TR-Dizin İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/396

Browse

Filters

2020 - 20223

Settings

Language: search.filters.language.trSubject: search.filters.subject.Robotik

Search Results

Now showing 1 - 3 of 3
  • Research Project
    Elektromanyetik Levitasyon ile Çalışan Biyosensör-Mikrorobot Sistemlerinin Geliştirilmesi ve Kontrolü
    (2020) Icoz, Kutay; Ablay, Günyaz
    Bu arastırma mikron seviyesinde hareket etme yetenegine sahip manyetik levitasyon ile çalısan biyosensör-mikrorobot tasarımını gerçeklestirmeye çalısmaktadır. Manyetik levitasyon teknigi, mikro/nano manyetik parçacıklar ile kuvvetlendirilmis veya paramanyetik bir ortama serpilmis biyolojik varlıkların (tümör hücresi gibi) tespitinde veya analizinde kullanılabilir. Benzer mantıkla, kontrollü manyetik levitasyon ile mikro-manyetik parçacıklar içeren mikrorobotlar gelistirilerek mikron seviyesindeki tekrarlanan çesitli görevlerin otomatik bir sekilde yapılması saglanabilir. Manyetik levitasyon tahrik sistemleri biyolojik ortamlarda zararsızdır, nahos ortam sartlarında çalısabilmektedir ve sürtünmenin etkisini minimize edebilme özelligine sahiptir. Mikrorobot teknolojisi ile minyatür parçalar belli bir hedef noktaya tasınabilir ve nahos/tehlikeli ortamlarda kurulabilirler. Bu proje, etkin ve otomatik mikro-parçacık manipülasyonu için geribeslemeli kontrol yapılarından olusan ve yatay eksende bir ve iki boyutlu manipülasyon imkanı saglayan bir elektromanyetik aktüatör tabanlı manyetik mikromanipülatör tasarımı ve uygulaması üzerine yapılmıstır. Elektromıknatıs tasarımında, uygulanan kontrol akımı ve elektromıknatıs konfigürasyonu manyetik kuvvet ve tork degerlerini belirlemektedir ve bundan dolayı en uygun, kuvvetli ve hassas bir tasarım için uygun nüve yapılarıyla beraber geribeslemeli kontrol mekanizmasının gelistirilmesine ihtiyaç vardır. Manyetik aktüatörlerin, 1 ila 10 ?m çaplı süperparamanyetik parçacık üzerinde yaklasık olarak 1 ila 25 pN kuvvet üretmesi amaçlanmıstır. Bunun için 6-8 mm boyundaki koni sekilli uca sahip nikel-demir alasımlı nüve ve 2000 bakır sarımından yapılmıs bir, iki ve dört elektromıknatıstan olusan konfigürasyonlar elde edildi. Manyetik mikromanipülatör, ilk prensipler yoluyla modellendi ve bu model yardımıyla iki farklı kontrol metodu önerildi. Ilk kontrolör ofset akım tabanlı lineer kontrolör olup modeldeki lineer olmayan terimleri dogrusallastırabilme özelligine sahiptir. Ikinci kontrolör ise integral geriadımlama tabanlı nonlineer bir kontrolör olup yumusak ve etkin kontrol akımları üretebilmektedir. Tasarlanan kontrolörlerin bir boyutta ve 2-boyutta sistemin kapalı çevrimli dinamigini kararlı hale getirdigi, hızlı geçici rejim yanıtı verdigi ve sıfır kararlı durum hatası verdigi deneysel çalısmalarla gösterilmistir. Tasarlanan elektromanyetik mikromanipülatör özellikle biyolojik ayrıstırma, tıp ve biyosensör gelistirilmesi gibi alanlarda kullanılabilecek genis bir kuvvet aralıgında çalısabilme kapasitesine sahiptir.
  • Research Project
    Biyonik Elin Faaliyete Hazırlanmasında Kaldırılacak Cisme dair Ağırlık Algısının Beyin Sinyalleriyle Belirlenmesi
    (2022) Ulutabanca, Halil; Altindis, Fatih; Unal, Ramazan; Yilmaz, Bulent; Sarrafıkhosrowshah, Mahsa
    The upper extremity prostheses vary due to the patient?s articulation level and the methods used to move them. There are prostheses that are either cosmetic, or that work with shoulder movement (mechanical), or controlled by myoelectronic and electroencephalography (EEG) signals. However, intuitive and unnatural control of the prosthesis places a great mental burden on the user. In this project, the aim is to develop a system to improve the control of the bionic hand prosthesis by using EEG and EMG signals together, by making use of the user's visual weight perception. With this system, it is aimed to reduce the physical and mental burden/discomfort patients may experience while using a mechanical prosthesis. The preconditioning of the prototype hand to be produced is provided by evaluating the weight of the objects seen by the patients to the extent that the brain perceives them visually. In this way, the force exerted by the patient on the shoulder while holding the object will decrease and the mental load will be alleviated. For this purpose, EEG and electromyography (EMG) signals of the subjects were taken and processed, and then a real-time implementation was developed. In the first stage, a study was conducted that aimed to operate the prosthesis by using the motor intention waves of the prosthesis users and the classification success of the machine learning approaches (detection of the intention to activate the prosthesis) was examined by taking EEG data from 30 healthy participants. In the second stage, EEG and EMG signals of 31 healthy participants were recorded synchronously while reaching for the object, lifting the object and leaving the object in the starting position. After the features of these signals were determined, it was determined that the object was heavy, medium weight or light using various classification approaches. In parallel with biosignal processing studies, prosthetic hand and wrist designs and three- dimensional prints were obtained. It is aimed to use the shoulder movement to open and close the prosthetic hand, and to control the wrist stiffness, to process the biosignals and drive a tiny motor with high torque with the automatic decision produced. In addition, the characterization of the prosthesis was made. As a result of the classification of the multi-channel EEG signals from 20 healthy individuals with Fourier-based synchrosequeezing transform (FSST) and singular value decomposition (SVD) approaches by extracting features, the goal was to control the stiffness of the wrist part of the prosthesis. As a result, it was possible for the system to detect the weight of the object the user sees while employing the prosthesis and to precondition the prosthesis according to this weight when they want to hold and move that object.
  • Research Project
    Biyonik Elin Faaliyete Hazırlanmasında Kaldırılacak Cisme Dair Ağırlık Algısının Beyin Sinyalleriyle Belirlenmesi
    (2022) Ulutabanca, Halil; Altindis, Fatih; Unal, Ramazan; Yilmaz, Bulent; Sarrafıkhosrowshah, Mahsa
    The upper extremity prostheses vary due to the patient?s articulation level and the methods used to move them. There are prostheses that are either cosmetic, or that work with shoulder movement (mechanical), or controlled by myoelectronic and electroencephalography (EEG) signals. However, intuitive and unnatural control of the prosthesis places a great mental burden on the user. In this project, the aim is to develop a system to improve the control of the bionic hand prosthesis by using EEG and EMG signals together, by making use of the user's visual weight perception. With this system, it is aimed to reduce the physical and mental burden/discomfort patients may experience while using a mechanical prosthesis. The preconditioning of the prototype hand to be produced is provided by evaluating the weight of the objects seen by the patients to the extent that the brain perceives them visually. In this way, the force exerted by the patient on the shoulder while holding the object will decrease and the mental load will be alleviated. For this purpose, EEG and electromyography (EMG) signals of the subjects were taken and processed, and then a real-time implementation was developed. In the first stage, a study was conducted that aimed to operate the prosthesis by using the motor intention waves of the prosthesis users and the classification success of the machine learning approaches (detection of the intention to activate the prosthesis) was examined by taking EEG data from 30 healthy participants. In the second stage, EEG and EMG signals of 31 healthy participants were recorded synchronously while reaching for the object, lifting the object and leaving the object in the starting position. After the features of these signals were determined, it was determined that the object was heavy, medium weight or light using various classification approaches. In parallel with biosignal processing studies, prosthetic hand and wrist designs and three- dimensional prints were obtained. It is aimed to use the shoulder movement to open and close the prosthetic hand, and to control the wrist stiffness, to process the biosignals and drive a tiny motor with high torque with the automatic decision produced. In addition, the characterization of the prosthesis was made. As a result of the classification of the multi-channel EEG signals from 20 healthy individuals with Fourier-based synchrosequeezing transform (FSST) and singular value decomposition (SVD) approaches by extracting features, the goal was to control the stiffness of the wrist part of the prosthesis. As a result, it was possible for the system to detect the weight of the object the user sees while employing the prosthesis and to precondition the prosthesis according to this weight when they want to hold and move that object.