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Journal Publications

 

  1. Ivica Nakić, Domagoj Tolić, Zoran Tomljanović and Ivana Palunko. Numerically efficient H∞ analysis of cooperative multi-agent systems. Journal of the Franklin Institute, 2022. URL, DOI BibTeX

    @article{NAKIC2022,
    	title = "Numerically efficient H∞ analysis of cooperative multi-agent systems",
    	journal = "Journal of the Franklin Institute",
    	year = 2022,
    	issn = "0016-0032",
    	doi = "https://doi.org/10.1016/j.jfranklin.2022.09.013",
    	url = "https://www.sciencedirect.com/science/article/pii/S0016003222006561/pdfft?md5=15b930d8b9020c86c75719fe03a2c4fb&pid=1-s2.0-S0016003222006561-main.pdf",
    	author = "Ivica Nakić and Domagoj Tolić and Zoran Tomljanović and Ivana Palunko",
    	abstract = "This article proposes a numerically efficient approach for computing the maximal (or minimal) impact one agent has on the cooperative system it belongs to. For example, if one is able to disturb/bolster merely one agent in order to maximally disturb/bolster the entire team, which agent to choose? We quantify the agent-to-system impact in terms of H∞ norm whereas output synchronization is taken as the underlying cooperative control scheme. The agent dynamics are homogeneous, second order and linear whilst communication graphs are weighted and undirected. We devise simple sufficient conditions on agent dynamics, topology and output synchronization parameters rendering all agent-to-system H∞ norms to attain their maxima in the origin (that is, when constant disturbances are applied). Essentially, we quickly identify bottlenecks and weak/strong spots in multi-agent systems without resorting to intense computations, which becomes even more important as the number of agents grows. Our analyses also provide directions towards improving communication graph design and tuning/selecting cooperative control mechanisms. Lastly, numerical examples with a large number of agents and experimental verification employing off-the-shelf nano quadrotors are provided."
    }
    
  2. Tudor Santejudean and Lucian Busoniu. Online learning control for path-aware global optimization with nonlinear mobile robots. Control Engineering Practice 126:105228, 2022. URL, DOI BibTeX

    @article{santejudean_online_2022,
    	author = "Santejudean, Tudor and Busoniu, Lucian",
    	title = "Online learning control for path-aware global optimization with nonlinear mobile robots",
    	journal = "Control Engineering Practice",
    	year = 2022,
    	note = "In press",
    	volume = 126,
    	pages = 105228,
    	doi = "https://doi.org/10.1016/j.conengprac.2022.105228",
    	url = "http://busoniu.net/files/papers/cep22-tudor.pdf"
    }
    
  3. Matthias Rosynski and Lucian Buşoniu. A Simulator and First Reinforcement Learning Results for Underwater Mapping. Sensors 22(14):5384, 2022. URL, DOI BibTeX

    @article{rosynski_simulator_2022,
    	author = "Matthias Rosynski and Lucian Bu\c{s}oniu",
    	title = "A Simulator and First Reinforcement Learning Results for Underwater Mapping",
    	journal = "Sensors",
    	year = 2022,
    	volume = 22,
    	url = "https://www.mdpi.com/1424-8220/22/14/5384/pdf?version=1658453246",
    	number = 14,
    	pages = 5384,
    	doi = "https://doi.org/10.3390/s22145384"
    }
    
  4. Petar Bevanda, Max Beier, Sebastian Kerz, Armin Lederer, Stefan Sosnowski and Sandra Hirche. Diffeomorphically Learning Stable Koopman Operators. IEEE Control Systems Letters 6():3427-3432, 2022. URL, DOI BibTeX

    @article{bevanda_diffeomorphically_2022,
    	author = "Bevanda, Petar and Beier, Max and Kerz, Sebastian and Lederer, Armin and Sosnowski, Stefan and Hirche, Sandra",
    	journal = "IEEE Control Systems Letters",
    	title = "Diffeomorphically Learning Stable Koopman Operators",
    	year = 2022,
    	volume = 6,
    	number = "",
    	pages = "3427-3432",
    	doi = "https://doi.org/10.1109/LCSYS.2022.3184927",
    	url = "https://mediatum.ub.tum.de/doc/1636838/3ptpmd2tuvg80a6m608tfg1b0.KoopFlows_lcssRev_arxiv_.pdf"
    }
    
  5. Athina Ilioudi, Azita Dabiri, Ben J Wolf and Bart De Schutter. Deep Learning for Object Detection and Segmentation in Videos: Toward an Integration With Domain Knowledge. IEEE Access, 2022. URL, DOI BibTeX

    @article{ilioudi_Deep_2022,
    	title = "Deep {Learning} for {Object} {Detection} and {Segmentation} in {Videos}: {Toward} an {Integration} {With} {Domain} {Knowledge}",
    	url = "https://pure.tudelft.nl/ws/portalfiles/portal/116826331/Deep_Learning_for_Object_Detection_and_Segmentation_in_Videos_Toward_an_Integration_With_Domain_Knowledge.pdf",
    	doi = "10.1109/ACCESS.2022.3162827",
    	abstract = "Deep learning has enabled the rapid expansion of computer vision tasks from image frames to video segments. This paper focuses on the review of the latest research in the field of computer vision tasks in general and on object localization and identification of their associated pixels in video frames in particular. After performing a systematic analysis of the existing methods, the challenges related to computer vision tasks are presented. In order to address the existing challenges, a hybrid framework is proposed, where deep learning methods are coupled with domain knowledge. An additional feature of this survey is that a review of the currently existing approaches integrating domain knowledge with deep learning techniques is presented. Finally, some conclusions on the implementation of hybrid architectures to perform computer vision tasks are discussed.",
    	urldate = "2022-05-05",
    	journal = "IEEE Access",
    	author = "Ilioudi, Athina and Dabiri, Azita and Wolf, Ben J. and De Schutter, Bart",
    	month = "",
    	year = 2022
    }
    
  6. Daniël M Bot, Ben J Wolf and Sietse M Netten. The Quadrature Method: A Novel Dipole Localisation Algorithm for Artificial Lateral Lines Compared to State of the Art. Sensors 21(13):4558, 2021. URL, DOI BibTeX

    @article{bot_Quadrature_2021,
    	title = "The {Quadrature} {Method}: {A} {Novel} {Dipole} {Localisation} {Algorithm} for {Artificial} {Lateral} {Lines} {Compared} to {State} of the {Art}",
    	volume = 21,
    	copyright = "http://creativecommons.org/licenses/by/3.0/",
    	issn = "1424-8220",
    	shorttitle = "The {Quadrature} {Method}",
    	url = "https://www.mdpi.com/1424-8220/21/13/4558/pdf?version=1625722411",
    	doi = "10.3390/s21134558",
    	abstract = "The lateral line organ of fish has inspired engineers to develop flow sensor arrays—dubbed artificial lateral lines (ALLs)—capable of detecting near-field hydrodynamic events for obstacle avoidance and object detection. In this paper, we present a comprehensive review and comparison of ten localisation algorithms for ALLs. Differences in the studied domain, sensor sensitivity axes, and available data prevent a fair comparison between these algorithms from their original works. We compare them with our novel quadrature method (QM), which is based on a geometric property specific to 2D-sensitive ALLs. We show how the area in which each algorithm can accurately determine the position and orientation of a simulated dipole source is affected by (1) the amount of training and optimisation data, and (2) the sensitivity axes of the sensors. Overall, we find that each algorithm benefits from 2D-sensitive sensors, with alternating sensitivity axes as the second-best configuration. From the machine learning approaches, an MLP required an impractically large training set to approach the optimisation-based algorithms’ performance. Regardless of the data set size, QM performs best with both a large area for accurate predictions and a small tail of large errors.",
    	language = "en",
    	number = 13,
    	urldate = "2022-05-05",
    	journal = "Sensors",
    	author = "Bot, Daniël M. and Wolf, Ben J. and van Netten, Sietse M.",
    	month = "",
    	year = 2021,
    	note = "Number: 13 Publisher: Multidisciplinary Digital Publishing Institute",
    	keywords = "artificial lateral line, dipole localisation, hydrodynamic imaging, neural networks",
    	pages = 4558
    }
    
  7. Petar Bevanda, Stefan Sosnowski and Sandra Hirche. Koopman operator dynamical models: Learning, analysis and control. Annual Reviews in Control 52:197-212, 2021. URL, DOI BibTeX

    @article{bevanda_koopman_2022,
    	title = "Koopman operator dynamical models: Learning, analysis and control",
    	journal = "Annual Reviews in Control",
    	volume = 52,
    	pages = "197-212",
    	year = 2021,
    	issn = "1367-5788",
    	doi = "https://doi.org/10.1016/j.arcontrol.2021.09.002",
    	url = "https://arxiv.org/pdf/2102.02522.pdf",
    	author = "Petar Bevanda and Stefan Sosnowski and Sandra Hirche",
    	keywords = "Koopman operator, Dynamical models, Representation learning, System analysis, Data-based control",
    	abstract = "The Koopman operator allows for handling nonlinear systems through a globally linear representation. In general, the operator is infinite-dimensional – necessitating finite approximations – for which there is no overarching framework. Although there are principled ways of learning such finite approximations, they are in many instances overlooked in favor of, often ill-posed and unstructured methods. Also, Koopman operator theory has long-standing connections to known system-theoretic and dynamical system notions that are not universally recognized. Given the former and latter realities, this work aims to bridge the gap between various concepts regarding both theory and tractable realizations. Firstly, we review data-driven representations (both unstructured and structured) for Koopman operator dynamical models, categorizing various existing methodologies and highlighting their differences. Furthermore, we provide concise insight into the paradigm’s relation to system-theoretic notions and analyze the prospect of using the paradigm for modeling control systems. Additionally, we outline the current challenges and comment on future perspectives."
    }
    

Conference Publications

 

  1. Tudor Sântejudean and Lucian Busoniu. Path-aware optimistic optimization for a mobile robot. In 2021 60th IEEE Conference on Decision and Control (CDC). 2021. URL, DOI BibTeX

    @inproceedings{santejudean_Pathaware_14,
    	address = "Austin, TX, USA",
    	title = "Path-aware optimistic optimization for a mobile robot",
    	booktitle = "2021 60th IEEE Conference on Decision and Control (CDC)",
    	isbn = "978-1-66543-659-5",
    	url = "http://busoniu.net/files/papers/cdc21-tudor.pdf",
    	doi = "10.1109/CDC45484.2021.9683546",
    	abstract = "We consider problems in which a mobile robot samples an unknown function defined over its operating space, so as to find a global optimum of this function. The path travelled by the robot matters, since it influences energy and time requirements. We consider a branch-and-bound algorithm called deterministic optimistic optimization, and extend it to the path-aware setting, obtaining path-aware optimistic optimization (OOPA). In this new algorithm, the robot decides how to move next via an optimal control problem that maximizes the long-term impact of the robot trajectory on lowering the upper bound, weighted by bound and function values to focus the search on the optima. An online version of value iteration is used to solve an approximate version of this optimal control problem. OOPA is evaluated in extensive experiments in two dimensions, where it does better than path-unaware and local-optimization baselines.",
    	urldate = "2022-05-05",
    	publisher = "IEEE",
    	author = "Sântejudean, Tudor and Busoniu, Lucian",
    	month = "",
    	year = 2021
    }
    
  2. Petar Bevanda, Max Beier, Shahab Heshmati-Alamdari, Stefan Sosnowski and Sandra Hirche. Towards Data-driven LQR with Koopmanizing Flows⋆. IFAC-PapersOnLine 55(15):13-18, 2022. URL, DOI BibTeX

    @article{BEVANDA202213,
    	title = "Towards Data-driven LQR with Koopmanizing Flows⋆",
    	journal = "IFAC-PapersOnLine",
    	volume = 55,
    	number = 15,
    	pages = "13-18",
    	year = 2022,
    	note = "6th IFAC Conference on Intelligent Control and Automation Sciences ICONS 2022",
    	issn = "2405-8963",
    	doi = "https://doi.org/10.1016/j.ifacol.2022.07.601",
    	url = "https://arxiv.org/pdf/2201.11640.pdf",
    	author = "Petar Bevanda and Max Beier and Shahab Heshmati-Alamdari and Stefan Sosnowski and Sandra Hirche",
    	keywords = "Machine learning, Koopman operators, Learning for control, Representation Learning, Neural networks, Learning Systems",
    	abstract = "We propose a novel framework for learning linear time-invariant (LTI) models for a class of continuous-time non-autonomous nonlinear dynamics based on a representation of Koopman operators. In general, the operator is infinite-dimensional but, crucially, linear. To utilize it for effcient LTI control design, we learn a finite representation of the Koopman operator that is linear in controls while concurrently learning meaningful lifting coordinates. For the latter, we rely on Koopmanizing Flows - a diffeomorphism-based representation of Koopman operators and extend it to systems with linear control entry. With such a learned model, we can replace the nonlinear optimal control problem with quadratic cost to that of a linear quadratic regulator (LQR), facilitating efficacious optimal control for nonlinear systems. The superior control performance of the proposed method is demonstrated on simulation examples."
    }
    
  3. Damir Bulić, Domagoj Tolić and Ivana Palunko. Beam-Based Tether Dynamics and Simulations using Finite Element Model. IFAC-PapersOnLine 55(15):154-159, 2022. URL, DOI BibTeX

    @article{BULIC2022154,
    	title = "Beam-Based Tether Dynamics and Simulations using Finite Element Model",
    	journal = "IFAC-PapersOnLine",
    	volume = 55,
    	number = 15,
    	pages = "154-159",
    	year = 2022,
    	note = "6th IFAC Conference on Intelligent Control and Automation Sciences ICONS 2022",
    	issn = "2405-8963",
    	doi = "https://doi.org/10.1016/j.ifacol.2022.07.624",
    	url = "https://www.sciencedirect.com/science/article/pii/S2405896322010357",
    	author = "Damir Bulić and Domagoj Tolić and Ivana Palunko",
    	keywords = "tether dynamics, tethered robots, Finite Element Method, beam equation, PDEs, simulations",
    	abstract = "Building on the beam equation, this paper derives a realistic and versatile 3D model of tether/cable/umbilical dynamics and approximates it via the Finite Element Method (FEM). This FEM implementation/approximation can be executed in real time, when the model accuracy is not of prime concern for the application at hand, or in an offline fashion, when the model accuracy is essential. Our model allows for different time-varying forces (e.g., wind gusts, sea currents, waves, etc.) acting along the cable. However, unlike the related works, where tether bending and stretching are mutually independent resulting in unrealistic tether elongations, our implementation takes into account the tether length, which in turn imposes coupling among transversal and longitudinal tether displacements. Our work is motivated by applications of tethered robots such as Unmanned Underwater Vehicles (UUVs), most commonly Remotely Operated Vehicles (ROVs) and Unmanned Aerial Vehicles (UAVs). In such applications, the tether is typically kept under tension leading to “small” tether displacements and the absence of deformations and entanglements, which is why the beam equation is selected to start with. However, partially owing to the attention paid to the tether length, our dynamic model behaves well even for “larger” displacements as illustrated in Gazebo ROS using a tethered ROV whereas the tether itself is implemented in Matlab via FEM."
    }
    
  4. Bilal Yousuf, Zsofia Lendek and Lucian Busoniu. Exploration-Based Search for an Unknown Number of Targets using a UAV. In Proceedings 6th IFAC Conference on Intelligent Control and Automation Sciences (ICONS-22). 2022. URL, DOI BibTeX

    @inproceedings{icons22-bilal,
    	author = "Yousuf, Bilal and Lendek, Zsofia and Busoniu, Lucian",
    	title = "Exploration-Based Search for an Unknown Number of Targets using a UAV",
    	booktitle = "Proceedings 6th IFAC Conference on Intelligent Control and Automation Sciences ({ICONS-22})",
    	year = 2022,
    	note = "accepted",
    	month = "13--15 July",
    	address = "Cluj-Napoca, Romania",
    	url = "http://busoniu.net/files/papers/icons22-bilal.pdf",
    	doi = "https://doi.org/10.1016/j.ifacol.2022.07.614"
    }
    
  5. Jan Brüdigam, Martin Schuck, Alexandre Capone, Stefan Sosnowski and Sandra Hirche. Structure-Preserving Learning Using Gaussian Processes and Variational Integrators. In Roya Firoozi, Negar Mehr, Esen Yel, Rika Antonova, Jeannette Bohg, Mac Schwager and Mykel Kochenderfer (eds.). Proceedings of The 4th Annual Learning for Dynamics and Control Conference 168. 2022, 1150–1162. URL PDF BibTeX

    @inproceedings{pmlr-v168-brudigam22a,
    	title = "Structure-Preserving Learning Using Gaussian Processes and Variational Integrators",
    	author = {Br\"udigam, Jan and Schuck, Martin and Capone, Alexandre and Sosnowski, Stefan and Hirche, Sandra},
    	booktitle = "Proceedings of The 4th Annual Learning for Dynamics and Control Conference",
    	pages = "1150--1162",
    	year = 2022,
    	editor = "Firoozi, Roya and Mehr, Negar and Yel, Esen and Antonova, Rika and Bohg, Jeannette and Schwager, Mac and Kochenderfer, Mykel",
    	volume = 168,
    	series = "Proceedings of Machine Learning Research",
    	month = "23--24 Jun",
    	publisher = "PMLR",
    	pdf = "https://proceedings.mlr.press/v168/brudigam22a/brudigam22a.pdf",
    	url = "https://proceedings.mlr.press/v168/brudigam22a.html",
    	abstract = "Gaussian process regression is increasingly applied for learning unknown dynamical systems. In particular, the implicit quantification of the uncertainty of the learned model makes it a promising approach for safety-critical applications. When using Gaussian process regression to learn unknown systems, a commonly considered approach consists of learning the residual dynamics after applying some generic discretization technique, which might however disregard properties of the underlying physical system. Variational integrators are a less common yet promising approach to discretization, as they retain physical properties of the underlying system, such as energy conservation and satisfaction of explicit kinematic constraints. In this work, we present a novel structure-preserving learning-based modelling approach that combines a variational integrator for the nominal dynamics of a mechanical system and learning residual dynamics with Gaussian process regression. We extend our approach to systems with known kinematic constraints and provide formal bounds on the prediction uncertainty. The simulative evaluation of the proposed method shows desirable energy conservation properties in accordance with general theoretical results and demonstrates exact constraint satisfaction for constrained dynamical systems."
    }
    
  6. Tudor Santejudean, Lucian Busoniu, Vineeth Varma and Constantin Morarescu. A simple path-aware optimization method for mobile robots. In 6th IFAC Symposium on Telematics Applications (TA-22). 2022. URL, DOI BibTeX

    @inproceedings{ta22-tudor,
    	author = "Santejudean, Tudor and Busoniu, Lucian and Varma, Vineeth and Morarescu, Constantin",
    	title = "A simple path-aware optimization method for mobile robots",
    	booktitle = "6th IFAC Symposium on Telematics Applications ({TA-22})",
    	year = 2022,
    	note = "accepted",
    	month = "15--17 June",
    	address = "Nancy, France",
    	url = "http://busoniu.net/files/papers/ifacta22-tudor.pdf",
    	doi = "https://doi.org/10.1016/j.ifacol.2022.08.001"
    }
    
  7. Antun Đuraš, Matija Sukno and Ivana Palunko. Recovering the 3D UUV Position using UAV Imagery in Shallow-Water Environments. In 2022 International Conference on Unmanned Aircraft Systems (ICUAS) (). June 2022, 948-954. DOI BibTeX

    @inproceedings{9836195,
    	author = "Đuraš, Antun and Sukno, Matija and Palunko, Ivana",
    	booktitle = "2022 International Conference on Unmanned Aircraft Systems (ICUAS)",
    	title = "Recovering the 3D UUV Position using UAV Imagery in Shallow-Water Environments",
    	year = 2022,
    	month = "June",
    	volume = "",
    	number = "",
    	pages = "948-954",
    	abstract = "In this paper we propose a novel approach aimed at recovering the 3D position of an UUV from UAV imagery in shallow-water environments. Through combination of UAV and UUV measurements, we show that our method can be utilized as an accurate and cost-effective alternative when compared to acoustic sensing methods, typically required to obtain ground truth information in underwater localization problems. Furthermore, our approach allows for a seamless conversion to geo-referenced coordinates which can be utilized for navigation purposes. To validate our method, we present the results with data collected through a simulation environment and field experiments, demonstrating the ability to successfully recover the UUV position with sub-meter accuracy.",
    	keywords = "",
    	doi = "10.1109/ICUAS54217.2022.9836195",
    	issn = "2575-7296"
    }
    
  8. Vicu-Mihalis Maer, Levente Tamas and Lucian Busoniu. Underwater robot pose estimation using acoustic methods and intermittent position measurements at the surface. In 2022 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR-22). 2022. URL, DOI BibTeX

    @inproceedings{aqtr22-mihalis,
    	author = "Vicu-Mihalis Maer and Levente Tamas and Lucian Busoniu",
    	title = "Underwater robot pose estimation using acoustic methods and intermittent position measurements at the surface",
    	booktitle = "2022 IEEE International Conference on Automation, Quality and Testing, Robotics ({AQTR-22})",
    	year = 2022,
    	month = "22--24 May",
    	address = "Cluj-Napoca, Romania",
    	url = "http://busoniu.net/files/papers/aqtr22-mihalis.pdf",
    	doi = "https://doi.org/10.1109/AQTR55203.2022.9802002"
    }
    
  9. Vicko Prkačin, Ivana Palunko and Ivan Petrović. Extended Kalman filter for payload state estimation utilizing aircraft inertial sensing. In 2021 Aerial Robotic Systems Physically Interacting with the Environment (AIRPHARO) (). 2021, 1-6. DOI BibTeX

    @inproceedings{9571038,
    	author = "Prkačin, Vicko and Palunko, Ivana and Petrović, Ivan",
    	booktitle = "2021 Aerial Robotic Systems Physically Interacting with the Environment (AIRPHARO)",
    	title = "Extended Kalman filter for payload state estimation utilizing aircraft inertial sensing",
    	year = 2021,
    	volume = "",
    	number = "",
    	pages = "1-6",
    	doi = "10.1109/AIRPHARO52252.2021.9571038"
    }
    
  10. Zewen Yang, Stefan Sosnowski, Qingchen Liu, Junjie Jiao, Armin Lederer and Sandra Hirche. Distributed Learning Consensus Control for Unknown Nonlinear Multi-Agent Systems based on Gaussian Processes. In 2021 60th IEEE Conference on Decision and Control (CDC) (). December 2021, 4406-4411. URL, DOI BibTeX

    @inproceedings{9683522,
    	author = "Yang, Zewen and Sosnowski, Stefan and Liu, Qingchen and Jiao, Junjie and Lederer, Armin and Hirche, Sandra",
    	booktitle = "2021 60th IEEE Conference on Decision and Control (CDC)",
    	title = "Distributed Learning Consensus Control for Unknown Nonlinear Multi-Agent Systems based on Gaussian Processes",
    	year = 2021,
    	volume = "",
    	number = "",
    	pages = "4406-4411",
    	abstract = "In this paper, a distributed learning leader-follower consensus protocol based on Gaussian process regression for a class of nonlinear multi-agent systems with unknown dynamics is designed. We propose a distributed learning approach to predict the residual dynamics for each agent. The stability of the consensus protocol using the data-driven model of the dynamics is shown via Lyapunov analysis. The followers ultimately synchronize to the leader with guaranteed error bounds by applying the proposed control law with a high probability. The effectiveness and the applicability of the developed protocol are demonstrated by simulation examples.",
    	keywords = "",
    	doi = "10.1109/CDC45484.2021.9683522",
    	issn = "2576-2370",
    	month = "Dec",
    	url = "https://mediatum.ub.tum.de/doc/1633342/t79gh8w431q90v5eb50wiwxmf.2103.15929.pdf"
    }
    

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 871295.

The SEACLEAR project spans four years, running from January 1st, 2020 to December 31st, 2023.

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