@article{SalamatElsbacher2022, author = {Salamat, Babak and Elsbacher, Gerhard}, title = {Centralized Control in Networks of Underactuated Nonidentical Euler-Lagrange Systems Using a Generalised Multicoordinates Transformation}, volume = {10}, journal = {IEEE Access}, publisher = {IEEE}, address = {New York}, issn = {2169-3536}, doi = {https://doi.org/10.1109/ACCESS.2022.3178606}, pages = {58311 -- 58319}, year = {2022}, abstract = {Controlling the network of underactuated Euler-Lagrange (EL) systems is challenging because of their coupled inertia matrices and time-variant control input matrices. We present generalized multi-coordinates transformation that renders the network of underactuated Euler-Lagrange dynamics in particular forms, whose mechanical properties should be preserved. The network of N nonidentical Euler-Lagrange EL-systems is modeled as a weighted interconnection graph where each EL-system is a node, and the control action at each node is a function of its state and the states of its neighbors. Second, we propose an online optimally centralized control mechanism with the prime objective of energy efficiency. The result is applied to the network of underactuated vertical takeoff and landing aircraft with strong input coupling, including the effect of the weight of the rotors into the dynamical system models. In this regard, we obtain very simple and powerful state-feedback solutions.}, language = {en} } @article{SalamatElsbacher2022, author = {Salamat, Babak and Elsbacher, Gerhard}, title = {Steering a Swarm of Large-Scale Underactuated Mechanical Systems Using a Generalized Coordinates Transformation}, volume = {9}, pages = {702}, journal = {Aerospace}, number = {11}, publisher = {MDPI}, address = {Basel}, issn = {2226-4310}, doi = {https://doi.org/10.3390/aerospace9110702}, year = {2022}, abstract = {Steering large-scale particle or robot systems is challenging because of their high dimensionality. We use a centralized stochastic approach that allows for optimal control at the cost of a central element instead of a decentralized approach. Previous works are often restricted to the assumption of fully actuated robots. Here we propose an approach for underactuated robots that allows for energy-efficient control of the robot system. We consider a simple task of gathering the robots (minimizing positional variance) and steering them towards a goal point within a bounded area without obstacles. We make two main contributions. First, we present a generalized coordinate transformation for underactuated robots, whose physical properties should be considered. We choose Euler-Lagrange systems that describe a large class of robot systems. Second, we propose an optimal control mechanism with the prime objective of energy efficiency. We show the feasibility of our approach in robot simulations.}, language = {en} } @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{ZieherOlcayKefferpuetzetal.2024, author = {Zieher, Simon and Olcay, Ertug and Kefferp{\"u}tz, Klaus and Salamat, Babak and Olzem, Sebastian-Sven and Elsbacher, Gerhard and Meeß, Henri}, title = {Drones for automated parcel delivery: Use case identification and derivation of technical requirements}, volume = {2024}, pages = {101253}, journal = {Transportation Research Interdisciplinary Perspectives}, number = {28}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2590-1982}, doi = {https://doi.org/10.1016/j.trip.2024.101253}, year = {2024}, abstract = {Drone-based parcel delivery is a rapidly growing domain posing many technological and operational challenges. Drone technologies have been under increasing levels of investigation and development over the past years. Since the realization of drone-based delivery can only be economically feasible at large scales, it requires processes that address the outstanding challenges holistically. However, the majority of research focuses on individual aspects such as environmental perception, route planning, operational design, and business models without addressing the challenges in the overall system. This paper conducts a quantitative analysis of the requirements for identified use cases that are considered relevant for the upcoming future. We provide a comprehensive technology overview and derive requirements and specifications for the targeted use cases that are not fulfilled by the current state-of-the-art. The use cases are not only related to last-mile delivery but also long-distance (hub-to-hub) logistics as well as courier and express delivery.}, 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} } @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} }