Greetings!
Welcome to my personal page. I just finished my PhD in the Group of Automation at the Chalmers University of Technology, supervised by Prof. Knut Åkesson. My research interests are in the area of mobile robotics and deep learning, particularly in the development of integrated algorithms for predictive dynamic obstacle avoidance based on multimodal motion prediction. If you are interested in my work, please feel free to contact me or raise an issue on the corresponding GitHub repository.
The latest official update of the application related to my research project: Volvo Group
I sucessfully defended my thesis on May 15, 2025. The title of my thesis is “Future-Oriented Navigation for Autonomous Mobile Robots”.
Active Projects

Dynamic Nonconvex Obstacle Avoidance: One-shot Motion Prediction (EBM) and Control (On-Manifold CBF)
Under review (collaboration with GRASP lab, Univerisity of Pennsylvania) [Code (Not yet)] [Paper (Not yet)] [Video-Demo]
Not yet

Complex Obstacle Avoidance and Fleet Collision Avoidance: Sampling-based Trajectory Planning (DWA) Inspried by Gradient Field (GPDF)
IROS 2025 - Hangzhou, CN [Code (Not yet)] [Paper (Not yet)] [Video-Demo]
Gradient Field-Based Dynamic Window Approach for Collision Avoidance in Complex Environments | Non-convex obstacle avoidance | Multi-robot collision avoidance

Dynamic Obstacle Avoidance: One-shot Motion Prediction (EBM) and Control (MPC)
RAL 2025 [Code] [ROS 2] [Paper] [Video-Demo]
Future-Oriented Navigation: Dynamic Obstacle Avoidance with One-Shot Energy-Based Multimodal Motion Prediction | Autonomous Mobile Robot | Energy-Based Multimodal Motion Prediction

Efficient Obstacle Avoidance and Multi-Agent Coordination: DDPG-Boosted MPC for Collision-Free Navigation
IROS 2024 (Abu Dhabi, UAE) [Code] [Paper] [Video-Demo]
Bird’s-Eye-View Trajectory Planning of Multiple Robots using Continuous Deep Reinforcement Learning and Model Predictive Control | Deep Reinforcement Learning | Multi-Agent Coordination

Dynamic Obstacle Avoidance: Iterative Motion Prediction (SWTA) and Control (MPC)
RAL 2023 - Yokohama, JPN (ICRA 2024) [Code] [Paper] [Video-Demo]
[Deprecation soon] Prescient Collision-Free Navigation of Mobile Robots With Iterative Multimodal Motion Prediction of Dynamic Obstacles | Autonomous Mobile Robot | Multimodal Motion Prediction
Previous Projects



Dynamic Obstacle Avoidance: MDN Predictor and MPC
CASE 2021 (Virtual/Lyon, FR) [Code] [Paper] [Video-Demo]
Motion Prediction Based on Multiple Futures for Dynamic Obstacle Avoidance of Mobile Robots | Mixture Density Network