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

Abstract

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.

arXiv comment2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

References in corpus (1)

• Online Deep Reinforcement Learning for Autonomous UAV Navigation and Exploration of Outdoor Environments

Cited by in corpus (9)

• UAV Path Planning using Global and Local Map Information with Deep Reinforcement Learning
• Integrated Guidance and Gimbal Control for Coverage Planning With Visibility Constraints
• Area Coverage with Multiple Capacity-Constrained Robots
• Unscented Optimal Control for 3D Coverage Planning with an Autonomous UAV Agent
• Integrated Ray-Tracing and Coverage Planning Control using Reinforcement Learning
• Cooperative Receding Horizon 3D Coverage Control with a Team of Networked Aerial Agents
• Sim-to-Real Transfer of Deep Reinforcement Learning Agents for Online Coverage Path Planning
• Deep Recurrent Q-learning for Energy-constrained Coverage with a Mobile Robot
• Equivariant Ensembles and Regularization for Reinforcement Learning in Map-based Path Planning