Zobrazeno 1 - 10
of 275
pro vyhledávání: '"Van, Mien"'
Reinforcement Learning (RL) or Deep Reinforcement Learning (DRL) is a powerful approach to solving Markov Decision Processes (MDPs) when the model of the environment is not known a priori. However, RL models are still faced with challenges such as ha
Externí odkaz:
http://arxiv.org/abs/2406.00732
Advancements in underwater vehicle technology have significantly expanded the potential scope for deploying autonomous or remotely operated underwater vehicles in novel practical applications. However, the efficiency and maneuverability of these vehi
Externí odkaz:
http://arxiv.org/abs/2403.15671
Autor:
Olayemi, Kabirat, Van, Mien, McLoone, Sean, Sun, Yuzhu, Close, Jack, Nhat, Nguyen Minh, McIlvanna, Stephen
Autonomous ground vehicle (UGV) navigation has the potential to revolutionize the transportation system by increasing accessibility to disabled people, ensure safety and convenience of use. However, UGV requires extensive and efficient testing and ev
Externí odkaz:
http://arxiv.org/abs/2403.15067
Autor:
Sun, Yuzhu, Van, Mien, McIlvanna, Stephen, Nhat, Nguyen Minh, Olayemi, Kabirat, Close, Jack, McLoone, Seán
The evolution and growing automation of collaborative robots introduce more complexity and unpredictability to systems, highlighting the crucial need for robot's adaptability and flexibility to address the increasing complexities of their environment
Externí odkaz:
http://arxiv.org/abs/2403.13090
To enable safe and effective human-robot collaboration (HRC) in smart manufacturing, seamless integration of sensing, cognition, and prediction into the robot controller is critical for real-time awareness, response, and communication inside a hetero
Externí odkaz:
http://arxiv.org/abs/2304.06923
In advanced manufacturing, strict safety guarantees are required to allow humans and robots to work together in a shared workspace. One of the challenges in this application field is the variety and unpredictability of human behavior, leading to pote
Externí odkaz:
http://arxiv.org/abs/2304.06867
Autor:
Sun, Yuzhu, Van, Mien, McIlvanna, Stephen, Nhat, Nguyen Minh, McLoone, Sean, Ceglarek, Dariusz, Ge, Shuzhi Sam
Physical human-robot collaboration (pHRC) requires both compliance and safety guarantees since robots coordinate with human actions in a shared workspace. This paper presents a novel fixed-time adaptive neural control methodology for handling time-va
Externí odkaz:
http://arxiv.org/abs/2303.02456
Autor:
Van, Mien, Sun, Yuzhu, Mcllvanna, Stephen, Nguyen, Minh-Nhat, Zocco, Federico, Liu, Zhijie, Wang, Hsueh-Cheng
This study proposes a new distributed control method based on an adaptive fuzzy control for multiple collaborative autonomous underwater vehicles (AUVs) to track a desired formation shape within a fixed time. First, a formation control protocol based
Externí odkaz:
http://arxiv.org/abs/2302.14162
While the concept of a digital twin to support maritime operations is gaining attention for predictive maintenance, real-time monitoring, control, and overall process optimization, clarity on its implementation is missing in the literature. Therefore
Externí odkaz:
http://arxiv.org/abs/2301.09574
In this paper we present the implementation of a Control Barrier Function (CBF) using a quadratic program (QP) formulation that provides obstacle avoidance for a robotic manipulator arm system. CBF is a control technique that has emerged and develope
Externí odkaz:
http://arxiv.org/abs/2211.11391