2 papers
cs.RO2021
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…
cs.RO2020
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…