024)
- [Challenges to Solving Combinatorially Hard Long-Horizon Deep RL Tasks](https://mlsanity.com/paper/2206.01812/) (2022)
- [Equivariant Ensembles and Regularization for Reinforcement Learning in Map-based Path Planning](https://mlsanity.com/paper/2403.12856/) (2024)
- [Learning to Recharge: UAV Coverage Path Planning through Deep Reinforcement Learning](https://mlsanity.com/paper/2309.03157/) (2023)
- [Learning to Generate All Feasible Actions](https://mlsanity.com/paper/2301.11461/) (2023)

## 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.