Ecole d'ingénieur et centre de recherche en Sciences du numérique

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

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

Submitted to 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Submitted on ArXiv, 5 March 2020

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.

Bibtex

Titre:UAV coverage path planning under varying power constraints using deep reinforcement learning
Type:Conférence
Langue:English
Ville:Las Vegas
Pays:ÉTATS-UNIS
Date:
Département:Systèmes de Communication
Eurecom ref:6195
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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 = {{S}ubmitted to 2020 {IEEE}/{RSJ} {I}nternational {C}onference on {I}ntelligent {R}obots and {S}ystems ({IROS}), {S}ubmitted on {A}r{X}iv, 5 {M}arch 2020}, address = {{L}as {V}egas, {\'{E}}{TATS}-{UNIS}}, month = {03}, url = {http://www.eurecom.fr/publication/6195} }
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