38 citations · 81 across the 17 of their papers we have counts for
23 papers
Arena 3.0: Advancing Social Navigation in Collaborative and Highly Dynamic Environments
Linh Kästner, Volodymyir Shcherbyna, Huajian Zeng +8
Building upon our previous contributions, this paper introduces Arena 3.0, an extension of Arena-Bench, Arena 1.0, and Arena 2.0. Arena 3.0 is a comprehensive software stack contai…
Mono Video-Based AI Corridor for Model-Free Detection of Collision-Relevant Obstacles
Thomas Michalke, Yassin Kaddar, Thomas Nürnberg +2
The detection of previously unseen, unexpected obstacles on the road is a major challenge for automated driving systems. Different from the detection of ordinary objects with pre-d…
HabitatDyn Dataset: Dynamic Object Detection to Kinematics Estimation
Zhengcheng Shen, Yi Gao, Linh Kästner +1
The advancement of computer vision and machine learning has made datasets a crucial element for further research and applications. However, the creation and development of robots w…
Holistic Deep-Reinforcement-Learning-based Training of Autonomous Navigation Systems
Linh Kästner, Marvin Meusel, Teham Bhuiyan +1
In recent years, Deep Reinforcement Learning emerged as a promising approach for autonomous navigation of ground vehicles and has been utilized in various areas of navigation such…
Arena-Web -- A Web-based Development and Benchmarking Platform for Autonomous Navigation Approaches
Linh Kästner, Reyk Carstens, Christopher Liebig +4
In recent years, mobile robot navigation approaches have become increasingly important due to various application areas ranging from healthcare to warehouse logistics. In particula…
Arena-Rosnav 2.0: A Development and Benchmarking Platform for Robot Navigation in Highly Dynamic Environments
Linh Kästner, Reyk Carstens, Huajian Zeng +5
Following up on our previous works, in this paper, we present Arena-Rosnav 2.0 an extension to our previous works Arena-Bench and Arena-Rosnav, which adds a variety of additional m…
Deep-Reinforcement-Learning-based Path Planning for Industrial Robots using Distance Sensors as Observation
Teham Bhuiyan, Linh Kästner, Yifan Hu +2
Industrial robots are widely used in various manufacturing environments due to their efficiency in doing repetitive tasks such as assembly or welding. A common problem for these ap…
Evaluating Digital Work Instructions with Augmented Reality versus Paper-based Documents for Manual, Object-Specific Repair Tasks in a Case Study with Experienced Workers
Leon Eversberg, Jens Lambrecht
Manual repair tasks in the industry of maintenance, repair, and overhaul require experience and object-specific information. Today, many of these repair tasks are still performed a…
Arena-Bench: A Benchmarking Suite for Obstacle Avoidance Approaches in Highly Dynamic Environments
Linh Kästner, Teham Bhuiyan, Tuan Anh Le +9
The ability to autonomously navigate safely, especially within dynamic environments, is paramount for mobile robotics. In recent years, DRL approaches have shown superior performan…
Imagination-augmented Navigation Based on 2D Laser Sensor Observations
Zhengcheng Shen, Linh Kästner, Magdalena Yordanova +1
Autonomous navigation of mobile robots is an essential task for various industries. Sensor data is crucial to ensure safe and reliable navigation. However, sensor observations are…
Human-Following and -guiding in Crowded Environments using Semantic Deep-Reinforcement-Learning for Mobile Service Robots
Linh Kästner, Bassel Fatloun, Zhengcheng Shen +2
Assistance robots have gained widespread attention in various industries such as logistics and human assistance. The tasks of guiding or following a human in a crowded environment…
Obstacle-aware Waypoint Generation for Long-range Guidance of Deep-Reinforcement-Learning-based Navigation Approaches
Linh Kästner, Xinlin Zhao, Zhengcheng Shen +1
Navigation of mobile robots within crowded environments is an essential task in various use cases, such as delivery, health care, or logistics. Deep Reinforcement Learning (DRL) em…
All-in-One: A DRL-based Control Switch Combining State-of-the-art Navigation Planners
Linh Kästner, Johannes Cox, Teham Buiyan +1
Autonomous navigation of mobile robots is an essential aspect in use cases such as delivery, assistance or logistics. Although traditional planning methods are well integrated into…
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…
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…
Classification of Spot-welded Joints in Laser Thermography Data using Convolutional Neural Networks
Linh Kästner, Samim Ahmadi, Florian Jonietz +4
Spot welding is a crucial process step in various industries. However, classification of spot welding quality is still a tedious process due to the complexity and sensitivity of th…
Integrative Object and Pose to Task Detection for an Augmented-Reality-based Human Assistance System using Neural Networks
Linh Kästner, Leon Eversberg, Marina Mursa +1
As a result of an increasingly automatized and digitized industry, processes are becoming more complex. Augmented Reality has shown considerable potential in assisting workers with…
Deep-Reinforcement-Learning-Based Semantic Navigation of Mobile Robots in Dynamic Environments
Linh Kästner, Cornelius Marx, Jens Lambrecht
Mobile robots have gained increased importance within industrial tasks such as commissioning, delivery or operation in hazardous environments. The ability to autonomously navigate…
A Markerless Deep Learning-based 6 Degrees of Freedom PoseEstimation for with Mobile Robots using RGB Data
Linh Kästner, Daniel Dimitrov, Jens Lambrecht
Augmented Reality has been subject to various integration efforts within industries due to its ability to enhance human machine interaction and understanding. Neural networks have…
Augmented-Reality-Based Visualization of Navigation Data of Mobile Robots on the Microsoft Hololens -- Possibilities and Limitations
Linh Kästner, Jens Lambrecht
The demand for mobile robots has rapidly increased in recent years due to the flexibility and high variety of application fields comparing to static robots. To deal with complex ta…
A 3D-Deep-Learning-based Augmented Reality Calibration Method for Robotic Environments using Depth Sensor Data
Linh Kästner, Vlad Catalin Frasineanu, Jens Lambrecht
Augmented Reality and mobile robots are gaining much attention within industries due to the high potential to make processes cost and time efficient. To facilitate augmented realit…