Browsing by Author "Torres-Torriti, Miguel"

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    An algorithm for processing block diagram models of dynamical systems and an open-source visual-programming simulation tool
    (2025) Torres-Torriti, Miguel; Rojas-Sepulveda, Matias
    Visual diagrammatic programming and block diagrams have been indispensable tools for systems modeling and simulation across research, development, and educational fields for several decades. Despite the availability of mature commercial and free software tools, there is a lack of information publicly accessible on algorithms for processing block diagrams that represent dynamical systems and simulate the corresponding models. A gap in the existing literature is the absence of mathematically formal and complete proposals of algorithms for processing block diagrams that are multigraphs containing directed cyclic graphs, and not just simpler directed graphs. The lack of a detailed exposition concerning the practical implementation of such algorithms is also a gap. This gap is likely because the simulation systems based on block diagram descriptions that have become de facto industry standards use proprietary solutions, even if their origins can be traced back to work done in research centers and universities more than seven decades ago. In response to these challenges, this paper summarizes the historical evolution of related paradigms, such as data flow diagrams, signal flow graphs, bond graphs, and block diagrams. We propose a general algorithm for block diagram processing and present an open-source software tool for Python that implements a diagrammatic visual programming interface and the proposed block diagram processing algorithm. The key contributions to the field of systems modeling and simulation can be summarized as follows. Firstly, the exposition of the algorithm formally proving its correctness, offers transparency which facilitates further research and development in the field, enabling academics and professionals to adapt, enhance, or expand upon the existing capabilities of the tool. Secondly, the implementation of a Python library and tool released as an open-source solution for simulating signal processing and dynamical systems through block diagrams by integrating the proposed algorithm capable of efficiently handling multigraph representations, including those with directed cyclic graphs. Ensuring accessibility of the tool to researchers, developers, and educators fosters innovation, research and empowers educators by providing them with a versatile tool that can be used to teach complex systems modeling and simulation concepts in a practical, hands-on manner across various domains, such as control systems, electrical engineering, and computer science.
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    Comparison of 3D scan matching techniques for autonomous robot navigation in urban and agricultural environments
    (2021) Guevara, Javier; Gene-Mola, Jordi; Gregorio, Eduard; Torres-Torriti, Miguel; Reina, Giulio; Auat Cheein, Fernando A.
    Global navigation satellite system (GNSS) is the standard solution for solving the localization problem in outdoor environments, but its signal might be lost when driving in dense urban areas or in the presence of heavy vegetation or overhanging canopies. Hence, there is a need for alternative or complementary localization methods for autonomous driving. In recent years, exteroceptive sensors have gained much attention due to significant improvements in accuracy and cost-effectiveness, especially for 3D range sensors. By registering two successive 3D scans, known as scan matching, it is possible to estimate the pose of a vehicle. This work aims to provide in-depth analysis and comparison of the state-of-the-art 3D scan matching approaches as a solution to the localization problem of autonomous vehicles. Eight techniques (deterministic and probabilistic) are investigated: iterative closest point (with three different embodiments), normal distribution transform, coherent point drift, Gaussian mixture model, support vector-parametrized Gaussian mixture and the particle filter implementation. They are demonstrated in long path trials in both urban and agricultural environments and compared in terms of accuracy and consistency. On the one hand, most of the techniques can be successfully used in urban scenarios with the probabilistic approaches that show the best accuracy. On the other hand, agricultural settings have proved to be more challenging with significant errors even in short distance trials due to the presence of featureless natural objects. The results and discussion of this work will provide a guide for selecting the most suitable method and will encourage building of improvements on the identified limitations. (C) 2021 Society of PhotoOptical Instrumentation Engineers (SPIE)
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    Distributed Tube-Based Nonlinear MPC for Motion Control of Skid-Steer Robots With Terra-Mechanical Constraints
    (2021) Prado, Alvaro J.; Torres-Torriti, Miguel; Cheein, Fernando A.
    Strategies to reduce slippage and disturbing wheelterrain interactions are essential to improve navigation and motion control of field robots. Thus, this work proposes an integral control architecture based on a distributed tube-based nonlinear Model Predictive Control scheme to regulate tire dynamics and an adaptive model-based control scheme for trajectory tracking over deformable terrain. For the proposed control architecture, the overall system is decomposed into simpler subsystems to separately represent the four-tire driven motion dynamics (i.e., slip and sideslip) from that of the vehicle's pose and speeds. Since a vehicle and its tires have different dynamic response characteristics, cooperative agents of the distributed control strategy are able to exchange information between subsystems to attain evenly allocated drivetrain torques during slippery situations. The motion controller is made adaptive to terra-mechanical parameters with a NonlinearMoving Horizon Estimation approach working under a parallel Real-Time Iteration scheme. Field experimentations in an industrial compact loader Cat degrees 262 C subject to off-road conditions demonstrated that the proposed approach was capable of reducing up to a minimum of 18.2% of tire slip and sidelip range of +/- 6.6 degrees when compared to its non-robust counterpart. Consequently, the proposed approach was also able to reduce lateral and longitudinal trajectory tracking errors by around 66.6% and 43.7%, respectively, which may have a direct impact on the resources of the machinery.
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    GNSS-Based Estimation of Average Instantaneous Power Consumption in Electric Vehicles
    (2023) Bustos, Javier A. Torres; Orchard, Marcos E.; Torres-Torriti, Miguel; Cheein, Fernando Auat
    Several countries are currently leading the challenge of replacing internal combustion engines (ICE) in vehicles by electrically powered ones, mainly motivated by the goal of reducing the dependence on oil and the carbon footprint. However, the autonomy of electric vehicles (EVs) remains significantly lower than their ICE counterpart: it depends on the amount of energy stored and its rate of utilization, characterized by the instantaneous power consumption (IPC). Although IPC can be measured from on-board voltage and current sensors, in route planning applications it is of paramount importance to generate prior estimates from other sources of information, such as the mass of the vehicle, its velocity, acceleration, aerodynamic, and rolling resistances (among other vehicle and terrain parameters). In this work, we propose the estimation of average IPC for a given path, using only localization information provided by a global navigation satellite system antenna, at different levels of errors. Our work is experimentally validated with an EV on the road, achieving on average a 99.7% of power estimation accuracy for a localization error of +/- 1.5 m-as best case-and 73.6% accuracy for +/- 8.6 m of localization error-as worst case. These results encourages us to use only position data to estimate average IPC of EVs, avoiding the need of placing extra sensors into the vehicles.
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    Markov Chain Monte Carlo Parameter Estimation for Nonzero Slip Models of Wheeled Mobile Robots: A Skid-Steer Case Study
    (2021) Yandun, Francisco; Torres-Torriti, Miguel; Cheein, Fernando Auat
    An accurate modeling, simulation, and estimation of the wheel-terrain interaction and its effects on a robot movement plays a key role in control and navigation tasks, specially in constantly changing environments. We study the calibration of wheel slip models using Particle Markov Chain Monte Carlo methods to approximate the posterior distributions of their parameters. In contrast to classic identification approaches, considering the parameters as random variables allows to obtain a probability measure of the parameter estimations and subsequently propagate their uncertainty to wheel slip-related variables. Extensive simulation and experimental results showed that the proposed methodology can effectively get reliable posterior approximations from noisy sensor measurements in changing terrains. Validation tests also include the applicability assessment of the proposed methodology by comparing it with the integrated prediction error minimization methodology. Field results presented up to 66% of improvement in the robot motion prediction with the proposed calibration approach.
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    Model predictive path-following controller for Generalised N-Trailer vehicles with noisy sensors and disturbances
    (2024) Deniz, Nestor; Jorquera, Franco; Torres-Torriti, Miguel; Cheein, Fernando Auat
    Generalised N-Trailer (GNT) vehicles, commonly used in agriculture, mining, and industry, consist of a tractor pulling multiple passive trailers. This paper presents a nonlinear moving horizon estimator (NMHE) and nonlinear model predictive controller (NMPC) for GNT robotic vehicles. The NMHE accurately estimates the vehicle's state under challenging conditions such as noisy measurements, disturbances, and different soil conditions, without relying on assumptions about disturbances, making it more effective than techniques like the Extended Kalman Filter (EKF). Similarly, the NMPC successfully steers the GNT along a predefined path with lower control effort compared to existing algorithms, thanks to smoother tractor manoeuvres that minimise energy in the objective function. Moreover, unlike other methods, this approach computes the tractors' velocities instead of those of the last trailer, eliminating the need for kinematic inversion and making it suitable for various vehicle configurations. Efficient solvers for the NMHE and NMPC problems were generated using two different open-source frameworks. The proposed framework was evaluated through challenging simulated studies and field experiments. Additional resources, including code implementation and field experiment videos, can be found at https://drive.google.com/drive/folders/1n-qVWJA40cZn-WiDpwXhhF9AdK54LTfX?usp=sharing.
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    Passive Landmark Geometry Optimization and Evaluation for Reliable Autonomous Navigation in Mining Tunnels Using 2D Lidars
    (2022) Torres-Torriti, Miguel; Nazate-Burgos, Paola; Paredes-Lizama, Fabian; Guevara, Javier; Auat Cheein, Fernando
    Autonomous navigation in mining tunnels is challenging due to the lack of satellite positioning signals and visible natural landmarks that could be exploited by ranging systems. Solutions requiring stable power feeds for locating beacons and transmitters are not accepted because of accidental damage risks and safety requirements. Hence, this work presents an autonomous navigation approach based on artificial passive landmarks, whose geometry has been optimized in order to ensure drift-free localization of mobile units typically equipped with lidar scanners. The main contribution of the approach lies in the design and optimization of the landmarks that, combined with scan matching techniques, provide a reliable pose estimation in modern smoothly bored mining tunnels. A genetic algorithm is employed to optimize the landmarks' geometry and positioning, thus preventing that the localization problem becomes ill-posed. The proposed approach is validated both in simulation and throughout a series of experiments with an industrial skid-steer CAT 262C robotic excavator, showing the feasibility of the approach with inexpensive passive and low-maintenance landmarks. The results show that the optimized triangular and symmetrical landmarks improve the positioning accuracy by 87.5% per 100 m traveled compared to the accuracy without landmarks. The role of optimized artificial landmarks in the context of modern smoothly bored mining tunnels should not be understated. The results confirm that without the optimized landmarks, the localization error accumulates due to odometry drift and that, contrary to the general intuition or belief, natural tunnel features alone are not sufficient for unambiguous localization. Therefore, the proposed approach ensures grid-based SLAM techniques can be implemented to successfully navigate in smoothly bored mining tunnels.
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    Restoration of images with a spatially varying PSF of the T80-S telescope optical model using neural networks
    (2022) Bernardi, Rafael L.; Berdja, Amokrane; Dani Guzman, Christian; Torres-Torriti, Miguel; Roth, Martin M.
    Most image restoration methods in astronomy rely upon probabilistic tools that infer the best solution for a deconvolution problem. They achieve good performances when the point spread function (PSF) is spatially invariant in the image plane. However, this condition is not always satisfied in real optical systems. We propose a new method for the restoration of images affected by static and anisotropic aberrations using Deep Neural Networks that can be directly applied to sky images. The network is trained using simulated sky images corresponding to the T80-S Telescope optical model, a 80-cm survey imager at Cerro Tololo (Chile), which are synthesized using a Zernike polynomial representation of the optical system. Once trained, the network can be used directly on sky images, outputting a corrected version of the image that has a constant and known PSF across its field of view. The method is to be tested on the T80-S Telescope. We present the method and results on synthetic data.
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    Restoration of T80-S telescope's images using neural networks
    (2023) Bernardi, Rafael L.; Berdja, Amokrane; Guzman, Christian Dani; Torres-Torriti, Miguel; Roth, Martin M.
    Convolutional neural networks (CNNs) have been used for a wide range of applications in astronomy, including for the restoration of degraded images using a spatially invariant point spread function (PSF) across the field of view. Most existing development techniques use a single PSF in the deconvolution process, which is unrealistic when spatially variable PSFs are present in real observation conditions. Such conditions are simulated in this work to yield more realistic data samples. We propose a method that uses a simulated spatially variable PSF for the T80-South (T80-S) telescope, an 80-cm survey imager at Cerro Tololo (Chile). The synthetic data use real parameters from the detector noise and atmospheric seeing to recreate the T80-S observational conditions for the CNN training. The method is tested on real astronomical data from the T80-S telescope. We present the simulation and training methods, the results from real T80-S image CNN prediction, and a comparison with space observatory Gaia. A CNN can fix optical aberrations, which include image distortion, PSF size and profile, and the field position variation while preserving the source's flux. The proposed restoration approach can be applied to other optical systems and to post-process adaptive optics static residual aberrations in large-diameter telescopes.
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    Tube-based nonlinear model predictive control for autonomous skid-steer mobile robots with tire-terrain interactions
    (2020) Javier Prado, Alvaro; Torres-Torriti, Miguel; Yuz, Juan; Auat Cheein, Fernando
    This work addresses the problem of robust tracking control for skid-steer mobile platforms, using tube-based Nonlinear Model Predictive Control. The strategy seeks to mitigate the impact of disturbances propagated to autonomous vehicles originated by traction losses. To this end, a dynamical model composed by two coupled sub-systems stands for lateral and longitudinal vehicle dynamics and fire behavior. The controller is aimed at tracking prescribed stable operation points of the slip and side-slip beyond the robot pose and speeds. To reach robust tracking performance on the global system, a centralized control scheme operates under a predictive control framework composed by three control actions. The first one compensates for disturbances using the reference trajectory-feedforward control. The second control action corrects the errors generated by the modeling mismatch. The third one is devoted to ensure robustness on the closed-loop system whilst compensating for deviations of the state trajectories from the nominal ones (i.e. disturbance-free). The strategy ensures robust feasibility even when tightening constraints are met. Such constraints are calculated on-line based on robust positively invariant sets characterized by polytopic sets (tubes), including a terminal region to guarantee robustness. The benefits of the controller regarding tracking performance, constraint satisfaction and computational practicability were tested through simulations with a Cat (R) 262C skid-steer model. Then, in field tests, the controller evidenced high tracking accuracy against terrain disturbances when benchmarking performance with respect to inherent robust predictive controllers.