A5024877013- Quantum Information and Cryptography
- Adaptive Control of Nonlinear Systems
- Quantum Computing Algorithms and Architecture
- Neural Networks and Reservoir Computing
- Hydraulic and Pneumatic Systems
- Blockchain Technology Applications and Security
- Target Tracking and Data Fusion in Sensor Networks
- UAV Applications and Optimization
- Control Systems in Engineering
- Autonomous Vehicle Technology and Safety
- Robotic Path Planning Algorithms
- Distributed Control Multi-Agent Systems
- Video Surveillance and Tracking Methods
- Reinforcement Learning in Robotics
- Cryptography and Data Security
- Guidance and Control Systems
- Vehicle Dynamics and Control Systems
Korea University
2024-2025
This paper presents a novel approach for enhancing autonomous drone mobility control using deep reinforcement learning (DRL), primarily aimed at improving navigation in challenging environments. Our research tackles the significant issue of real-time obstacle avoidance, critical aspect control. is achieved through integration sensing-aware nonlinear mechanisms, facilitating advanced trajectory optimization. A notable contribution our work incorporation human-in-the-loop feedback...
Multi-agent reinforcement learning (MARL) algorithms have been widely used for many applications requiring sequential decision-making to maximize the expected rewards through multi-agent cooperation. However, MARL faces significant challenges, particularly in resource-limited real-time computing environments. To tackle this problem, paper considers selection of agents training which can be beneficial terms computation-overhead reduction. For selection, a farthest agent (FAS) is proposed,...