Details

Autor(en) / Beteiligte

• Theile, Mirco
• Bayerlein, Harald
• Nai, Richard
• Gesbert, David
• Caccamo, Marco

Titel

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

Ist Teil von

• 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020, p.1444-1449

Ort / Verlag

IEEE

Erscheinungsjahr

2020

Quelle

IEL

Beschreibungen/Notizen

• 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.