Graduate School and Research Center in Digital Sciences

UAV coverage path planning under varying power constraints using deep reinforcement learning

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

IROS 2020, IEEE/RSJ International Conference on Intelligent Robots and Systems, 25-29 October 2020, Las Vegas, NV, USA, (Virtual Conference)

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.

Document Arxiv Bibtex

Title:UAV coverage path planning under varying power constraints using deep reinforcement learning
Type:Conference
Language:English
City:Las Vegas
Country:UNITED STATES
Date:
Department:Communication systems
Eurecom ref:6195
Copyright: © 2020 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Bibtex: @inproceedings{EURECOM+6195, year = {2020}, title = {{UAV} coverage path planning under varying power constraints using deep reinforcement learning}, author = {{T}heile, {M}irco and {B}ayerlein, {H}arald and {N}ai, {R}ichard and {G}esbert, {D}avid and {C}accamo, {M}arco}, booktitle = {{IROS} 2020, {IEEE}/{RSJ} {I}nternational {C}onference on {I}ntelligent {R}obots and {S}ystems, 25-29 {O}ctober 2020, {L}as {V}egas, {NV}, {USA}, ({V}irtual {C}onference) }, address = {{L}as {V}egas, {UNITED} {STATES}}, month = {10}, url = {http://www.eurecom.fr/publication/6195} }
See also: