6 papers
A Reward-Petri-Net Interpretation of Temporal Behavior Trees
Till Schmeil, Günther Waxenegger-Wilfing, Sebastian Schirmer
This paper introduces an interpretation of Temporal Behavior Trees (TBTs) as Reward-Petri-Nets (RPNs) for reinforcement learning (RL). Designing reward functions for complex, long-…
Optimal Multi-Debris Mission Planning in LEO: A Deep Reinforcement Learning Approach with Co-Elliptic Transfers and Refueling
Agni Bandyopadhyay, Gunther Waxenegger-Wilfing
This paper addresses the challenge of multi target active debris removal (ADR) in Low Earth Orbit (LEO) by introducing a unified coelliptic maneuver framework that combines Hohmann…
Evaluating Robustness and Adaptability in Learning-Based Mission Planning for Active Debris Removal
Agni Bandyopadhyay, Günther Waxenegger-Wilfing
Autonomous mission planning for Active Debris Removal (ADR) must balance efficiency, adaptability, and strict feasibility constraints on fuel and mission duration. This work compar…
Optimizing Mission Planning for Multi-Debris Rendezvous Using Reinforcement Learning with Refueling and Adaptive Collision Avoidance
Agni Bandyopadhyay, Gunther Waxenegger-Wilfing
As the orbital environment around Earth becomes increasingly crowded with debris, active debris removal (ADR) missions face significant challenges in ensuring safe operations while…
Revisiting Space Mission Planning: A Reinforcement Learning-Guided Approach for Multi-Debris Rendezvous
Agni Bandyopadhyay, Guenther Waxenegger-Wilfing
This research introduces a novel application of a masked Proximal Policy Optimization (PPO) algorithm from the field of deep reinforcement learning (RL), for determining the most e…
Forecasting Thermoacoustic Instabilities in Liquid Propellant Rocket Engines Using Multimodal Bayesian Deep Learning
Ushnish Sengupta, Günther Waxenegger-Wilfing, Jan Martin +2
The 100 MW cryogenic liquid oxygen/hydrogen multi-injector combustor BKD operated by the DLR Institute of Space Propulsion is a research platform that allows the study of thermoaco…