# Learning to Recharge: UAV Coverage Path Planning through Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2309.03157 · HTML: https://mlsanity.com/paper/2309.03157/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 6 Sep 2023.

## Builds on (most cited first, 10 of 10)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning](https://mlsanity.com/paper/2108.06266/) (2021)
- [Reinforcement Learning for Solving the Vehicle Routing Problem](https://mlsanity.com/paper/1802.04240/) (2018)
- [A Closer Look at Invalid Action Masking in Policy Gradient Algorithms](https://mlsanity.com/paper/2006.14171/) (2020)
- [Action Space Shaping in Deep Reinforcement Learning](https://mlsanity.com/paper/2004.00980/) (2020)
- [UAV Coverage Path Planning under Varying Power Constraints using Deep Reinforcement Learning](https://mlsanity.com/paper/2003.02609/) (2020)
- [UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2007.00544/) (2020)
- [Reinforcement Learning-Based Coverage Path Planning with Implicit Cellular Decomposition](https://mlsanity.com/paper/2110.09018/) (2021)
- [UAV Path Planning using Global and Local Map Information with Deep Reinforcement Learning](https://mlsanity.com/paper/2010.06917/) (2020)
- [Drone swarm patrolling with uneven coverage requirements](https://mlsanity.com/paper/2107.00362/) (2021)

## Built on by (most cited first, 3 of 3)

- [Equivariant Ensembles and Regularization for Reinforcement Learning in Map-based Path Planning](https://mlsanity.com/paper/2403.12856/) (2024)
- [On-policy Actor-Critic Reinforcement Learning for Multi-UAV Exploration](https://mlsanity.com/paper/2409.11058/) (2024)
- [Continuous World Coverage Path Planning for Fixed-Wing UAVs using Deep Reinforcement Learning](https://mlsanity.com/paper/2505.08382/) (2025)

## Abstract (arXiv)

Coverage path planning (CPP) is a critical problem in robotics, where the goal is to find an efficient path that covers every point in an area of interest. This work addresses the power-constrained CPP problem with recharge for battery-limited unmanned aerial vehicles (UAVs). In this problem, a notable challenge emerges from integrating recharge journeys into the overall coverage strategy, highlighting the intricate task of making strategic, long-term decisions. We propose a novel proximal policy optimization (PPO)-based deep reinforcement learning (DRL) approach with map-based observations, utilizing action masking and discount factor scheduling to optimize coverage trajectories over the entire mission horizon. We further provide the agent with a position history to handle emergent state loops caused by the recharge capability. Our approach outperforms a baseline heuristic, generalizes to different target zones and maps, with limited generalization to unseen maps. We offer valuable insights into DRL algorithm design for long-horizon problems and provide a publicly available software framework for the CPP problem.