Yuri Lopes

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Papers by Yuri Lopes

Multi-Operator Control of Connectivity-Preserving Robot Swarms Using Supervisory Control Theory

2022 International Conference on Robotics and Automation (ICRA), May 23, 2022

The attachment contains:- Extended SCT models for message relaying and robot exchange (PDF file)

Sensitivity analysis of building physical parameters to maximize heating energy saving using MPC

Buildings are one of the largest energy consumers and emitters of CO2, and they consume a signifi... more Buildings are one of the largest energy consumers and emitters of CO2, and they consume a significant portion of the overall energy in the world . There is an increased interest in using model-predictive control (MPC) to optimise building performance in many aspects such as saving heating energy . Further, as MPC uses future control scenarios and can optimise the outcomes, there is a great potential for linking to and optimising the use of energy storage within buildings using this control strategy. This study aims to investigate the influence of MPC on heating energy consumption while the indoor air temperature is maintained at an acceptable level. In the control of heating systems at each time step the system output (e.g. indoor air temperature) is measured and compared to desired references (e.g. indoor air temperature set point) and an appropriate change to the system inputs (e.g. the mass flow rate (MFR) going through a radiator) is employed. A refined solution is to use machine learning techniques to derive a model of the building dynamics based on collected data which is then used to optimise the choice of MFR. The model will take a sequence of MFR and other external variables (e.g. external temperature, solar radiation and wind) and predicts the indoor air temperature over a prediction horizon. MPC is employed to perform an optimisation over the prediction horizon. The optimisation searches for a sequence of MFR that minimises a cost function that considers the predicted indoor air temperatures against the desired reference (set point air temperatures). The cost function can consider a series of desirable features, such as maintaining the indoor air temperature to desired set points, reduce energy consumption, and actuator wear (e.g. valves). The case study is an office building located in the UK. Due to the response to external conditions (temperature, solar radiation, and wind), it is possible that the potential savings from the use of MPC vary depending on the buildings physical characteristics. Therefore, this research studies different physical parameters of building to characterise how MPC has an influence on building performance. The studied parameters are, a) two different thermal insulations as new and old buildings, b) three different floor areas of the building, and c) three orientations of the building. All buildings will be studied with an indoor air temperature set point of 22 °C (±1 °C). The before-mentioned parameters are separately and simultaneously studied to optimise building performance. According to these parameters, the results can show how the influence of MPC, for instance, a saving of heating energy consumption can be maximised for each parameter and all parameters.

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Model predictive control of non-domestic heating using genetic programming dynamic models

Applied Soft Computing, Dec 1, 2020

We present a novel approach to obtaining dynamic nonlinear models using genetic programming (GP) ... more We present a novel approach to obtaining dynamic nonlinear models using genetic programming (GP) for the model predictive control (MPC) of the indoor temperatures of buildings. Currently, the large-scale adoption of MPC in buildings is economically unviable due to the time and cost involved in the design and tuning of predictive models by expert control engineers. We show that GP is able to automate this process, and have performed open-loop system identification over the data produced by an industry grade building simulator. The simulated building was subject to an amplitude modulated pseudo-random binary sequence (APRBS), which allows the collected data to be sufficiently informative to capture the underlying system dynamics under relevant operating conditions. In this initial report, we detail how we employed GP to construct the predictive model for MPC for heating a single-zone building in simulation, and report results of using this model for controlling the internal environmental conditions of the simulated single-zone building. We conclude that GP shows great promise for producing models that allow the MPC of building to achieve the desired temperature band in a single zone space.

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An application of Generative Adversarial Networks to improve automatic inspection in automotive manufacturing

Applied Soft Computing, Mar 1, 2023

Coherent movement of error-prone individuals through mechanical coupling

Nature Communications

We investigate how reliable movement can emerge in aggregates of highly error-prone individuals. ... more We investigate how reliable movement can emerge in aggregates of highly error-prone individuals. The individuals—robotic modules—move stochastically using vibration motors. By coupling them via elastic links, soft-bodied aggregates can be created. We present distributed algorithms that enable the aggregates to move and deform reliably. The concept and algorithms are validated through formal analysis of the elastic couplings and experiments with aggregates comprising up to 49 physical modules—among the biggest soft-bodied aggregates to date made of autonomous modules. The experiments show that aggregates with elastic couplings can shrink and stretch their bodies, move with a precision that increases with the number of modules, and outperform aggregates with no, or rigid, couplings. Our findings demonstrate that mechanical couplings can play a vital role in reaching coherent motion among individuals with exceedingly limited and error-prone abilities, and may pave the way for low-power...

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Multi-Operator Control of Connectivity-Preserving Robot Swarms Using Supervisory Control Theory

2022 International Conference on Robotics and Automation (ICRA)

The attachment contains:- Extended SCT models for message relaying and robot exchange (PDF file)

gpTreeConstantWFInVall.xml

Description of the dynamical model in Genetic Programming Markup Language (GPML) used for model p... more

Sensitivity analysis of building physical parameters to maximize heating energy saving using MPC

International Conference on Energy Harvesting, Storage, and Transfer, 2019

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A Soft-Bodied Modular Reconfigurable Robotic System Composed of Interconnected Kilobots

2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS), 2019

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Model predictive control of non-domestic heating using genetic programming dynamic models

Applied Soft Computing, 2020

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Multi-Operator Control of Connectivity-Preserving Robot Swarms Using Supervisory Control Theory

2022 International Conference on Robotics and Automation (ICRA), May 23, 2022

The attachment contains:- Extended SCT models for message relaying and robot exchange (PDF file)

Coherent movement of error-prone individuals through mechanical coupling

Nature Communications

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Multi-Operator Control of Connectivity-Preserving Robot Swarms Using Supervisory Control Theory

2022 International Conference on Robotics and Automation (ICRA)

The attachment contains:- Extended SCT models for message relaying and robot exchange (PDF file)

A Soft-Bodied Modular Reconfigurable Robotic System Composed of Interconnected Kilobots

2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS), 2019

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Supervisory control theory for controlling swarm robotics systems

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A Generative Adversarial Network approach for automatic inspection in automotive assembly lines

Anais Estendidos do XXXV Conference on Graphics, Patterns and Images (SIBGRAPI Estendido 2022)

In manufacturing systems, quality of inspection is a critical issue. This can be conducted by hum... more In manufacturing systems, quality of inspection is a critical issue. This can be conducted by humans, or by employing Computer Vision Systems (CVS) which are trained upon representative datasets of images to detect classes of defects that may occur. The construction of such datasets strongly limits the use of CVS methods, as the variety of defects has combinatorial nature. Alternatively, instead of recognizing defects, a system can be trained to detect non-defective standards, becoming appropriate for some application profiles. In flexible automotive manufacturing, for example, parts are assembled within a reduced set of correct combinations, while the amount of possible incorrect assembling is enormous. In this paper, we show how a CVS can be extended with a Deep Learning-based approach that exploits a Generative Adversarial Network (GAN) to detect non-defective production, eliminating the need for constructing defect image datasets. The proposal is tested over the assembly line of...

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A Systematic Review of Spatial Approximations in Spatial Database Systems

Journal of Information and Data Management

Many applications rely on spatial information retrieval, which involves costly computational geom... more Many applications rely on spatial information retrieval, which involves costly computational geometric algorithms to process spatial queries. Spatial approximations simplify the geometric shape of complex spatial objects, allowing faster spatial queries at the expense of result accuracy. In this sense, spatial approximations have been employed to efficiently reduce the number of objects under consideration, followed by a refinement step to restore accuracy. For instance, spatial index structures employ spatial approximations to organize spatial objects in hierarchical structures (e.g., the R-tree). It leads to the interest in studying how spatial approximations can be efficiently employed to improve spatial query processing. This article presents a systematic review on this topic. We gather relevant studies by performing a search string on several digital libraries. We further expand the studies under consideration by employing a single iteration of the snowballing approach, where w...

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Supervisory Control of Robot Swarms Using Public Events

2020 IEEE International Conference on Robotics and Automation (ICRA)

Supervisory Control Theory (SCT) provides a formal framework for controlling discrete event syste... more Supervisory Control Theory (SCT) provides a formal framework for controlling discrete event systems. It has recently been used to generate correct-byconstruction controllers for swarm robotics systems. Current SCT frameworks are limited, as they support only (private) events that are observable within the same robot. In this paper, we propose an extended SCT framework that incorporates (public) events that are shared among robots. The extended framework allows to model formally the interactions among the robots. It is evaluated using a case study, where a group of mobile robots need to synchronise their movements in space and time-a requirement that is specified at the formal level. We validate our approach through experiments with groups of e-puck robots.

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The Application of Spatial Approximations to Spatial Query Processing: A Systematic Review of Literature

Anais do XXXVI Simpósio Brasileiro de Banco de Dados (SBBD 2021), 2021

Spatial approximations simplify the geometric shape of complex spatial objects. Hence, they have ... more Spatial approximations simplify the geometric shape of complex spatial objects. Hence, they have been employed to alleviate the evaluation of costly computational geometric algorithms when processing spatial queries. For instance, spatial index structures employ them to organize spatial objects in tree structures (e.g., the R-tree). We report experiments considering two real datasets composed of ∼1.5 million regions and ∼2.7 million lines. The experiments confirm the performance benefits of spatial approximations and spatial index structures. However, we also identify that a second processing step is needed to deliver the final answer and often requires higher processing time than the step that uses index structures only. It leads to the interest in studying how spatial approximations can be efficiently used to improve both steps. This paper presents a systematic review on this topic. As a result, we provide an overview and comparison of existing approaches that propose, evaluate, o...

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Probabilistic Supervisory Control Theory (pSCT) Applied to Swarm Robotics (Poster)

Swarm robotics studies large groups of robots that work together to accomplish common tasks. Much... more Swarm robotics studies large groups of robots that work together to accomplish common tasks. Much of the used source code is developed in an ad-hoc manner, meaning that the correctness of the controller is not always verifiable. In previous work, supervisory control theory (SCT) and associated design tools have been used to address this problem. Given a formal description of the swarm's agents capabilities and their desired behaviour, the control source code can be automatically generated. However, regular SCT cannot model probabilistic controllers (supervisors). In this paper, we propose a probabilistic supervisory control theory (pSCT) framework. It applies prior work on probabilistic generators in a way that allows controllers to be decomposed into multiple local modular supervisors. Local modular supervisors take advantage of the modularity of formal specifications to reduce the size required to store the control logic.To validate the pSCT framework, we model a distributed swarm robotic version of the graph colouring problem and automatically generate the control source code for the Kilobot swarm robotics platform. We report the results of systematic experiments with swarms of 25 and 100 physical robots.

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