# UAV Coverage Path Planning under Varying Power Constraints using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2003.02609 · HTML: https://mlsanity.com/paper/2003.02609/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2020.

## Built on by (most cited first, 15 of 15)

- [Deep PackGen: A Deep Reinforcement Learning Framework for Adversarial Network Packet Generation](https://mlsanity.com/paper/2305.11039/) (2023)
- [Learning Coverage Paths in Unknown Environments with Deep Reinforcement Learning](https://mlsanity.com/paper/2306.16978/) (2023)
- [Mixed Integer Programming for Time-Optimal Multi-Robot Coverage Path Planning with Efficient Heuristics](https://mlsanity.com/paper/2306.17609/) (2023)
- [Equivariant Ensembles and Regularization for Reinforcement Learning in Map-based Path Planning](https://mlsanity.com/paper/2403.12856/) (2024)
- [Censored Deep Reinforcement Patrolling with Information Criterion for Monitoring Large Water Resources using Autonomous Surface Vehicles](https://mlsanity.com/paper/2210.08115/) (2022)
- [Learning to Recharge: UAV Coverage Path Planning through Deep Reinforcement Learning](https://mlsanity.com/paper/2309.03157/) (2023)
- [The Emergence of Deep Reinforcement Learning for Path Planning](https://mlsanity.com/paper/2507.15469/) (2025)
- [Sim-to-Real Transfer of Deep Reinforcement Learning Agents for Online Coverage Path Planning](https://mlsanity.com/paper/2406.04920/) (2024)
- [Deep Recurrent Q-learning for Energy-constrained Coverage with a Mobile Robot](https://mlsanity.com/paper/2210.00327/) (2022)
- [Synthetic Aperture Sensing for Occlusion Removal with Drone Swarms](https://mlsanity.com/paper/2212.14692/) (2022)
- [UAV Obstacle Avoidance by Human-in-the-Loop Reinforcement in Arbitrary 3D Environment](https://mlsanity.com/paper/2304.05959/) (2023)
- [Coverage Path Planning for Thermal Interface Materials](https://mlsanity.com/paper/2405.13512/) (2024)
- [Navigating the Smog: A Cooperative Multi-Agent RL for Accurate Air Pollution Mapping through Data Assimilation](https://mlsanity.com/paper/2407.12539/) (2024)
- [On-policy Actor-Critic Reinforcement Learning for Multi-UAV Exploration](https://mlsanity.com/paper/2409.11058/) (2024)
- [Coverage Path Planning: Classical Foundations, Recent Advances, and Future Directions](https://mlsanity.com/paper/2607.10649/) (2026)

## Abstract (arXiv)

Coverage path planning (CPP) is the task of designing a trajectory that enables a mobile agent to travel over every point of an area of interest. We propose a new method to control an unmanned aerial vehicle (UAV) carrying a camera on a CPP mission with random start positions and multiple options for landing positions in an environment containing no-fly zones. While numerous approaches have been proposed to solve similar CPP problems, we leverage end-to-end reinforcement learning (RL) to learn a control policy that generalizes over varying power constraints for the UAV. Despite recent improvements in battery technology, the maximum flying range of small UAVs is still a severe constraint, which is exacerbated by variations in the UAV's power consumption that are hard to predict. By using map-like input channels to feed spatial information through convolutional network layers to the agent, we are able to train a double deep Q-network (DDQN) to make control decisions for the UAV, balancing limited power budget and coverage goal. The proposed method can be applied to a wide variety of environments and harmonizes complex goal structures with system constraints.