Automatic motion of manipulator using sampling based motion planning algorithms - application in service robotics

The thesis presents new approaches for autonomous motion execution of a robotic arm. The calculation of the motion is called motion planning and requires the computation of robot arm's path. The text covers the calculation of the path and several algorithms have been therefore implemented and tested in several real scenarios. The work focuses on sampling based planners, which means that the path is created by connecting explicitly random generated points in the free space. The algorithms can be divided into three categories: those that are working in configuration space(C-Space)(C- Space is the set of all possible joint angles of a robotic arm) , the mixed approaches using both Cartesian and C-Space and those that are using only the Cartesian space. Although Cartesian space seems more appropriate, due to dimensionality, this work illustrates that the C-Space planners can achieve comparable or better results. Initially an enhanced approach for efficient collision detection in C-Space, used by the planners, is presented. Afterwards the N dimensional cuboid region, notated as Rq, is defined. The Rq configures the C-Space so that the sampling is done close to a selected, called center, cell. The approach is enhanced by the decomposition of the Cartesian space into cells. A cell is selected appropriately if: (a) is closer to the target position and (b) lies inside the constraints. Inverse kinematics(IK) are applied to calculate a centre configuration used later by the Rq. The CellBiRRT is proposed and combines all the features. Continuously mixed approaches that do not require goal configuration or an analytic solution of IK are presented. Rq regions as well as Cells are also integrated in these approaches. A Cartesian sampling based planner using quaternions for linear interpolation is also proposed and tested. The common feature of the so far algorithms is the feasibility which is normally against the optimality. Therefore an additional part of this work deals with the optimality of the path. An enhanced approach of CellBiRRT, called CellBiRRT*, is developed and promises to compute shorter paths in a reasonable time. An on-line method using both CellBiRRT and CellBiRRT* is proposed where the path of the robot arm is improved and recalculated even if sudden changes in the environment are detected. Benchmarking with the state of the art algorithms show the good performance of the proposed approaches. The good performance makes the algorithms suitable for real time applications. In this work several applications are described: Manipulative skills, an approach for an semi-autonomous control of the robot arm and a motion planning library. The motion planning library provides the necessary interface for easy use and further development of the motion planning algorithms. It can be used as the part connecting the manipulative skill designing and the motion of a robotic arm

Automatische Bewegung eines Robotarmes mittels Probenahme basierten Bewegungsplannung Algorithmen - Anwendung im Service Robotics

The thesis presents new approaches for autonomous motion execution of a robotic arm. The calculation of the motion is called motion planning and requires the computation of robot arm's path. The text covers the calculation of the path and several algorithms have been therefore implemented and tested in several real scenarios. The work focuses on sampling based planners, which means that the path is created by connecting explicitly random generated points in the free space. The algorithms can be divided into three categories: those that are working in configuration space(C-Space)(C- Space is the set of all possible joint angles of a robotic arm) , the mixed approaches using both Cartesian and C-Space and those that are using only the Cartesian space. Although Cartesian space seems more appropriate, due to dimensionality, this work illustrates that the C-Space planners can achieve comparable or better results. Initially an enhanced approach for efficient collision detection in C-Space, used by the planners, is presented. Afterwards the N dimensional cuboid region, notated as Rq, is defined. The Rq configures the C-Space so that the sampling is done close to a selected, called center, cell. The approach is enhanced by the decomposition of the Cartesian space into cells. A cell is selected appropriately if: (a) is closer to the target position and (b) lies inside the constraints. Inverse kinematics(IK) are applied to calculate a centre configuration used later by the Rq. The CellBiRRT is proposed and combines all the features. Continuously mixed approaches that do not require goal configuration or an analytic solution of IK are presented. Rq regions as well as Cells are also integrated in these approaches. A Cartesian sampling based planner using quaternions for linear interpolation is also proposed and tested. The common feature of the so far algorithms is the feasibility which is normally against the optimality. Therefore an additional part of this work deals with the optimality of the path. An enhanced approach of CellBiRRT, called CellBiRRT*, is developed and promises to compute shorter paths in a reasonable time. An on-line method using both CellBiRRT and CellBiRRT* is proposed where the path of the robot arm is improved and recalculated even if sudden changes in the environment are detected. Benchmarking with the state of the art algorithms show the good performance of the proposed approaches. The good performance makes the algorithms suitable for real time applications. In this work several applications are described: Manipulative skills, an approach for an semi-autonomous control of the robot arm and a motion planning library. The motion planning library provides the necessary interface for easy use and further development of the motion planning algorithms. It can be used as the part connecting the manipulative skill designing and the motion of a robotic arm

Ο ρόλος διαδικτυακών έμπειρων συστημάτων στην υποβοήθηση της κυτταρολογικής διάγνωσης υλικού FNA ψυχρών όζων του θυρεοειδούς αδένα.

Εισαγωγή Σκοπός Αυτή η μελέτη διερευνά τις δυνατότητες μιας μεθοδολογίας τεχνητής νοημοσύνης (AI), της Ακτινικής Λειτουργίας Βάσης (RBF) Τεχνητό Νευρικό Δίκτυο (ANN) στην αξιολόγηση των αλλοιώσεων του θυρεοειδούς αδένα. Υλικά – Μέθοδοι Η μελέτη διεξήχθη σε 447 ασθενείς που είχαν σύμφωνη κυτταρολογική και ιστολογική διάγνωση . Τα κυτταρολογικά δείγματα μονιμοποιήθηκαν σε υγρή φάση. Κάθε δείγμα ψηφιοποιήθηκε σε εικόνες και τα χαρακτηριστικά πυρηνικής μορφολογίας μετρήθηκαν με τη χρήση ενός συστήματος ανάλυσης εικόνας. Τα αποτελέσματα των μετρήσεων (41.324 πυρήνες) χωρίστηκαν σε δύο ομάδες: την ομάδα εκπαίδευσης που χρησιμοποιήθηκε για τη δημιουργία του RBF ANN και την ομάδα δοκιμής που χρησιμοποιήθηκε για την αξιολόγηση της απόδοσης του RBF. Το σύστημα είχε ως στόχο να προβλέψει την ιστολογική διάγνωση ως καλοήθη ή κακοήθη. Αποτελέσματα: Το RBF ANN στην ομάδα εκπαίδευσής έδειξε : ευαισθησία 82,5%, ειδικότητα 94,6% και συνολική ακρίβεια 90,3%, ενώ στην ομάδα δοκιμής αυτοί οι δείκτες ήταν 81,4%, 90,0% και 86,9% αντίστοιχα. Ένας αλγόριθμος χρησιμοποιήθηκε για την ταξινόμηση των ασθενών με βάση το RBF ANN, η ολική ευαισθησία ήταν 95,0% και η ειδικότητα 95,5% όπου και δεν παρατηρήθηκε σημαντική διαφορά. Συμπέρασμα: Οι μέθοδοι τεχνητής νοημοσύνης και ειδικά το ANN, μόνο τα τελευταία χρόνια έχουν μελετηθεί εκτενώς. Η προτεινόμενη προσέγγιση είναι πολλά υποσχόμενη, για την αποφυγή εσφαλμένων διαγνώσεων και την υποστήριξη της καθημερινής πρακτικής του κυτταρολόγου . Το κύριο μειονέκτημα αυτής της προσέγγισης είναι η αυτοματοποίηση μιας διαδικασίας για τον ακριβή εντοπισμό και μέτρηση των πυρήνων των κυττάρων από τις ψηφιοποιημένες εικόνες.Objective: This study investigates the potential of an artificial intelligence (AI) methodology, the Radial Basis Function (RBF) Artificial Neural Network (ANN) in the evaluation of thyroid lesions. Study design: Was performed on 447 patients that had both cytological and histological evaluation in agreement. Cytological specimens were prepared using liquid based cytology and the histological result was based on subsequent surgical samples. Each specimen was digitized, on these images nuclear morphology features were measured by the use of an image analysis system. The extracted measurements (41,324 nuclei) were separated into two sets: the training set that was used to create the RBF ANN and the test set that was used to evaluate the RBF performance. The system aimed to predict the histological status as benign or malignant. Results: The RBF ANN obtained in the training set: sensitivity 82.5%, specificity 94.6% and overall accuracy 90.3%, while in the test set these indices were 81.4%, 90.0% and 86.9% respectively. Algorithm was used to classify patients on the basis of the RBF ANN, the overall sensitivity was 95.0% and the specificity 95.5% and no statistically significant difference was observed. Conclusion: AI techniques and especially ANNs, only the recent years have been studied extensively. The proposed approach is promising, to avoid misdiagnoses and assist, the everyday practice of the cytopathology. The major drawback in this approach is the automation of a procedure to accurately detect and measure cell nuclei from the digitized images