References of "Voos, Holger 50003283"
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See detailA short Survey on the Cyber Security in Control Systems
Bezzaoucha, Souad UL; Voos, Holger UL

Scientific Conference (2020, July)

In the present survey paper, we give a short, yet exhaustive state-of-the-art about the cyber-security applied to control systems, especially the event-based strategy. Indeed, in the past few years, due ... [more ▼]

In the present survey paper, we give a short, yet exhaustive state-of-the-art about the cyber-security applied to control systems, especially the event-based strategy. Indeed, in the past few years, due to a highest degree of connectivity in modern systems, new related control-specific cyber-physical systems security challengesarise and novel approaches integrating the cyber aspect are developed.Our goal in this paper is then to provide an overview of attack-modeling and security analysis approaches in recent works thatexplore networked control systems subject to cyber-attacks attacks. To this end, we look at the control, estimation, and modeling problems. [less ▲]

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See detailA Real-Time Approach for Chance-Constrained Motion Planning with Dynamic Obstacles
Castillo Lopez, Manuel UL; Ludivig, Philippe; Sajadi-Alamdari, Seyed Amin et al

in IEEE Robotics and Automation Letters (2020), 5(2), 3620-3625

Uncertain dynamic obstacles, such as pedestrians or vehicles, pose a major challenge for optimal robot navigation with safety guarantees. Previous work on motion planning has followed two main strategies ... [more ▼]

Uncertain dynamic obstacles, such as pedestrians or vehicles, pose a major challenge for optimal robot navigation with safety guarantees. Previous work on motion planning has followed two main strategies to provide a safe bound on an obstacle's space: a polyhedron, such as a cuboid, or a nonlinear differentiable surface, such as an ellipsoid. The former approach relies on disjunctive programming, which has a relatively high computational cost that grows exponentially with the number of obstacles. The latter approach needs to be linearized locally to find a tractable evaluation of the chance constraints, which dramatically reduces the remaining free space and leads to over-conservative trajectories or even unfeasibility. In this work, we present a hybrid approach that eludes the pitfalls of both strategies while maintaining the original safety guarantees. The key idea consists in obtaining a safe differentiable approximation for the disjunctive chance constraints bounding the obstacles. The resulting nonlinear optimization problem is free of chance constraint linearization and disjunctive programming, and therefore, it can be efficiently solved to meet fast real-time requirements with multiple obstacles. We validate our approach through mathematical proof, simulation and real experiments with an aerial robot using nonlinear model predictive control to avoid pedestrians. [less ▲]

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See detailMixed Integer Linear Programming and Constraint Programming Models for the Online Printing Shop Scheduling Problem
Tessaro Lunardi, Willian UL; Birgin, Ernesto G.; Laborie, Philippe et al

in Computers and Operations Research (2020)

In this work, the online printing shop scheduling problem is considered. This challenging real problem, that appears in the nowadays printing industry, can be seen as a flexible job shop scheduling ... [more ▼]

In this work, the online printing shop scheduling problem is considered. This challenging real problem, that appears in the nowadays printing industry, can be seen as a flexible job shop scheduling problem with sequence flexibility in which precedence constraints among operations of a job are given by an arbitrary directed acyclic graph. In addition, several complicating particularities such as periods of unavailability of the machines, resumable operations, sequence-dependent setup times, partial overlapping among operations with precedence constraints, release times, and fixed operations are also present in the addressed problem. In the present work, mixed integer linear programming and constraint programming models for the minimization of the makespan are presented. Modeling the problem is twofold. On the one hand, the problem is precisely defined. On the other hand, the capabilities and limitations of a commercial software for solving the models are analyzed. Extensive numerical experiments with small- , medium-, and large-sized instances are presented. Numerical experiments show that the commercial solver is able to optimally solve only a fraction of the small-sized instances when considering the mixed integer linear programming model; while all small-sized and a fraction of the medium-sized instances are optimally solved when considering the constraint programming formulation of the problem. Moreover, the commercial solver is able to deliver feasible solutions for the large-sized instances that are of the size of the instances that appear in practice. [less ▲]

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See detailMetaheuristics for the Online Printing Shop Scheduling Problem
Tessaro Lunardi, Willian UL; Birgin, Ernesto G.; Ronconi, Débora P. et al

in European Journal of Operational Research (2020)

In this work, the online printing shop scheduling problem introduced in (Lunardi et al., Mixed Integer Linear Programming and Constraint Programming Models for the Online Printing Shop Scheduling Problem ... [more ▼]

In this work, the online printing shop scheduling problem introduced in (Lunardi et al., Mixed Integer Linear Programming and Constraint Programming Models for the Online Printing Shop Scheduling Problem, Computers & Operations Research, to appear) is considered. This challenging real scheduling problem, that emerged in the nowadays printing industry, corresponds to a flexible job shop scheduling problem with sequencing flexibility; and it presents several complicating specificities such as resumable operations, periods of unavailability of the machines, sequence-dependent setup times, partial overlapping between operations with precedence constraints, and fixed operations, among others. A local search strategy and metaheuristic approaches for the problem are proposed and evaluated. Based on a common representation scheme, trajectory and populational metaheuristics are considered. Extensive numerical experiments with large-sized instances show that the proposed methods are suitable for solving practical instances of the problem; and that they outperform a half-heuristic-half-exact off-the-shelf solver by a large extent. Numerical experiments with classical instances of the flexible job shop scheduling problem show that the introduced methods are also competitive when applied to this particular case. [less ▲]

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See detailMetaheuristics for the Online Printing Shop Scheduling Problem - Supplementary Material
Tessaro Lunardi, Willian UL; Birgin, Ernesto G.; Ronconi, Débora P. et al

Report (2020)

This document presents further numerical results of the experiments concerning the classical instances of the flexible job shop scheduling problem, performed in (Lunardi et al., Metaheuristics for the ... [more ▼]

This document presents further numerical results of the experiments concerning the classical instances of the flexible job shop scheduling problem, performed in (Lunardi et al., Metaheuristics for the Online Printing Shop Scheduling Problem, submitted). Additionally, this document gathers the best makespan values (upper bounds and lower bounds) found by state-of-the-art algorithms. [less ▲]

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See detailSafer UAV Piloting: A Robust Sense-and-Avoid Solution for Remotely Piloted Quadrotor UAVs in Complex Environments
Wang, Min UL; Voos, Holger UL

in Safer UAV Piloting: A Robust Sense-and-Avoid Solution for Remotely Piloted Quadrotor UAVs in Complex Environments (2019, December)

Current commercial UAVs are to a large extent remotely piloted by amateur human pilots. Due to lack of teleoperation experience or skills, they often drive the UAVs into collision. Therefore, in order to ... [more ▼]

Current commercial UAVs are to a large extent remotely piloted by amateur human pilots. Due to lack of teleoperation experience or skills, they often drive the UAVs into collision. Therefore, in order to ensure safety of the UAV as well as its surroundings, it is necessary for the UAV to boast the capability of detecting emergency situation and acting on its own when facing imminent threat. However, the majority of UAVs currently available in the market are not equipped with such capability. To fill in the gap, in this paper we present a complete sense-and-avoid solution for assisting unskilled pilots in ensuring a safe flight. Particularly, we propose a novel nonlinear vehicle control system which takes into account of sensor characteristics, an emergency evaluation policy and a novel optimization-based avoidance control strategy. The effectiveness of the proposed approach is demonstrated and validated in simulation with multiple moving objects. [less ▲]

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See detailA case study on the impact of masking moving objects on the camera pose regression with CNNs
Cimarelli, Claudio UL; Cazzato, Dario UL; Olivares Mendez, Miguel Angel UL et al

in 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) (2019, November 25)

Robot self-localization is essential for operating autonomously in open environments. When cameras are the main source of information for retrieving the pose, numerous challenges are posed by the presence ... [more ▼]

Robot self-localization is essential for operating autonomously in open environments. When cameras are the main source of information for retrieving the pose, numerous challenges are posed by the presence of dynamic objects, due to occlusion and continuous changes in the appearance. Recent research on global localization methods focused on using a single (or multiple) Convolutional Neural Network (CNN) to estimate the 6 Degrees of Freedom (6-DoF) pose directly from a monocular camera image. In contrast with the classical approaches using engineered feature detector, CNNs are usually more robust to environmental changes in light and to occlusions in outdoor scenarios. This paper contains an attempt to empirically demonstrate the ability of CNNs to ignore dynamic elements, such as pedestrians or cars, through learning. For this purpose, we pre-process a dataset for pose localization with an object segmentation network, masking potentially moving objects. Hence, we compare the pose regression CNN trained and/or tested on the set of masked images and the original one. Experimental results show that the performances of the two training approaches are similar, with a slight reduction of the error when hiding occluding objects from the views. [less ▲]

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See detailSimultaneous State and False-Data Injection Attacks Reconstruction for NonLinear Systems: an LPV Approach
Bezzaoucha, Souad UL; Voos, Holger UL

in Bezzaoucha, Souad (Ed.) International Conference on Automation, Control and Robots (2019, October)

The present contribution addresses simultaneous state and actuator/sensor false-data injection attacks reconstruction for nonlinear systems. The considered actuator/sensor attacks are modeled as time ... [more ▼]

The present contribution addresses simultaneous state and actuator/sensor false-data injection attacks reconstruction for nonlinear systems. The considered actuator/sensor attacks are modeled as time-varying parameters with a multiplicative effect on the actuator input signal and the sensor output signal, respectively. Based on the sector non-linearity approach and the convex polytopic transformation, the nonlinear model is written in a Linear Parameter-Varying (LPV) form, then an observer allowing both state and attack reconstruction is designed by solving an LMI optimization problem. [less ▲]

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See detailOn the Event-based Attack-tolerant Control: A Polytopic Representation
Bezzaoucha, Souad UL; Voos, Holger UL

in Bezzaoucha, Souad (Ed.) International Conference on Automation, Control and Robots (2019, October)

In the present contribution, we present a new event-based control representation. Based on the polytopic approach, more specifically the sector nonlinear transformation, an event-based attack-tolerant ... [more ▼]

In the present contribution, we present a new event-based control representation. Based on the polytopic approach, more specifically the sector nonlinear transformation, an event-based attack-tolerant control, and scheduling co-design strategy are proposed. From the event triggering definition (sample-and-hold strategy), polytopic writing of the event-triggered feedback control is first presented and then incorporated into the system dynamics for analysis. Our goal is to present a unique model that is able to deal with the co-design problem simultaneously and that can be handled by classical control synthesis tools. The novel representation, including data deception and attack tolerant control is formulated as a BMI optimization problem ensuring both stability and some level performance requirements (L2 attenuation of the cyber-attack). [less ▲]

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See detailFaster Visual-Based Localization with Mobile-PoseNet
Cimarelli, Claudio UL; Cazzato, Dario UL; Olivares Mendez, Miguel Angel UL et al

in International Conference on Computer Analysis of Images and Patterns (2019, August 22)

Precise and robust localization is of fundamental importance for robots required to carry out autonomous tasks. Above all, in the case of Unmanned Aerial Vehicles (UAVs), efficiency and reliability are ... [more ▼]

Precise and robust localization is of fundamental importance for robots required to carry out autonomous tasks. Above all, in the case of Unmanned Aerial Vehicles (UAVs), efficiency and reliability are critical aspects in developing solutions for localization due to the limited computational capabilities, payload and power constraints. In this work, we leverage novel research in efficient deep neural architectures for the problem of 6 Degrees of Freedom (6-DoF) pose estimation from single RGB camera images. In particular, we introduce an efficient neural network to jointly regress the position and orientation of the camera with respect to the navigation environment. Experimental results show that the proposed network is capable of retaining similar results with respect to the most popular state of the art methods while being smaller and with lower latency, which are fundamental aspects for real-time robotics applications. [less ▲]

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See detailDeep learning based semantic situation awareness system for multirotor aerial robots using LIDAR
Sanchez Lopez, Jose Luis UL; Sampedro, Carlos; Cazzato, Dario UL et al

in 2019 International Conference on Unmanned Aircraft Systems (ICUAS) (2019, June)

In this work, we present a semantic situation awareness system for multirotor aerial robots, based on 2D LIDAR measurements, targeting the understanding of the environment and assuming to have a precise ... [more ▼]

In this work, we present a semantic situation awareness system for multirotor aerial robots, based on 2D LIDAR measurements, targeting the understanding of the environment and assuming to have a precise robot localization as an input of our algorithm. Our proposed situation awareness system calculates a semantic map of the objects of the environment as a list of circles represented by their radius, and the position and the velocity of their center in world coordinates. Our proposed algorithm includes three main parts. First, the LIDAR measurements are preprocessed and an object segmentation clusters the candidate objects present in the environment. Secondly, a Convolutional Neural Network (CNN) that has been designed and trained using an artificially generated dataset, computes the radius and the position of the center of individual circles in sensor coordinates. Finally, an indirect-EKF provides the estimate of the semantic map in world coordinates, including the velocity of the center of the circles in world coordinates.We have quantitative and qualitative evaluated the performance of our proposed situation awareness system by means of Software-In-The-Loop simulations using VRep with one and multiple static and moving cylindrical objects in the scene, obtaining results that support our proposed algorithm. In addition, we have demonstrated that our proposed algorithm is capable of handling real environments thanks to real laboratory experiments with non-cylindrical static (i.e. a barrel) and moving (i.e. a person) objects. [less ▲]

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See detailDeep Reinforcement Learning based Continuous Control for Multicopter Systems
Manukyan, Anush UL; Olivares Mendez, Miguel Angel UL; Geist, Matthieu et al

in International Conference on Control, Decision and Information CoDIT, Paris 23-26 April 2019 (2019, April 26)

In this paper we apply deep reinforcement learning techniques on a multicopter for learning a stable hovering task in a continuous action state environment. We present a framework based on OpenAI GYM ... [more ▼]

In this paper we apply deep reinforcement learning techniques on a multicopter for learning a stable hovering task in a continuous action state environment. We present a framework based on OpenAI GYM, Gazebo and RotorS MAV simulator, utilized for successfully training different agents to perform various tasks. The deep reinforcement learning method used for the training is model-free, on-policy, actor-critic based algorithm called Trust Region Policy Optimization (TRPO). Two neural networks have been used as a nonlinear function approximators. Our experiments showed that such learning approach achieves successful results, and facilitates the process of controller design. [less ▲]

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See detailAn Effective Hybrid Imperialist Competitive Algorithm and Tabu Search for an Extended Flexible Job Shop Scheduling Problem
Tessaro Lunardi, Willian UL; Voos, Holger UL; Cherri, Luiz Henrique

in 34th ACM/SIGAPP Symposium On Applied Computing, Limassol, Cyprus April 8-12, 2019 (2019, April 08)

An extended version of the flexible job shop problem is tackled in this work. The investigated extension of the classical flexible job shop problem allows the precedences between the operations to be ... [more ▼]

An extended version of the flexible job shop problem is tackled in this work. The investigated extension of the classical flexible job shop problem allows the precedences between the operations to be given by an arbitrary directed acyclic graph instead of a linear order. The problem consists of designating the operations to the machines and sequencing them in compliance with the supplied precedences. The goal in the present work is the minimization of the makespan. In order to produce reasonable outcomes in acceptable time, a hybrid imperialist competitive algorithm and tabu search is proposed to solve the problem. Numerical experiments assess the efficiency of the proposed method and compare it with well-known scheduling algorithms. [less ▲]

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See detailStability Analysis of Power Networks under Cyber-Physical Attacks: an LPV-Descriptor Approach
Bezzaoucha, Souad UL; Voos, Holger UL

in Bezzaoucha, Souad (Ed.) International Conference on Control, Decision and Information Technologies (2019, April)

This paper proposes a unified and advanced framework for the modeling, stability study and stabilization of a Power Networks subject to an omniscient adversary (i.e. cyber-attack). From the system model ... [more ▼]

This paper proposes a unified and advanced framework for the modeling, stability study and stabilization of a Power Networks subject to an omniscient adversary (i.e. cyber-attack). From the system model developed in [24], based on the well-known sector non-linearity approach and the convex polytopic transformation, the attacked system (descriptor model) is re-written in a more convenient form (Linear Parameter Varying-LPV) with unmeasurable premise variables. The so-called Lyapunov-based methods are applied in order to study the stability and security problems despite the presence of cyber-attacks. The conditions will be given in terms of Linear- Bilinear Matrix Inequality LMI- BMI constraints. [less ▲]

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See detailEvaluation of End-To-End Learning for Autonomous Driving: The Good, the Bad and the Ugly
Varisteas, Georgios UL; Frank, Raphaël UL; Sajadi Alamdari, Seyed Amin UL et al

in 2nd International Conference on Intelligent Autonomous Systems, Singapore, Feb. 28 to Mar. 2, 2019 (2019, March 01)

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See detailA non-invasive tool for attention-deficit disorder analysis based on gaze tracks
Cazzato, Dario UL; Castro, Silvia M.; Agamennoni, Osvaldo et al

in Proceedings of the 2nd International Conference on Applications of Intelligent Systems (2019)

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See detailUnderstanding and Modelling Human Attention for Soft Biometrics Purposes
Cazzato, Dario UL; Leo, Marco; Carcagnì, Pierluigi et al

in AIVR 2019: Proceedings of the 2019 3rd International Conference on Artificial Intelligence and Virtual Reality (2019)

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See detailVision-Based Aircraft Pose Estimation for UAVs Autonomous Inspection without Fiducial Markers
Cazzato, Dario UL; Olivares Mendez, Miguel Angel UL; Sanchez Lopez, Jose Luis UL et al

in IECON 2019-45th Annual Conference of the IEEE Industrial Electronics Society (2019)

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See detailReal-Time Human Head Imitation for Humanoid Robots
Cazzato, Dario UL; Cimarelli, Claudio UL; Sanchez Lopez, Jose Luis UL et al

in Proceedings of the 2019 3rd International Conference on Artificial Intelligence and Virtual Reality (2019)

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See detailA parallel multi-population biased random-key genetic algorithm for electric distribution network reconfiguration
de Faria Junior, Haroldo UL; Tessaro Lunardi, Willian UL; Voos, Holger UL

in The Genetic and Evolutionary Computation Conference - GECCO'19 (2019)

This work presents a multi-population biased random-key genetic algorithm (BRKGA) for the electric distribution network reconfiguration problem (DNR). DNR belongs to the class of network design problems ... [more ▼]

This work presents a multi-population biased random-key genetic algorithm (BRKGA) for the electric distribution network reconfiguration problem (DNR). DNR belongs to the class of network design problems which include transportation problems, computer network restoration and telecommunication network design and can be used for loss minimization and load balancing, being an important tool for distribution network operators. A BRKGA is a class of genetic algorithms in which solutions are encoded as vectors of random keys, i.e. randomly generated real numbers from a uniform distribution in the interval [0, 1). A vector of random keys is translated into a solution of the optimization problem by a decoder. The decoder used generates only feasible solutions by using an efficient codification based upon the fundamentals of graph theory, restricting the search space. The parallelization is based on the single program multiple data paradigm and is executed on the cores of a multi-core processor. Time to target plots, which characterize the running times of stochastic algorithms for combinatorial optimization, are used to compare the performance of the serial and parallel algorithms. The proposed method has been tested on two standard distribution systems and the results show the effectiveness and performance of the parallel algorithm. [less ▲]

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