Διεπαφές Ηλεκτρομυογραφικών Σημάτων για την Αλληλεπίδραση Ανθρώπου Ρομποτικών Συστημάτων σε Δομημένα και Δυναμικά Περιβάλλοντα

173 σ.Σε αυτή την διδακτορική διατριβή επικεντρωνόμαστε σε διεπαφές ηλεκτρομυογραφικών σημάτων οι οποίες μπορούν να χρησιμοποιηθούν για εφαρμογές αλληλεπίδρασης ανθρώπου ρομποτικών συστημάτων, τόσο σε δομημένα όσο και σε δυναμικά περιβάλλοντα. Αρχικά παρουσιάζουμε μια σειρά από προηγμένα σχήματα μηχανικής μάθησης για διεπαφές ηλεκτρομυογραφικών σημάτων, τα οποία συνδυάζουν έναν ταξινομητή με έναν παλινδρομητή, προκειμένου να κατακερματίσουν τον χώρο δράσης του ρομπότ, προσφέροντας καλύτερα αποτελέσματα αποκωδικοποίησης της ανθρώπινης κίνησης με μοντέλα εκπαιδευμένα για συγκεκριμένες διεργασίες. Όσον αφορά τις εφαρμογές αλληλεπίδρασης ανθρώπου ρομπότ, επικεντρωνόμαστε κυρίως στη έννοια και τις διαφορετικές χρήσεις του ανθρωπομορφισμού των ρομποτικών συστημάτων. Αρχικά διακρίνουμε τις διαφορετικές έννοιες του ανθρωπομορφισμού και εισάγουμε την έννοια του λειτουργικού ανθρωπομορφισμού για σχήματα αντιστοίχησης της ανθρώπινης κίνησης σε ανθρωπομορφική ρομποτική κίνηση, τηρώντας παράλληλα συγκεκριμένους περιορισμούς που θέτει ο χρήστης. Στην συνέχεια προτείνουμε μια ολοκληρωμένη μεθοδολογία για την ποσοτικοποίηση του ανθρωπομορφισμού των ρομποτικών χεριών, βασισμένη σε μεθόδους θεωρίας συνόλων και υπολογιστικής γεωμετρίας. Η συγκεκριμένη μεθοδολογία παρέχει ένα κατανοητό μετρικό του ανθρωπομορφισμού το οποίο κυμαίνεται από 0 (μη-ανθρωπομορφικά ρομποτικά συστήματα) σε 1 (ανθρωπομορφικά ρομποτικά συστήματα) και μπορεί να χρησιμοποιηθεί για διαφορετικά είδη ρομπότ. Τέλος, αναπτύσσουμε μια σειρά από ρομποτικά χέρια, ανοιχτού υλικού και κώδικα, τα οποία είναι ελαφριά, χαμηλού κόστους, εύκολα συναρμολογούμενα, υποϋπενεργούμενα και εγγενώς υποχωρητικά. Τα συγκεκριμένα χέρια μπορούν να χρησιμοποιηθούν τόσο για μελέτες ηλεκτρομυογραφικού ελέγχου (ακόμη και για οικονομικά μυοηλεκτρικά προσθετικά χέρια), όσο και για εφαρμογές αλληλεπίδρασης ανθρώπου ρομποτικών συστημάτων (για μελέτες τηλεχειρισμού ρομποτικών συστημάτων βραχίονα – χεριού), για την αρπαγή πληθώρας καθημερινών αντικειμένων σε δυναμικά περιβάλλοντα (ακόμη και υπό συνθήκες αβεβαιότητας σχετικά με τη θέση και το σχήμα των αντικειμένων). Προκειμένου να αποδείξουμε την αποδοτικότητα και λειτουργικότητα των προτεινόμενων μεθοδολογιών, εκτελέσαμε σειρά πειραμάτων με διαφορετικά ρομποτικά συστήματα, τόσο σε δυναμικά όσο και σε δομημένα περιβάλλοντα.In this PhD thesis we focus on EMG based interfaces that can be efficiently used for Human Robot Interaction (HRI) applications in structured and dynamic environments. Initially, we present a series of advanced learning schemes for EMG based interfaces that take advantage of both a classifier and a regressor, in order to split the task-space and provide better human motion estimation accuracy with task specific models. Regarding HRI applications, we mainly focus on anthropomorphism of robot artifacts. At first we distinguish between the different notions of anthropomorphism and we introduce Functional Anthropomorphism for mapping human to anthropomorphic robot motion, respecting at the same time specific human imposed functional constraints. Then we propose a methodology for quantifying anthropomorphism of robot hands, based on set theory and computational geometry methods. This latter methodology concludes to a comprehensive score of anthropomorphism that ranges between 0 (non-humanlike) and 1 (human identical) and can be used for various robot artifacts. Subsequently, we develop a series of open-source, modular, intrinsically-compliant, low-cost, light-weight, underactuated robot hands that can be easily reproduced with off-the-self materials. The proposed hands, efficiently grasp a plethora of everyday life objects, under object pose and/or shape uncertainties and can be used for various HRI applications or even as affordable myoelectric prostheses. In order to prove the efficiency of the proposed methods, we have conducted numerous experiments involving different robot artifacts, operating in both structured and dynamic environments.Μηνάς Β. Λιαροκάπη

On Aerial Robots with Grasping and Perching Capabilities: A Comprehensive Review

Over the last decade, there has been an increased interest in developing aerial robotic platforms that exhibit grasping and perching capabilities not only within the research community but also in companies across different industry sectors. Aerial robots range from standard multicopter vehicles/drones, to autonomous helicopters, and fixed-wing or hybrid devices. Such devices rely on a range of different solutions for achieving grasping and perching. These solutions can be classified as: 1) simple gripper systems, 2) arm-gripper systems, 3) tethered gripping mechanisms, 4) reconfigurable robot frames, 5) adhesion solutions, and 6) embedment solutions. Grasping and perching are two crucial capabilities that allow aerial robots to interact with the environment and execute a plethora of complex tasks, facilitating new applications that range from autonomous package delivery and search and rescue to autonomous inspection of dangerous or remote environments. In this review paper, we present the state-of-the-art in aerial grasping and perching mechanisms and we provide a comprehensive comparison of their characteristics. Furthermore, we analyze these mechanisms by comparing the advantages and disadvantages of the proposed technologies and we summarize the significant achievements in these two research topics. Finally, we conclude the review by suggesting a series of potential future research directions that we believe that are promising

Leveraging human perception in robot grasping and manipulation through crowdsourcing and gamification

Robot grasping in unstructured and dynamic environments is heavily dependent on the object attributes. Although Deep Learning approaches have delivered exceptional performance in robot perception, human perception and reasoning are still superior in processing novel object classes. Furthermore, training such models requires large, difficult to obtain datasets. This work combines crowdsourcing and gamification to leverage human intelligence, enhancing the object recognition and attribute estimation processes of robot grasping. The framework employs an attribute matching system that encodes visual information into an online puzzle game, utilizing the collective intelligence of players to expand the attribute database and react to real-time perception conflicts. The framework is deployed and evaluated in two proof-of-concept applications: enhancing the control of a robotic exoskeleton glove and improving object identification for autonomous robot grasping. In addition, a model for estimating the framework response time is proposed. The obtained results demonstrate that the framework is capable of rapid adaptation to novel object classes, based purely on visual information and human experience

A hybrid, wearable exoskeleton glove equipped with variable stiffness joints, abduction capabilities, and a telescopic thumb

Robotic hand exoskeletons have become a popular and efficient technological solution for assisting people that suffer from neurological conditions and for enhancing the capabilities of healthy individuals. This class of devices ranges from rigid and complex structures to soft, lightweight, wearable gloves. In this work, we propose a hybrid (tendon-driven and pneumatic), lightweight, affordable, easy-to-operate exoskeleton glove equipped with variable stiffness, laminar jamming structures, abduction/adduction capabilities, and a pneumatic telescopic extra thumb that increases grasp stability. The efficiency of the proposed device is experimentally validated through five different types of experiments: i) abduction/adduction tests, ii) force exertion experiments that capture the forces that can be exerted by the proposed device under different conditions, iii) bending profile experiments that evaluate the effect of the laminar jamming structures on the way the fingers bend, iv) grasp quality assessment experiments that focus on the effect of the inflatable thumb on enhancing grasp stability, and v) grasping experiments involving everyday objects and seven subjects. The hybrid assistive, exoskeleton glove considerably improves the grasping capabilities of the user, being able to exert the forces required to execute a plethora of activities of daily living. All files that allow the replication of the device are distributed in an open-source manner

Assessing the Suitability and Effectiveness of Mixed Reality Interfaces for Accurate Robot Teleoperation

In this work, a Mixed Reality (MR) system is evaluated to assess whether it can be efficiently used in teleoperation tasks that require an accurate control of the robot end-effector. The robot and its local environment are captured using multiple RGB-D cameras, and a remote user controls the robot arm motion through Virtual Reality (VR) controllers. The captured data is streamed through the network and reconstructed in 3D, allowing the remote user to monitor the state of execution in real time through a VR headset. We compared our method with two other interfaces: i) teleoperation in pure VR, with the robot model rendered with the real joint states, and ii) teleoperation in MR, with the rendered model of the robot superimposed on the actual point cloud data. Preliminary results indicate that the virtual robot visualization is better than the pure point cloud for accurate teleoperation of a robot arm

On alternative uses of structural compliance for the development of adaptive robot grippers and hands

Adaptive robot hands are typically created by introducing structural compliance either in their joints (e.g., implementation of flexures joints) or in their finger-pads. In this paper, we present a series of alternative uses of structural compliance for the development of simple, adaptive, compliant and/or under-actuated robot grippers and hands that can efficiently and robustly execute a variety of grasping and dexterous, in-hand manipulation tasks. The proposed designs utilize only one actuator per finger to control multiple degrees of freedom and they retain the superior grasping capabilities of the adaptive grasping mechanisms even under significant object pose or other environmental uncertainties. More specifically, in this work, we introduce, discuss, and evaluate: (a) a design of pre-shaped, compliant robot fingers that adapts/conforms to the object geometry, (b) a hyper-adaptive finger-pad design that maximizes the area of the contact patches between the hand and the object, maximizing also grasp stability, and (c) a design that executes compliance adjustable manipulation tasks that can be predetermined by tuning the in-series compliance of the tendon routing system and by appropriately selecting the imposed tendon loads. The grippers are experimentally tested and their efficiency is validated using three different types of tests: (i) grasping tests that involve different everyday objects, (ii) grasp quality tests that estimate the contact area between the grippers and the objects grasped, and (iii) dexterous, in-hand manipulation experiments to evaluate the manipulation capabilities of the Compliance Adjustable Manipulation (CAM) hand. The devices employ mechanical adaptability to facilitate and simplify the efficient execution of robust grasping and dexterous, in-hand manipulation tasks

Electromyography based gesture decoding employing few-shot learning, transfer learning, and training from scratch

Over the last decade several machine learning (ML) based data-driven approaches have been used for Electromyography (EMG) based control of prosthetic hands. However, the performance of EMG-based frameworks can be affected by: i) the onset of fatigue due to long data collection sessions, ii) musculoskeletal differences between individuals, and iii) sensor position drifting between different sessions with the same user. To evaluate these aspects, in this work, we compare the performance of EMG-based hand gesture decoding models developed using three approaches. This comparison allows for future works in EMG-based Human-Machine Interfaces development to make more informed ML decisions. First, we trained from scratch a Transformer-based architecture, called Temporal Multi-Channel Vision Transformer (TMC-ViT). For our second approach, we utilized a pre-trained and fine-tuned TMC-ViT model (a transfer learning approach). Finally, for our third approach, we developed a Prototypical Network (a few-shot learning approach). The models are trained in a subject-specific and subject-generic manner for eight subjects and validated employing the 10-fold cross-validation procedure. This study shows that training a deep learning decoding model from scratch in a subject-specific manner leads to higher decoding accuracies when a larger dataset is available. For smaller datasets, subject-generic models, or inter-session models, the few-shot learning approach produces more robust results with better performance, and is more suited to applications where long data collection scenarios are not possible, or where multiple users are intended for the interface. Our findings show that the few-shot learning approach can outperform training a model from scratch in different scenarios

A Compact Ratchet Clutch Mechanism for Fine Tendon Termination and Adjustment

Adaptive, underactuated and compliant robot systems have received an increased interest over the last decade. Possible applications of these systems range from the development of adaptive robot hands to tendon-driven, soft exosuits. Despite the significant progress in the field, some basic design issues such as the tendon termination and adjustment have not yet been addressed properly. In this paper, we focus on tendon-driven, underactuated systems and we propose a compact ratchet clutch mechanism that facilitates a fine tendon termination and adjustment. The proposed mechanism is experimentally compared with six common tendon termination solutions, using two different tests: i) an accuracy test to verify how precisely each mechanism can adjust the tendon length and ii) a tensile test to derive the strength limit of each mechanism. The experiments validate that the ratchet clutch system is a precise and robust mechanism that outperforms all the solutions compared. A cable driven finger was designed and built to accommodate the proposed mechanism and test its efficiency and applicability to devices that require compactness (e.g., adaptive robot hands). The design of the mechanism is disseminated in an open-source manner