@article{SalamatElsbacherTonello2025, author = {Salamat, Babak and Elsbacher, Gerhard and Tonello, Andrea M.}, title = {Energy Shaping Control in Underactuated Robot Systems with Underactuation Degree Two}, volume = {10}, journal = {IEEE Robotics and Automation Letters}, number = {3}, publisher = {IEEE}, address = {New York}, issn = {2377-3766}, doi = {https://doi.org/10.1109/LRA.2025.3534688}, pages = {2734 -- 2741}, year = {2025}, language = {en} } @article{SalamatElsbacherTonelloetal.2023, author = {Salamat, Babak and Elsbacher, Gerhard and Tonello, Andrea M. and Belzner, Lenz}, title = {Model-Free Distributed Reinforcement Learning State Estimation of a Dynamical System Using Integral Value Functions}, volume = {2}, journal = {IEEE Open Journal of Control Systems}, publisher = {IEEE}, address = {Piscataway}, issn = {2694-085X}, doi = {https://doi.org/10.1109/OJCSYS.2023.3250089}, pages = {70 -- 78}, year = {2023}, language = {en} } @article{SalamatYaghmaeiElsbacheretal.2023, author = {Salamat, Babak and Yaghmaei, Abolfazl and Elsbacher, Gerhard and Tonello, Andrea M. and Yazdanpanah, Mohammad Javad}, title = {An Innovative Control Design Procedure for Under-Actuated Mechanical Systems: Emphasizing Potential Energy Shaping and Structural Preservation}, volume = {2}, journal = {IEEE Open Journal of Control Systems}, publisher = {IEEE}, address = {New York}, issn = {2694-085X}, doi = {https://doi.org/10.1109/OJCSYS.2023.3320512}, pages = {356 -- 365}, year = {2023}, abstract = {In this paper, we propose a procedure to solve the controlled design for a class of underactuated mechanical systems. Our proposed method can be viewed as a sub-method of the IDA-PBC or Controlled Lagrangian approaches, with a particular focus on shaping the potential energy. By emphasizing potential energy shaping, we can effectively tackle the bottleneck presented by the matching equation in these approaches. Moreover, our method leverages a suitable coordinate transformation that is inspired by the physics of the system, further enhancing its efficacy. Therefore, our design procedure is based on a coordinate transformation plus potential energy shaping in the new coordinates, and its existence and possibility of potential energy shaping can be verified via some algebraic calculations, making it constructive. To illustrate the results, we consider the cart-pole system and a recently introduced underactuated mechanical system named swash mass pendulum (SMP) [1]. The SMP consists of a pendulum made of a rigid shaft connected to a pair of cross-shafts where two swash masses can move under the action of servo-mechanisms.}, language = {en} } @article{SalamatOlzemElsbacheretal.2024, author = {Salamat, Babak and Olzem, Sebastian-Sven and Elsbacher, Gerhard and Tonello, Andrea M.}, title = {Global Multi-Phase Path Planning Through High-Level Reinforcement Learning}, volume = {3}, journal = {IEEE Open Journal of Control Systems}, publisher = {IEEE}, address = {Piscataway}, issn = {2694-085X}, doi = {https://doi.org/10.1109/OJCSYS.2024.3435080}, pages = {405 -- 415}, year = {2024}, abstract = {In this paper, we introduce the Global Multi-Phase Path Planning ( GMP3 ) algorithm in planner problems, which computes fast and feasible trajectories in environments with obstacles, considering physical and kinematic constraints. Our approach utilizes a Markov Decision Process (MDP) framework and high-level reinforcement learning techniques to ensure trajectory smoothness, continuity, and compliance with constraints. Through extensive simulations, we demonstrate the algorithm's effectiveness and efficiency across various scenarios. We highlight existing path planning challenges, particularly in integrating dynamic adaptability and computational efficiency. The results validate our method's convergence guarantees using Lyapunov's stability theorem and underscore its computational advantages.}, language = {en} } @article{SalamatBencicElsbacheretal.2023, author = {Salamat, Babak and Bencic, Daniel and Elsbacher, Gerhard and Seidel, Christian and Tonello, Andrea M.}, title = {Investigating Integral Reinforcement Learning to Achieve Asymptotic Stability in Underactuated Mechanical Systems}, volume = {9}, journal = {IEEE Robotics and Automation Letters}, number = {1}, publisher = {IEEE}, address = {New York}, issn = {2377-3766}, doi = {https://doi.org/10.1109/LRA.2023.3332556}, pages = {191 -- 198}, year = {2023}, language = {en} } @unpublished{SalamatMatternOlzemetal.2025, author = {Salamat, Babak and Mattern, Dominik and Olzem, Sebastian-Sven and Elsbacher, Gerhard and Seidel, Christian and Tonello, Andrea M.}, title = {GMP3: Learning-Driven, Bellman-Guided Trajectory Planning for UAVs in Real-Time on SE(3)}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2509.21264}, year = {2025}, abstract = {We propose GMP3, a multiphase global path planning framework that generates dynamically feasible three-dimensional trajectories for unmanned aerial vehicles (UAVs) operating in cluttered environments. The framework extends traditional path planning from Euclidean position spaces to the Lie group SE(3), allowing joint learning of translational motion and rotational dynamics. A modified Bellman-based operator is introduced to support reinforcement learning (RL) policy updates while leveraging prior trajectory information for improved convergence. GMP3 is designed as a distributed framework in which agents influence each other and share policy information along the trajectory: each agent refines its assigned segment and shares with its neighbors via a consensus-based scheme, enabling cooperative policy updates and convergence toward a path shaped globally even under kinematic constraints. We also propose DroneManager, a modular ground control software that interfaces the planner with real UAV platforms via the MAVLink protocol, supporting real-time deployment and feedback. Simulation studies and indoor flight experiments validate the effectiveness of the proposed method in constrained 3D environments, demonstrating reliable obstacle avoidance and smooth, feasible trajectories across both position and orientation. The open-source implementation is available at https://github.com/Domattee/DroneManager.}, language = {en} } @article{SalamatMatternOlzemetal.2026, author = {Salamat, Babak and Mattern, Dominik and Olzem, Sebastian-Sven and Elsbacher, Gerhard and Seidel, Christian and Tonello, Andrea M.}, title = {GMP3: Learning-Driven, Bellman-Guided Trajectory Planning for AAVs in Real-Time on SE(3)}, volume = {14}, journal = {IEEE Access}, publisher = {IEEE}, address = {New York}, issn = {2169-3536}, doi = {https://doi.org/10.1109/ACCESS.2026.3661193}, pages = {22720 -- 22733}, year = {2026}, abstract = {We propose GMP3, a multiphase global path planning framework that generates dynamically feasible three-dimensional trajectories for unmanned aerial vehicles (UAVs) operating in cluttered environments. The framework extends traditional path planning from Euclidean position spaces to the Lie group SE(3), allowing joint learning of translational motion and rotational dynamics. A modified Bellman-based operator is introduced to support reinforcement learning (RL) policy updates while leveraging prior trajectory information for improved convergence. GMP3 is designed as a distributed framework in which agents influence each other and share policy information along the trajectory: each agent refines its assigned segment and shares with its neighbors via a consensus-based scheme, enabling cooperative policy updates and convergence toward a path shaped globally even under kinematic constraints.We also propose DroneManager, a modular ground control software that interfaces the planner with real UAVs platforms via the Micro Air Vehicle Link (MAVLink) protocol, supporting real-time deployment and feedback. Simulation studies and indoor flight experiments validate the effectiveness of the proposed method in constrained 3D environments, demonstrating reliable obstacle avoidance and smooth, feasible trajectories across both position and orientation.}, language = {en} }