Reactive Gait Composition with Stability: Dynamic Walking amidst Static and Moving Obstacles

This paper presents a modular approach to motion planning with provable stability guarantees for robots that move through changing environments via periodic locomotion behaviors. We focus on dynamic walkers as a paradigm for such systems, although the tools developed in this paper can be used to support general compositional approaches to robot motion planning with Dynamic Movement Primitives (DMPs). Our approach ensures a priori that the suggested plan can be stably executed. This is achieved by formulating the planning process as a Switching System with Multiple Equilibria (SSME) and proving that the system's evolution remains within explicitly characterized trapping regions in the state space under suitable constraints on the frequency of switching among the DMPs. These conditions effectively encapsulate the low-level stability limitations in a form that can be easily communicated to the planner to guarantee that the suggested plan is compatible with the robot's dynamics. Furthermore, we show how the available primitives can be safely composed online in a receding horizon manner to enable the robot to react to moving obstacles. The proposed framework is applied on 3D bipedal walking models under common modeling assumptions, and offers a modular approach towards stably integrating readily available low-level locomotion control and high-level planning methods.Comment: 18 pages, 10 figure

Comparison of Different Clustering Algorithms for Diagnosing Memory-Related Performance Issues Using a Distributed Computing System

Αποτυχίες δημοφιλών συστημάτων μεγάλων τεχνολογικών κολοσσών καθιστούν την εφαρμογή των δοκιμών φορτίου (load tests) απαραίτητη για τον έλεγχο των συστημάτων λογισμικού. Παρόλα αυτά, η διάγνωση των προβλημάτων που σχετίζονται με την μνήμη αποτελεί μια σημαντική πρόκληση για τους προγραμματιστές. Για την αντιμετώπιση τους, εφαρμόζονται συχνά αυτοματοποιημένες τεχνικές ανάλυσης οι οποίες όμως απαιτούν σημαντική χειροκίνητη προσπάθεια και υψηλό βαθμό γνώσης του συστήματος. Μια λύση στο πρόβλημα αυτό, αποτελεί η χρήση τεχνικών της μηχανικής μάθησης (machine learning) με σκοπό την διάγνωση της υπάρχουσας ανώμαλης συμπεριφοράς του συστήματος. Οι Mark D. Syer et al. προτείνουν μια νέα αυτοματοποιημένη προσέγγιση συνδυάζοντας τους μετρητές απόδοσης (performance counters) και τα αρχεία εκτέλεσης (execution logs) εφαρμόζοντας την ιεραρχική ομαδοποίηση (hierarchical clustering) για την συσταδοποίηση των δεδομένων. Η ομαδοποίηση αυτή όμως, αποτυγχάνει σε περιπτώσεις μεγάλων δεδομένων (big data) καθώς παρουσιάζει μεγάλη πολυπλοκότητα. Εμείς, εφαρμόζουμε μια διαφορετική προσέγγιση του αλγορίθμου του Syer εκμεταλλευόμενοι το πλεονέκτημα του παραλληλισμού των ποικίλων διεργασιών που μας παρέχει το Spark framework. Βασιζόμενοι σε μια προηγούμενη εταιρική υλοποίηση του αλγόριθμου, στη φάση της συσταδοποίησης, εφαρμόζουμε τον k-means αλγόριθμο, αντί της ιεραρχικής, ώστε να αξιολογήσουμε τη συμπεριφορά των δυο αλγορίθμων για μεγάλα δεδομένα αλλά και αλγοριθμικά ως κομμάτι της προσέγγισης του Syer. Για την αξιολόγηση χρησιμοποιούμε συνθετικά δεδομένα από ένα πρόγραμμα υλοποίησης της Software Competitiveness International αλλά και πραγματικά δεδομένα από την εφαρμογή του Apache Tomcat έχοντας εισάγει ένα memory spike. Όσον αφορά τα αποτελέσματα, η προσέγγιση μας ανιχνεύει με ικανοποιητική ακρίβεια memory spikes ή συστάδες οι οποίες τα περιέχουν. Τέλος, σε περιπτώσεις μεγάλων σετ δεδομένων, τα αποτελέσματα που προκύπτουν, καθιστούν τον k-means αλγόριθμο καλύτερο ως προς τον χρόνο εκτέλεσης και την απόδοση σε σχέση με την ιεραρχική ομαδοποίηση.Failures in popular systems of technological giants illustrate load testing is a necessary procedure for the quality of software systems. However, the diagnosis of memory-related issues is a major challenge for developers. To address them, they often apply automated analysis techniques which require considerable manual effort and a high degree of system knowledge. One solution to this problem is the application of machine learning techniques to diagnose the existing abnormal system behavior. Mark D. Syer et al. propose a new automated approach combining performance counters and executing files by applying hierarchical clustering for clustering data. This grouping, however, fails in the case of large data sets as it generates greater complexity. We apply a different approach to the algorithm of Syer by using the Spark framework which offers parallelism of processes. Based on a previous corporate implementation of the algorithm, we apply the k-means algorithm in the clustering phase instead of the hierarchical clustering. This is done in order to evaluate the behavior of the two algorithms for large data sets and validate the k-means algorithm as part of the overall Syer approach. Our case studies use performance counters and execution logs from two systems. For the evaluation, we use synthetic data from one program created by Software Competitiveness International and actual data from the implementation of Apache Tomcat with an injection of a memory spike. Our approach identifies memory spikes corresponding to log lines with a high degree of precision. The approach detects a fairly accurate number of individual memory spikes or the clusters containing them. Finally, in the case of large data sets, the k-means algorithm performs better in terms of execution time and performance than hierarchical clustering

The Wall Lizards of the Balkan Peninsula: Tackling Questions at the Interphase of Phylogenomics and Population Genomics

[Abstract] Wall lizards of the genus Podarcis (Sauria, Lacertidae) are the predominant reptile group in southern Europe, including 24 recognized species. Mitochondrial DNA data have shown that, with the exception of P. muralis, the Podarcis species distributed in the Balkan peninsula form a species group that is further sub-divided into two subgroups: the one of “P. tauricus” consisting of P. tauricus, P. milensis, P. gaigeae, and P. melisellensis, and the other of “P. erhardii” comprising P. erhardii, P. levendis, P. cretensis, and P. peloponnesiacus. In an attempt to explore the Balkan Podarcis phylogenomic relationships, assess the levels of genetic structure and to re-evaluate the number of extant species, we employed phylogenomic and admixture approaches on ddRADseq (double digested Restriction site Associated DNA sequencing) genomic data. With this efficient Next Generation Sequencing approach, we were able to obtain a large number of genomic loci randomly distributed throughout the genome and use them to resolve the previously obscure phylogenetic relationships among the different Podarcis species distributed in the Balkans. The obtained phylogenomic relationships support the monophyly of both aforementioned subgroups and revealed several divergent lineages within each subgroup, stressing the need for taxonomic re-evaluation of Podarcis’ species in Balkans. The phylogenomic trees and the species delimitation analyses confirmed all recently recognized species (P. levendis, P. cretensis, and P. ionicus) and showed the presence of at least two more species, one in P. erhardii and the other in P. peloponnesiacus.This study was funded by NSFR 2007-2013 programme for development, European Social Fund, Operational Programme, Education and Lifelong Learning investing in knowledge society, Ministry of Education and Religious Affairs, Managing Authority, Co-financed by Greece and the European Union. Part of this work was funded by the Klaus Tschira Foundation, by the Ministry of Science and Innovation of Spain (PID2019-104184RB-I00 / AEI / 10.13039/501100011033), and by the Xunta de Galicia and FEDER funds of the EU under the Centro de Investigación de Galicia accreditation 2019-2022 (ED431G 2019/01)Xunta de Galicia; ED431G 2019/0