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Found 14 papers, 7 papers with code
Arena-Rosnav 2.0: A Development and Benchmarking Platform for Robot Navigation in Highly Dynamic Environments
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 modules for developing and benchmarking robotic navigation approaches.
Arena-Web -- A Web-based Development and Benchmarking Platform for Autonomous Navigation Approaches
In recent years, efforts have been made to make these sophisticated approaches accessible to a wider audience.
Holistic Deep-Reinforcement-Learning-based Training of Autonomous Navigation Systems
This however, comes along with a number of problems such as catastrophic forgetfulness, inefficient navigation behavior, and non-optimal synchronization between different entities of the navigation stack.
Deep-Reinforcement-Learning-based Path Planning for Industrial Robots using Distance Sensors as Observation
Industrial robots are widely used in various manufacturing environments due to their efficiency in doing repetitive tasks such as assembly or welding.
Enhancing Navigational Safety in Crowded Environments using Semantic-Deep-Reinforcement-Learning-based Navigation
In this paper, we propose a semantic Deep-reinforcement-learning-based navigation approach that teaches object-specific safety rules by considering high-level obstacle information.
Spatial Imagination With Semantic Cognition for Mobile Robots
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 mapping, collision avoidance, and path planning.
Arena-Rosnav: Towards Deployment of Deep-Reinforcement-Learning-Based Obstacle Avoidance into Conventional Autonomous Navigation Systems
Recently, mobile robots have become important tools in various industries, especially in logistics.
Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint Generators
Deep Reinforcement Learning has emerged as an efficient dynamic obstacle avoidance method in highly dynamic environments.
Classification of Spot-welded Joints in Laser Thermography Data using Convolutional Neural Networks
Spot welding is a crucial process step in various industries.
Integrative Object and Pose to Task Detection for an Augmented-Reality-based Human Assistance System using Neural Networks
As a result of an increasingly automatized and digitized industry, processes are becoming more complex.
Deep-Reinforcement-Learning-Based Semantic Navigation of Mobile Robots in Dynamic Environments
Visual based navigation and high level semantics bear the potential to enhance the safety of path planing by creating links the agent can reason about for a more flexible navigation.
Robotics
A Markerless Deep Learning-based 6 Degrees of Freedom PoseEstimation for with Mobile Robots using RGB Data
In this work we propose a method to deploy state of the art neural networks for real time 3D object localization on augmented reality devices.
A 3D-Deep-Learning-based Augmented Reality Calibration Method for Robotic Environments using Depth Sensor Data
The results are adaptable to work with all depth cameras and are promising for further research.
