2022
DOI: 10.1007/s10846-021-01568-y
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Double Critic Deep Reinforcement Learning for Mapless 3D Navigation of Unmanned Aerial Vehicles

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Cited by 28 publications
(9 citation statements)
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“…This information allows the estimation of the agent's degree of learning in the environment, as the improved in terms of reward is intrinsically linked to the agent's performance in the environment it must navigate. All environments used for training the network are provided by the Grando et al [29]. However, some changes are made in the source code of the Gazebo simulation to use the camera of the simulated UAV.…”
Section: Resultsmentioning
confidence: 99%
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“…This information allows the estimation of the agent's degree of learning in the environment, as the improved in terms of reward is intrinsically linked to the agent's performance in the environment it must navigate. All environments used for training the network are provided by the Grando et al [29]. However, some changes are made in the source code of the Gazebo simulation to use the camera of the simulated UAV.…”
Section: Resultsmentioning
confidence: 99%
“…The study around mapless navigation is extensively explored using terrestrial mobile robots [3]. Autonomous navigation of aerial mobile robots using Deep-RL approaches is less frequent and mainly focused on approaches that avoid the use of visual information [6], [10] or using simplified information, without contrastive learning [7]- [9], [13].…”
Section: Related Workmentioning
confidence: 99%
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“…Mapless planners [3][4][5]7] only employ a local map that is just enough for local planning, and a memoryless one [6] plans directly on sensor data. Distinguishing from those, map-based planning systems [8,9] generally integrate global maps [10,11] a priori and a global planning algorithm such as RRT* [12] to guarantee planning completeness in tasks such as exploration, [13][14][15][16][17] or navigating toward a goal [18][19][20]. All mapless and memoryless planners cannot access any map but use sensor data.…”
Section: Introductionmentioning
confidence: 99%