3 citations · 3 across the 8 of their papers we have counts for
8 papers
Decoupling Task and Behavior: A Two-Stage Reward Curriculum in Reinforcement Learning for Robotics
Kilian Freitag, Knut Åkesson, Morteza Haghir Chehreghani
Deep Reinforcement Learning is a promising tool for robotic control, yet practical application is often hindered by the difficulty of designing effective reward functions. Real-wor…
Proactive Local-Minima-Free Robot Navigation: Blending Motion Prediction with Safe Control
Yifan Xue, Ze Zhang, Knut Åkesson +1
This work addresses the challenge of safe and efficient mobile robot navigation in complex dynamic environments with concave moving obstacles. Reactive safe controllers like Contro…
Infrastructure-based Autonomous Mobile Robots for Internal Logistics -- Challenges and Future Perspectives
Erik Brorsson, Kristian Ceder, Ze Zhang +11
The adoption of Autonomous Mobile Robots (AMRs) for internal logistics is accelerating, with most solutions emphasizing decentralized, onboard intelligence. While AMRs in indoor en…
Combining High Level Scheduling and Low Level Control to Manage Fleets of Mobile Robots
Sabino Francesco Roselli, Ze Zhang, Knut Åkesson
The deployment of mobile robots for material handling in industrial environments requires scalable coordination of large fleets in dynamic settings. This paper presents a two-layer…
Collision-Free Navigation of Mobile Robots via Quadtree-Based Model Predictive Control
Osama Al Sheikh Ali, Sotiris Koutsoftas, Ze Zhang +2
This paper presents an integrated navigation framework for Autonomous Mobile Robots (AMRs) that unifies environment representation, trajectory generation, and Model Predictive Cont…
Gradient Field-Based Dynamic Window Approach for Collision Avoidance in Complex Environments
Ze Zhang, Yifan Xue, Nadia Figueroa +1
For safe and flexible navigation in multi-robot systems, this paper presents an enhanced and predictive sampling-based trajectory planning approach in complex environments, the Gra…
MVUDA: Unsupervised Domain Adaptation for Multi-view Pedestrian Detection
Erik Brorsson, Lennart Svensson, Kristofer Bengtsson +1
We address multi-view pedestrian detection in a setting where labeled data is collected using a multi-camera setup different from the one used for testing. While recent multi-view…
ECAP: Extensive Cut-and-Paste Augmentation for Unsupervised Domain Adaptive Semantic Segmentation
Erik Brorsson, Knut Åkesson, Lennart Svensson +1
We consider unsupervised domain adaptation (UDA) for semantic segmentation in which the model is trained on a labeled source dataset and adapted to an unlabeled target dataset. Unf…