6 citations · 7 across the 6 of their papers we have counts for
5 papers · 1 filter
Optimized Coordination Strategy for Multi-Aerospace Systems in Pick-and-Place Tasks By Deep Neural Network
Ye Zhang, Linyue Chu, Letian Xu +3
In this paper, we present an advanced strategy for the coordinated control of a multi-agent aerospace system, utilizing Deep Neural Networks (DNNs) within a reinforcement learning…
Maximum Solar Energy Tracking Leverage High-DoF Robotics System with Deep Reinforcement Learning
Anjie Jiang, Kangtong Mo, Satoshi Fujimoto +4
Solar trajectory monitoring is a pivotal challenge in solar energy systems, underpinning applications such as autonomous energy harvesting and environmental sensing. A prevalent fa…
DRAL: Deep Reinforcement Adaptive Learning for Multi-UAVs Navigation in Unknown Indoor Environment
Kangtong Mo, Linyue Chu, Xingyu Zhang +4
Autonomous indoor navigation of UAVs presents numerous challenges, primarily due to the limited precision of GPS in enclosed environments. Additionally, UAVs' limited capacity to c…
Self-Adaptive Robust Motion Planning for High DoF Robot Manipulator using Deep MPC
Ye Zhang, Kangtong Mo, Fangzhou Shen +4
In contemporary control theory, self-adaptive methodologies are highly esteemed for their inherent flexibility and robustness in managing modeling uncertainties. Particularly, robu…
Fine-Tuning Gemma-7B for Enhanced Sentiment Analysis of Financial News Headlines
Kangtong Mo, Wenyan Liu, Xuanzhen Xu +3
In this study, we explore the application of sentiment analysis on financial news headlines to understand investor sentiment. By leveraging Natural Language Processing (NLP) and La…