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See detailRobotix-Academy Conference for Industrial Robotics (RACIR) 2017
Müller, Rainer; Plapper, Peter UL; Brüls, Olivier et al

Book published by Shaker Verlag - 1st ed (2017)

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See detailRobotix-Academy Conference for Industrial Robotics (RACIR) 2018
Müller, Rainer; Plapper, Peter UL; Brüls, Olivier et al

Book published by Shaker Verlag - 1st ed (2018)

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See detailRobust Approach for Urban Road Surface Extraction Using Mobile Laser Scanning Data
Nurunnabi, Abdul Awal Md UL; Teferle, Felix Norman UL; Lindenbergh, Roderik et al

in Robust Approach for Urban Road Surface Extraction Using Mobile Laser Scanning Data (2022, June)

Road surface extraction is crucial for 3D city analysis. Mobile laser scanning (MLS) is the most appropriate data acquisition system for the road environment because of its efficient vehicle-based on-road ... [more ▼]

Road surface extraction is crucial for 3D city analysis. Mobile laser scanning (MLS) is the most appropriate data acquisition system for the road environment because of its efficient vehicle-based on-road scanning opportunity. Many methods are available for road pavement, curb and roadside way extraction. Most of them use classical approaches that do not mitigate problems caused by the presence of noise and outliers. In practice, however, laser scanning point clouds are not free from noise and outliers, and it is apparent that the presence of a very small portion of outliers and noise can produce unreliable and non-robust results. A road surface usually consists of three key parts: road pavement, curb and roadside way. This paper investigates the problem of road surface extraction in the presence of noise and outliers, and proposes a robust algorithm for road pavement, curb, road divider/islands, and roadside way extraction using MLS point clouds. The proposed algorithm employs robust statistical approaches to remove the consequences of the presence of noise and outliers. It consists of five sequential steps for road ground and non-ground surface separation, and road related components determination. Demonstration on two different MLS data sets shows that the new algorithm is efficient for road surface extraction and for classifying road pavement, curb, road divider/island and roadside way. The success can be rated in one experiment in this paper, where we extract curb points; the results achieve 97.28%, 100% and 0.986 of precision, recall and Matthews correlation coefficient, respectively. [less ▲]

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See detailRobust dynamical network reconstruction
Yuan, Y.; Stan, G. B.; Warnick, S. et al

in The proceedings of the 49th IEEE Conference on Decision and Control (CDC) (2010)

Motivated by biological applications, this paper addresses the problem of network reconstruction from data. Previous work has shown necessary and sufficient conditions for network reconstruction of noise ... [more ▼]

Motivated by biological applications, this paper addresses the problem of network reconstruction from data. Previous work has shown necessary and sufficient conditions for network reconstruction of noise-free LTI systems. This paper assumes that the conditions for network reconstruction have been met but here we additionally take into account noise and unmodelled dynamics (including nonlinearities). Algorithms are therefore proposed to reconstruct dynamical (Boolean) network structure from time-series (steady-state) data respectively in presence of noise and nonlinearities. In order to identify the network structure that generated the data, we compute the smallest distances between the measured data and the data that would have been generated by particular Boolean structures. Information criteria and optimisation technique balancing such distance and model complexity are introduced to search for the true structure. We conclude with biologically-inspired network reconstruction examples which include noise and nonlinearities. [less ▲]

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See detailRobust dynamical network structure reconstruction
Yuan, Ye; Stan, Guy-Bart; Warnick, Stan et al

in Automatica (2011), 47(6),

This paper addresses the problem of network reconstruction from data. Previous work identified necessary and sufficient conditions for network reconstruction of LTI systems, assuming perfect measurements ... [more ▼]

This paper addresses the problem of network reconstruction from data. Previous work identified necessary and sufficient conditions for network reconstruction of LTI systems, assuming perfect measurements (no noise) and perfect system identification. This paper assumes that the conditions for network reconstruction have been met but here we additionally take into account noise and unmodelled dynamics (including nonlinearities). In order to identify the network structure that generated the data, we compute the smallest distances between the measured data and the data that would have been generated by particular network structures. We conclude with biologically inspired network reconstruction examples which include noise and nonlinearities. [less ▲]

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See detailRobust dynamical network structure reconstruction
Yuan, Y.; Stan, G. B.; Warnick, S. et al

Scientific Conference (2010)

Motivated by biological applications, this paper addresses the problem of network reconstruction from data. Previous work has shown necessary and sufficient conditions for network reconstruction of noise ... [more ▼]

Motivated by biological applications, this paper addresses the problem of network reconstruction from data. Previous work has shown necessary and sufficient conditions for network reconstruction of noise-free LTI systems. This paper assumes that the conditions for network reconstruction have been met but here we additionally take into account noise and unmodelled dynamics (including nonlinearities). Algorithms are therefore proposed to reconstruct dynamical (Boolean) network structure from time-series (steady-state) data respectively in presence of noise and nonlinearities. In order to identify the network structure that generated the data, we compute the smallest distances between the measured data and the data that would have been generated by particular Boolean structures. Information criteria and optimisation technique balancing such distance and model complexity are introduced to search for the true structure. We conclude with biologically-inspired network reconstruction examples which include noise and nonlinearities. [less ▲]

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See detailRobust H∞ feedback control for uncertain stochastic delayed genetic regulatory networks with additive and multiplicative noise
Pan, Wei UL; Wang, Z.; Gao, H. et al

in International Journal of Robust and Nonlinear Control (2010), 20(18), 2093-2107

Noises are ubiquitous in genetic regulatory networks (GRNs). Gene regulation is inherently a stochastic process because of intrinsic and extrinsic noises that cause kinetic parameter variations and basal ... [more ▼]

Noises are ubiquitous in genetic regulatory networks (GRNs). Gene regulation is inherently a stochastic process because of intrinsic and extrinsic noises that cause kinetic parameter variations and basal rate disturbance. Time delays are usually inevitable due to different biochemical reactions in such GRNs. In this paper, a delayed stochastic model with additive and multiplicative noises is utilized to describe stochastic GRNs. A feedback gene controller design scheme is proposed to guarantee that the GRN is mean-square asymptotically stable with noise attenuation, where the structure of the controllers can be specified according to engineering requirements. By applying control theory and mathematical tools, the analytical solution to the control design problem is given, which helps to provide some insight into synthetic biology and systems biology. The control scheme is employed in a three-gene network to illustrate the applicability and usefulness of the design. [less ▲]

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See detailRobust network reconstruction in polynomial time
Hayden, D.; Yuan, Y.; Goncalves, Jorge UL

in The proceedings of the 51st IEEE Conference on Decision and Control (2012)

This paper presents an efficient algorithm for robust network reconstruction of Linear Time-Invariant (LTI) systems in the presence of noise, estimation errors and unmodelled nonlinearities. The method ... [more ▼]

This paper presents an efficient algorithm for robust network reconstruction of Linear Time-Invariant (LTI) systems in the presence of noise, estimation errors and unmodelled nonlinearities. The method here builds on previous work [1] on robust reconstruction to provide a practical implementation with polynomial computational complexity. Following the same experimental protocol, the algorithm obtains a set of structurally-related candidate solutions spanning every level of sparsity. We prove the existence of a magnitude bound on the noise, which if satisfied, guarantees that one of these structures is the correct solution. A problem-specific model-selection procedure then selects a single solution from this set and provides a measure of confidence in that solution. Extensive simulations quantify the expected performance for different levels of noise and show that significantly more noise can be tolerated in comparison to the original method. [less ▲]

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See detailRobust Online Obstacle Detection and Tracking for Collision-free Navigation of Multirotor UAVs in Complex Environments
Wang, Min UL; Voos, Holger UL; Su, Daobilige

in 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), Singapore 18-21 November 2018 (2018)

Object detection and tracking is a challenging task, especially for unmanned aerial robots in complex environments where both static and dynamic objects are present. It is, however, essential for ensuring ... [more ▼]

Object detection and tracking is a challenging task, especially for unmanned aerial robots in complex environments where both static and dynamic objects are present. It is, however, essential for ensuring safety of the robot during navigation in such environments. In this work we present a practical online approach which is based on a 2D LIDAR. Unlike common approaches in the literature of modeling the environment as 2D or 3D occupancy grids, our approach offers a fast and robust method to represent the objects in the environment in a compact form, which is significantly more efficient in terms of both memory and computation in comparison with the former. Our approach is also capable of classifying objects into categories such as static and dynamic, and tracking dynamic objects as well as estimating their velocities with reasonable accuracy. [less ▲]

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See detailRobust Real-time Sense-and-Avoid Solutions for Remotely Piloted Quadrotor UAVs in Complex Environments
Wang, Min UL

Doctoral thesis (2020)

UAV teleoperation is a demanding task: to successfully accomplish the mission without collision requires skills and experience. In real-life environments, current commercial UAVs are to a large extent ... [more ▼]

UAV teleoperation is a demanding task: to successfully accomplish the mission without collision requires skills and experience. In real-life environments, 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 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 work we present 2D LIDAR based Sense-and-Avoid solutions which are able to actively assist unskilled human operator in obstacle avoidance, so that the operator can focus on high-level decisions and global objectives in UAV applications such as search and rescue, farming etc. Specifically, with our novel 2D LIDAR based obstacle detection and tracking algorithm, perception-assistive flight control design, progressive emergency evaluation policies and optimization based and adaptive virtual cushion force field (AVCFF) based avoidance strategies, our proposed UAV teleoperation assistance systems are capable of obstacle detection and tracking, as well as automatic obstacle avoidance in complex environment where both static and dynamic objects are present. Additionally, while the optimization based solution is validated in Matlab, the AVCFF based avoidance system has been fully integrated with sensing system, perception-assistive flight controller on the basis of the Hector Quadrotor open source framework, and the effectiveness of the complete Sense-and-Avoid solution has been demonstrated and validated on a realistic simulated UAV platform in Gazebo simulations, where the UAV is operated at a high speed. [less ▲]

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See detailRobust Signal-Structure Reconstruction
Chetty, Vasu; Hayden, David; Goncalves, Jorge UL et al

in The proceedings of the IEEE 52nd Annual Conference on Decision and Control (2013)

This paper focuses on the reconstruction of the signal structure of a system in the presence of noise and nonlinearities. Previous results on polynomial time reconstruction in this area were restricted to ... [more ▼]

This paper focuses on the reconstruction of the signal structure of a system in the presence of noise and nonlinearities. Previous results on polynomial time reconstruction in this area were restricted to systems where target specificity was part of the inherent structure, [5]. This work extends these results to all reconstructible systems and proposes a faster reconstruction algorithm along with an improved model selection procedure. Finally, a simulation study then details the performance of this new algorithm on reconstructible systems. [less ▲]

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See detailRobust stability of delayed genetic regulatory networks with different sources of uncertainties
Pan, Wei UL; Wang, Z.; Hu, J.

in Asian Journal of Control (2011), 13(5), 645-654

Gene regulation is inherently a stochastic process due to intrinsic and extrinsic noises which cause the fluctuations and uncertainties of kinetic parameters. On the other hand, time delays are usually ... [more ▼]

Gene regulation is inherently a stochastic process due to intrinsic and extrinsic noises which cause the fluctuations and uncertainties of kinetic parameters. On the other hand, time delays are usually inevitable due to different biochemical reactions in the genetic regulatory networks (GRNs) which are also affected by noises. Therefore, in this paper, we propose a GRN model that is subject to additive and multiplicative noises as well as time-varying delays. The time-varying delay is assumed to belong to an interval and no restriction on the derivative of the time-varying delay is needed, which allows the delay to be a fast time-varying function. Robust stochastic stability of such GRNs with disturbance attenuation is analyzed by applying the control theory and mathematical tools. Based on the Lyapunov method, new stability conditions are derived in the form of linear matrix inequalities that are dependent on the upper and lower bounds of time delays. An example is employed to illustrate the applicability and usefulness of the developed theoretical results. [less ▲]

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See detailRobust synchronization in networks of cyclic feedback systems
Hamadeh, A. O.; Goncalves, Jorge UL; Stan, G. B. V.

in The proceedings of the 47th IEEE Conference on Decision and Control (2008)

This paper presents a result on the robust synchronization of outputs of statically interconnected non-identical cyclic feedback systems that are used to model, among other processes, gene expression. The ... [more ▼]

This paper presents a result on the robust synchronization of outputs of statically interconnected non-identical cyclic feedback systems that are used to model, among other processes, gene expression. The result uses incremental versions of the small gain theorem and dissipativity theory to arrive at an upper bound on the norm of the synchronization error between corresponding states, giving a measure of the degree of convergence of the solutions. This error bound is shown to be a function of the difference between the parameters of the interconnected systems, and disappears in the case where the systems are identical, thus retrieving an earlier synchronization result. [less ▲]

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See detailRobust synchronization of heterogeneous robot swarms on the sphere
Markdahl, Johan UL; Proverbio, Daniele UL; Goncalves, Jorge UL

in 2020 59th IEEE Conference on Decision and Control (CDC) (2020)

Synchronization on the sphere is important to certain control applications in swarm robotics. Of recent interest is the Lohe model, which generalizes the Kuramoto model from the circle to the sphere. The ... [more ▼]

Synchronization on the sphere is important to certain control applications in swarm robotics. Of recent interest is the Lohe model, which generalizes the Kuramoto model from the circle to the sphere. The Lohe model is mainly studied in mathematical physics as a toy model of quantum synchronization. The model makes few assumptions, wherefore it is well-suited to represent a swarm. Previous work on this model has focused on the cases of complete and acyclic networks or the homogeneous case where all oscillator frequencies are equal. This paper concerns the case of heterogeneous oscillators connected by a non-trivial network. We show that any undesired equilibrium is exponentially unstable if the frequencies satisfy a given bound. This property can also be interpreted as a robustness result for small model perturbations of the homogeneous case with zero frequencies. As such, the Lohe model is a good choice for control applications in swarm robotics. [less ▲]

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See detailRobust Techniques for Building Footprint Extraction in Aerial Laser Scanning 3D Point Clouds
Nurunnabi, Abdul Awal Md UL; Teferle, Felix Norman UL; Balado, Jesus et al

in Robust Techniques for Building Footprint Extraction in Aerial Laser Scanning 3D Point Clouds (2022, November)

The building footprint is crucial for a volumetric 3D representation of a building that is applied in urban planning, 3D city modeling, cadastral and topographic map generation. Aerial laser scanning (ALS ... [more ▼]

The building footprint is crucial for a volumetric 3D representation of a building that is applied in urban planning, 3D city modeling, cadastral and topographic map generation. Aerial laser scanning (ALS) has been recognized as the most suitable means of large-scale 3D point cloud data (PCD) acquisition. PCD can produce geometric detail of a scanned surface. However, it is almost impossible to get point clouds without noise and outliers. Besides, data incompleteness and occlusions are two common phenomena for PCD. Most of the existing methods for building footprint extraction employ classification, segmentation, voting techniques (e.g., Hough-Transform or RANSAC), or Principal Component Analysis (PCA) based methods. It is known that classical PCA is highly sensitive to outliers, even RANSAC which is known as a robust technique for shape detection is not free from outlier effects. This paper presents a novel algorithm that employs MCMD (maximum consistency within minimum distance), MSAC (a robust variant of RANSAC) and a robust regression to extract reliable building footprints in the presence of outliers, missing points and irregular data distributions. The algorithm is successfully demonstrated through two sets of ALS PCD. [less ▲]

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See detailRobust Traffic Anomaly Detection with Principal Component Pursuit
Abdelkefi, Atef; Jiang, Yuming; Wang, Wei UL et al

in Proceedings of the ACM CoNEXT Student Workshop (2010)

Principal component analysis (PCA) is a statistical technique that has been used for data analysis and dimensionality reduction. It was introduced as a network traffic anomaly detection technique firstly ... [more ▼]

Principal component analysis (PCA) is a statistical technique that has been used for data analysis and dimensionality reduction. It was introduced as a network traffic anomaly detection technique firstly in [1]. Since then, a lot of research attention has been received, which results in an extensive analysis and several extensions. In [2], the sensitivity of PCA to its tuning parameters, such as the dimension of the low-rank subspace and the detection threshold, on traffic anomaly detection was indicated. However, no explanation on the underlying reasons of the problem was given in [2]. In [3], further investigation on the PCA sensitivity was conducted and it was found that the PCA sensitivity comes from the inability of PCA to detect temporal correlations. Based on this finding, an extension of PCA to Kalman-Loeve expansion (KLE) was proposed in [3]. While KLE shows slight improvement, it still exhibits similar sensitivity issue since a new tuning parameter called temporal correlation range was introduced. Recently, in [4], additional effort was paid to illustrate the PCA-poisoning problem. To underline this problem, an evading strategy called Boiled-Frog was proposed which adds a high fraction of outliers to the traffic. To defend against this, the authors employed a more robust version of PCA called PCA-GRID. While PCA-GRID shows performance improvement regarding the robustness to the outliers, it experiences a high sensitivity to the threshold estimate and the k-dimensional subspace that maximizes the dispersion of the data. The purpose of this work is to consider another technique to address the PCA poisoning problems to provide robust traffic anomaly detection: The Principal Component Pursuit. [less ▲]

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See detailRobustness analysis of cellular systems for in silico drug discovery
Perumal, Thanneer Malai UL; Wu, Y.; Gunawan, R.

Scientific Conference (2008)

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See detailThe role of microscale solid matrix compressibility on the mechanical behaviour of poroelastic materials
Dehghani, Hamidreza UL; Noll, Isabelle; Penta, Raimondo et al

in European Journal of Mechanics. A, Solids (2020), 83

We present the macroscale three-dimensional numerical solution of anisotropic Biot's poroelasticity, with coefficients derived from a micromechanical analysis as prescribed by the asymptotic ... [more ▼]

We present the macroscale three-dimensional numerical solution of anisotropic Biot's poroelasticity, with coefficients derived from a micromechanical analysis as prescribed by the asymptotic homogenisation technique. The system of partial differential equations (PDEs) is discretised by finite elements, exploiting a formal analogy with the fully coupled thermal displacement systems of PDEs implemented in the commercial software Abaqus. The robustness of our computational framework is confirmed by comparison with the well-known analytical solution of the one-dimensional Therzaghi's consolidation problem. We then perform three-dimensional numerical simulations of the model in a sphere (representing a biological tissue) by applying a given constant pressure in the cavity. We investigate how the macroscale radial displacements (as well as pressures) profiles are affected by the microscale solid matrix compressibility (MSMC). Our results suggest that the role of the MSMC on the macroscale displacements becomes more and more prominent by increasing the length of the time interval during which the constant pressure is applied. As such, we suggest that parameter estimation based on techniques such as poroelastography (which are commonly used in the context of biological tissues, such as the brain, as well as solid tumours) should allow for a sufficiently long time in order to give a more accurate estimation of the mechanical properties of tissues. [less ▲]

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