5 papers
Rule-based High-Level Coaching for Goal-Conditioned Reinforcement Learning in Search-and-Rescue UAV Missions Under Limited-Simulation Training
Mahya Ramezani, Holger Voos
This paper presents a hierarchical decision-making framework for unmanned aerial vehicle (UAV) missions motivated by search-and-rescue (SAR) scenarios under limited simulation trai…
MPC-based Deep Reinforcement Learning Method for Space Robotic Control with Fuel Sloshing Mitigation
Mahya Ramezani, M. Amin Alandihallaj, Barış Can Yalçın +2
This paper presents an integrated Reinforcement Learning (RL) and Model Predictive Control (MPC) framework for autonomous satellite docking with a partially filled fuel tank. Tradi…
Barrier Method for Inequality Constrained Factor Graph Optimization with Application to Model Predictive Control
Anas Abdelkarim, Holger Voos, Daniel Görges
Factor graphs have demonstrated remarkable efficiency for robotic perception tasks, particularly in localization and mapping applications. However, their application to optimal con…
ecg2o: A Seamless Extension of g2o for Equality-Constrained Factor Graph Optimization
Anas Abdelkarim, Holger Voos, Daniel Görges
Factor graph optimization serves as a fundamental framework for robotic perception, enabling applications such as pose estimation, simultaneous localization and mapping (SLAM), str…
Human-Centric Aware UAV Trajectory Planning in Search and Rescue Missions Employing Multi-Objective Reinforcement Learning with AHP and Similarity-Based Experience Replay
Mahya Ramezani, Jose Luis Sanchez-Lopez
The integration of Unmanned Aerial Vehicles (UAVs) into Search and Rescue (SAR) missions presents a promising avenue for enhancing operational efficiency and effectiveness. However…