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Junlin Lou, Burak Yuksek, Gokhan Inalhan and Antonios Tsourdos
In this study, we consider the problem of motion planning for urban air mobility applications to generate a minimal snap trajectory and trajectory that cost minimal time to reach a goal location in the presence of dynamic geo-fences and uncertainties in ...
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Shui Jiang, Yanning Ge, Xu Yang, Wencheng Yang and Hui Cui
Reinforcement learning (RL) is pivotal in empowering Unmanned Aerial Vehicles (UAVs) to navigate and make decisions efficiently and intelligently within complex and dynamic surroundings. Despite its significance, RL is hampered by inherent limitations su...
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Di Wang and Haizhong Qian
Existing research on automatic river network classification methods has difficulty scientifically quantifying and determining feature threshold settings and evaluating weights when calculating multi-indicator features of the local and overall structures ...
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Nagyum Kim, Cheolpyo Hong, Changwoo Lee and Hyo-Min Cho
The purpose of this study was to create a Doppler ultrasound training phantom aimed at aiding beginners in comprehending and effectively utilizing critical parameters during the learning process. Our designed training phantom does not require the use of ...
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Baris Eren Perk and Gokhan Inalhan
To control unmanned aerial systems, we rarely have a perfect system model. Safe and aggressive planning is also challenging for nonlinear and under-actuated systems. Expert pilots, however, demonstrate maneuvers that are deemed at the edge of plane envel...
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Yubing Mao, Farong Gao, Qizhong Zhang and Zhangyi Yang
This study aims to solve the problem of sparse reward and local convergence when using a reinforcement learning algorithm as the controller of an AUV. Based on the generative adversarial imitation (GAIL) algorithm combined with a multi-agent, a multi-age...
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Lei He, Jian Ou, Mingyue Ba, Guohong Deng and Echuan Yang
Autonomous urban driving navigation is still an open problem and has ample room for improvement in unknown complex environments. This paper proposes an end-to-end autonomous driving approach that combines Conditional Imitation Learning (CIL), Mask R-CNN ...
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Janis Arents and Modris Greitans
Industrial robots and associated control methods are continuously developing. With the recent progress in the field of artificial intelligence, new perspectives in industrial robot control strategies have emerged, and prospects towards cognitive robots h...
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Zaynab Almutairi and Hebah Elgibreen
A number of AI-generated tools are used today to clone human voices, leading to a new technology known as Audio Deepfakes (ADs). Despite being introduced to enhance human lives as audiobooks, ADs have been used to disrupt public safety. ADs have thus rec...
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Kaan Yilmaz and Neil Yorke-Smith
In line with the growing trend of using machine learning to help solve combinatorial optimisation problems, one promising idea is to improve node selection within a mixed integer programming (MIP) branch-and-bound tree by using a learned policy. Previous...
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