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Multi-UAV Path Planning for Wireless Data Harvesting With Deep Reinforcement Learning

IEEE open journal of the Communications Society, 2021-01, Vol.2, p.1171-1187

2021

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Learning to Generate All Feasible Actions

IEEE access, 2024, Vol.12, p.40668-40681

2024

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A Cyber-Physical Prototyping and Testing Framework to Enable the Rapid Development of UAVs

Aerospace, 2022-05, Vol.9 (5), p.270

2022

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

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

2020

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Edge Generation Scheduling for DAG Tasks Using Deep Reinforcement Learning

IEEE transactions on computers, 2024-04, Vol.73 (4), p.1034-1047

2024

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Latency-Aware Generation of Single-Rate DAGs from Multi-Rate Task Sets

2020 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), 2020, p.226-238

2020

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Cloud-Edge Training Architecture for Sim-to-Real Deep Reinforcement Learning

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022, p.9363-9370

2022

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Trajectory Estimation for Geo-Fencing Applications on Small-Size Fixed-Wing UAVs

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019, p.1971-1977

2019

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UAV Path Planning using Global and Local Map Information with Deep Reinforcement Learning

2021 20th International Conference on Advanced Robotics (ICAR), 2021, p.539-546

2021

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UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning Approach

GLOBECOM 2020 - 2020 IEEE Global Communications Conference, 2020, p.1-6

2020

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Integrated Power Modeling for a Solar-Powered, Computationally-Intensive Unmanned Aircraft

2020 AIAA/IEEE Electric Aircraft Technologies Symposium (EATS), 2020, p.1-21

2020

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uavEE: A Modular, Power-Aware Emulation Environment for Rapid Prototyping and Testing of UAVs

2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), 2018, p.217-224

2018

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A High-Fidelity, Low-Order Propulsion Power Model for Fixed-Wing Electric Unmanned Aircraft

2018 AIAA/IEEE Electric Aircraft Technologies Symposium (EATS), 2018, p.1-16

2018

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Multi-UAV Path Planning for Wireless Data Harvesting With Deep Reinforcement Learning

Learning to Generate All Feasible Actions

A Cyber-Physical Prototyping and Testing Framework to Enable the Rapid Development of UAVs

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

Edge Generation Scheduling for DAG Tasks Using Deep Reinforcement Learning

Latency-Aware Generation of Single-Rate DAGs from Multi-Rate Task Sets

Cloud-Edge Training Architecture for Sim-to-Real Deep Reinforcement Learning

Trajectory Estimation for Geo-Fencing Applications on Small-Size Fixed-Wing UAVs

UAV Path Planning using Global and Local Map Information with Deep Reinforcement Learning

UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning Approach

Integrated Power Modeling for a Solar-Powered, Computationally-Intensive Unmanned Aircraft

uavEE: A Modular, Power-Aware Emulation Environment for Rapid Prototyping and Testing of UAVs

A High-Fidelity, Low-Order Propulsion Power Model for Fixed-Wing Electric Unmanned Aircraft

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