5 papers · 1 filter
Enhancing Navigational Safety in Crowded Environments using Semantic-Deep-Reinforcement-Learning-based Navigation
Linh Kästner, Junhui Li, Zhengcheng Shen +1
Intelligent navigation among social crowds is an essential aspect of mobile robotics for applications such as delivery, health care, or assistance. Deep Reinforcement Learning emer…
Photothermal-SR-Net: A Customized Deep Unfolding Neural Network for Photothermal Super Resolution Imaging
Samim Ahmadi, Linh Kästner, Jan Christian Hauffen +2
This paper presents deep unfolding neural networks to handle inverse problems in photothermal radiometry enabling super resolution (SR) imaging. Photothermal imaging is a well-know…
Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint Generators
Linh Kästner, Teham Buiyan, Xinlin Zhao +3
Deep Reinforcement Learning has emerged as an efficient dynamic obstacle avoidance method in highly dynamic environments. It has the potential to replace overly conservative or ine…
Spatial Imagination With Semantic Cognition for Mobile Robots
Zhengcheng Shen, Linh Kästner, Jens Lambrecht
The imagination of the surrounding environment based on experience and semantic cognition has great potential to extend the limited observations and provide more information for ma…
Arena-Rosnav: Towards Deployment of Deep-Reinforcement-Learning-Based Obstacle Avoidance into Conventional Autonomous Navigation Systems
Linh Kästner, Teham Buiyan, Xinlin Zhao +3
Recently, mobile robots have become important tools in various industries, especially in logistics. Deep reinforcement learning emerged as an alternative planning method to replace…