Research team

Industrial Vision Lab (InViLab)

Expertise

One of the main research topic is to develop visual servoing controllers and architectures and to investigate the human-robot interaction, which implies machine safety tasks and collaborative robots. Another research direction within the robotics laboratory is the use of robots/cobots in the biomedical area. One of the studies is accurate tracking position during radiotherapy, e.g. synchronization of the radiation focus to the movement of the lung tissue during breathing. In the last years, I have also investigated the problem of control of platoon vehicles in order to mitigate the stop and go effect on the highways. There is an increased interest from the automotive industry to employ control architectures which will contribute to the final product improvement.

Automated Open Precision Farming Platform (Utopia) 01/03/2021 - 29/02/2024

Abstract

Precision-farming needs large-scale adoption to increase production at such a level that it significantly contributes to minimizing the gap between actual and required world-production of food. Increasing the measurement and actuation intervals of e.g. monitoring for pests and watering are expected to contribute to e.g. increased yields. Sensing is an important element to quantify productivity, product quality and to make decisions. Applications, such as mapping, surveillance, exploration and precision agriculture, require a reliable platform for remote sensing. In precision agriculture, the goal is to gather and analyze information about the variability of soil/water and plant conditions in order to maximize the efficiency of the farm field. This would also increase the burden on the farmer, as the measurement-time and data-processing time increases significantly. This can be mitigated with Automated (cooperative) Precision Farming with the use of autonomous driving vehicles, vessels, drones and dedicated installations mounted on regular agri-machinery. For the cooperative robotic missions, the data will be tagged with accurate position information and merged with other data in order to create a digital map. To achieve good performance for an intelligent system in autonomous navigation tasks we will also build a 3D world model which will be integrated with a digital twin at plant level in order to improve the local path such that we obtain accurate information. To integrate the data from heterogeneous sensors, a platform will be developed to determine the practicality of the available sensors for the optimization of the spatio-temporal interpolation. This project will focus on a single (standardized) platform where (robotic)paths, monitoring strategies can be set and the drones/USV's/AGV's automatically deployed when certain conditions are met. The measurement data will be available for different stakeholders in the same platform.

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Development of a precision clinical plasma treatment system using environmental sensing and robotic controls. 01/07/2020 - 31/12/2021

Abstract

In the context of clinical treatment of cancers, a major challenge involves the precise delivery of therapeutic agents to the tumor while limiting off-target effects. This is true for multiple treatment modalities including radiotherapy and non-thermal plasma (NTP) therapy. Hence, the main focus of this research project is to introduce the design of a supervisory control structure into a patient-in-the-loop therapeutic application. This system will be developed by integrating 3 components: 1) environmental sensors, 2) a robotic control unit, and 3) a therapeutic device (NTP generator). Since NTP treatment is highly dependent on parameters such as treatment time, application distance, etc. a feedback approach is necessary to compensate for tumor motion induced by the patient during treatment (e.g. respiration). To this end, artificial intelligence tools, including neural networks, will be employed to model the dynamic disturbances of the tumor. The developed self-learning artificial intelligence models will be embedded within model-based controllers to predict and minimize the effect of disturbances. Performance of the control structure will be validated with real-time experiments of plasma delivery in biological systems. The proposed methodology has the potential to improve the precision and accuracy of clinical NTP treatment and consequently minimize damage to healthy tissue.

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Investigating fundamental plasma effects on tumor microenvironment through development of a controlled plasma treatment system for clinical cancer therapy. 01/01/2020 - 31/12/2023

Abstract

Non-thermal plasma technology is gaining attention as a novel cancer therapeutic. In the clinic, plasma has been applied to patients with head and neck squamous cell carcinoma, the 6th most common cancer worldwide with long-term survival below 50%. While initial studies are promising (e.g. partial remission, decreased levels of pain, no reported side-effects), a critical issue became apparent when translating plasma technology from the laboratory to the clinic: low reproducibility of treatment. Current plasma devices are handheld and require the operator (clinician) to make a judgement as to how long to treat the patient. This leads to large variability, which becomes even more pronounced when the clinician must move the plasma applicator over a large area of treatment. We aim to develop a robotic plasma treatment system that will enable us to investigate fundamental plasma effects on the tumor for clinical cancer therapy. We will use multiple sensors to detect the patient environment, artificial intelligence to 'learn and predict' patient disturbance patterns (e.g. breathing), and a robotic arm to deliver plasma. We will test our developed system in 3D and mouse cancer models and study the consequence of plasma treatment in the tumor, and to the survival of the animal. Altogether, our project will progress plasma technology for clinical translation by elucidating previously unknown biological responses to plasma and addressing issues in the clinic.

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Visual servoing control in a cluttered environment based on artificial intelligence. 01/04/2019 - 30/03/2020

Abstract

The necessity of designing flexible and versatile systems is one of the most current trends in robotic research. Including visual servoing techniques in an existing robotic system is a very challenging task. In this project a solution for extending the capabilities of a 6 DOF manipulator robot for visual servoing system development, is proposed. In order to achieve this task, different types of visual features (which can be extracted from the image using a visual sensor) are detected and their properties are analyzed. Here, visual features such as point features and image moments are taken into account for designing the controller. An image-based control architecture is designed and a real-time implementation on a manipulator robot is developed. The primary objective of this research project is to converge into an accurate algorithm for object reconstruction in a clutter environment and subsequently helping the robot to perform a visual servoing task. The object reconstruction is done by employing tools from artificial intelligence such as deep Convolutional Neural Network. The image acquisition and image processing together with the computing of the image-based control law will be implemented in Matlab. Thus, a new type of robot driving interface that links the robots' controller with Matlab environment is proposed. Such a user driver interface will allow not only to design and implement real-time controllers but also to perform other tasks such as identification, path planning, etc. Finally, the robustness and stability of the proposed visual feature based control law will be implemented, tested and validated in real-time through multiple experiments.

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Smart Granules - Dynamic control of nutrient removal in small-scale industrial aerobic granular sludge systems. 01/01/2017 - 31/12/2020

Abstract

Aerobic granular sludge (AGS) represents a revolutionary new biological treatment system based on the formation of very dense microbial aggregates. The current research proposal aims at developing an applicable dynamic control strategy for industrial AGS reactors, based on the signals of common, low-cost and robust sensors. A five step approach will be followed to reach this objective, starting from the development of a control strategy for "simple" well-defined AGS systems on laboratory scale to pilot-scale evaluation of an optimized control algorithm with real variable industrial wastewater.

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Non-linear and time-varying data-based modeling of rotating machinery. 01/07/2016 - 31/12/2017

Abstract

Rotating machines appear in many application fields ranging from large scale applications (e.g. wind turbines) to smaller ones (e.g. medical fluid pumps). The availability of a mathematical model for the dynamical behavior is of crucial importance for the design, prediction and control of these rotating systems. In the scientific domain of "system identification", the mathematical model of the system under test is retrieved through experimental input-output data. Since the dynamical characterization of rotating machines is non-linear as well as time-varying, it cannot be modeled adequately using classical existing estimation or identification methods. The aim of this project is then to develop a theoretical framework to model the time-varying and non-linear dynamical behavior of rotating machinery from experimental data. The proposed methodology consists of modeling the non-linear and time-varying dynamical character of rotating devices through a collection of linear periodically time-varying models. In this project, we will focus on the identification and validation of the non-linear and time-varying dynamics of a mechanical rotor suspended on hydrodynamic plain bearings. The novel approach consists of four main steps: (i) Construction of a non-linear and time-varying virtual model of "fluid-driven" bearing—rotor systems starting from the laws of physics; (ii) Development of a parametric identification technique; (iii) realization and adjustments of the controllable rotor—bearing setup; (iv) validation of the theoretical framework on the real-life rotor—bearing setup.

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