Decision Process of Autonomous Drones for Environmental Monitoring

Yıldırım, Ömür; Diepold, Klaus; Vural, Revna Acar

doi:10.1109/inista.2019.8778341

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…We presented preliminary results about vertical cell decomposition based a decision process for an unmanned aerial vehicle and discussed about the requirements that autonomy weights on the system [23]. One of the main differences of our work and previous studies is the processing aerial view of small-sized individual targets rather a group of targets.…”

Section: Introductionmentioning

confidence: 99%

Improving coverage method of autonomous drones for environmentalmonitoring

2020

Turk J Elec Eng & Comp Sci

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With the rapid developments of unmanned aerial vehicles (UAVs), usage of UAVs is increasing to bring autonomy for complicated processes such as environmental monitoring. Because of the complexity of the problem, environmental monitoring tasks are highly demanding in terms of time and resources. To reduce expensive costs of operations, improvements on autonomous observation capabilities has a key role. In this work, we offer coverage improvements for our autonomous environmental monitoring system. We compared different path planning approaches to find out the optimum path planning solution. Simulation results showed that required task execution time and required resources are decreased by usage of improved decomposition of the coverage field.

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Section: Introductionmentioning

confidence: 99%

Improving coverage method of autonomous drones for environmentalmonitoring

2020

Turk J Elec Eng & Comp Sci

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Unmanned Aerial Vehicles Communications Security Challenges: A Survey

Krichen

Mihoub

2023

Synthesis Lectures on Intelligent Technologies

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An Algorithm of Complete Coverage Path Planning for Unmanned Surface Vehicle Based on Reinforcement Learning

Xing

Wang

Liu

et al. 2023

JMSE

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A deep reinforcement learning method to achieve complete coverage path planning for an unmanned surface vehicle (USV) is proposed. This paper firstly models the USV and the workspace required for complete coverage. Then, for the full-coverage path planning task, this paper proposes a preprocessing method for raster maps, which can effectively delete the blank areas that are impossible to cover in the raster map. In this paper, the state matrix corresponding to the preprocessed raster map is used as the input of the deep neural network. The deep Q network (DQN) is used to train the complete coverage path planning strategy of the agent. The improvement of the selection of random actions during training is first proposed. Considering the task of complete coverage path planning, this paper replaces random actions with a set of actions toward the nearest uncovered grid. To solve the problem of the slow convergence speed of the deep reinforcement learning network in full-coverage path planning, this paper proposes an improved method of deep reinforcement learning, which superimposes the final output layer with a dangerous actions matrix to reduce the risk of selection of dangerous actions of USVs during the learning process. Finally, the designed method validates via simulation examples.

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Decision Process of Autonomous Drones for Environmental Monitoring

Cited by 3 publications

References 11 publications

Improving coverage method of autonomous drones for environmentalmonitoring

Improving coverage method of autonomous drones for environmentalmonitoring

Unmanned Aerial Vehicles Communications Security Challenges: A Survey

An Algorithm of Complete Coverage Path Planning for Unmanned Surface Vehicle Based on Reinforcement Learning

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