no code implementations • 15 Apr 2024 • Savvas Papaioannou, Christian Vitale, Panayiotis Kolios, Christos G. Panayiotou, Marios M. Polycarpou
The second-stage fault-tolerant controller then aims to follow this reference plan, even in the presence of erroneous control inputs caused by non-Gaussian disturbances.
no code implementations • 15 Apr 2024 • Savvas Papaioannou, Panayiotis Kolios, Christos G. Panayiotou, Marios M. Polycarpou
In the rapidly changing environments of disaster response, planning and decision-making for autonomous agents involve complex and interdependent choices.
no code implementations • 3 Apr 2024 • Kleanthis Malialis, Jin Li, Christos G. Panayiotou, Marios M. Polycarpou
Data stream mining aims at extracting meaningful knowledge from continually evolving data streams, addressing the challenges posed by nonstationary environments, particularly, concept drift which refers to a change in the underlying data distribution over time.
no code implementations • 22 May 2023 • Savvas Papaioannou, Panayiotis Kolios, Theocharis Theocharides, Christos G. Panayiotou, Marios M. Polycarpou
This work proposes an integrated guidance and gimbal control coverage path planning (CPP) approach, in which the mobility and gimbal inputs of an autonomous UAV agent are jointly controlled and optimized to achieve full coverage of a given object of interest, according to a specified set of optimality criteria.
no code implementations • 15 May 2023 • Jin Li, Kleanthis Malialis, Marios M. Polycarpou
In our digital universe nowadays, enormous amount of data are produced in a streaming manner in a variety of application areas.
no code implementations • 18 Apr 2023 • Savvas Papaioannou, Panayiotis Kolios, Theocharis Theocharides, Christos G. Panayiotou, Marios M. Polycarpou
In this work, a novel distributed search-planning framework is proposed, where a dynamically varying team of autonomous agents cooperate in order to search multiple objects of interest in three-dimension (3-D).
no code implementations • 21 Feb 2023 • Savvas Papaioannou, Panayiotis Kolios, Theocharis Theocharides, Christos G. Panayiotou, Marios M. Polycarpou
In this work a robust and scalable cooperative multi-agent searching and tracking framework is proposed.
no code implementations • 1 Feb 2023 • Savvas Papaioannou, Panayiotis Kolios, Theocharis Theocharides, Christos G. Panayiotou, Marios M. Polycarpou
In this paper we are interested in the task of searching and tracking multiple moving targets in a bounded surveillance area with a group of autonomous mobile agents.
no code implementations • 1 Feb 2023 • Savvas Papaioannou, Panayiotis Kolios, Theocharis Theocharides, Christos G. Panayiotou, Marios M. Polycarpou
Based on this, we develop decentralized cooperative look-ahead strategies for efficient searching and tracking of an unknown number of targets inside a bounded surveillance area.
no code implementations • 1 Feb 2023 • Savvas Papaioannou, Panayiotis Kolios, Theocharis Theocharides, Christos G. Panayiotou, Marios M. Polycarpou
In this paper, we investigate the problem of joint searching and tracking of multiple mobile targets by a group of mobile agents.
no code implementations • 2 Jan 2023 • Pavlos Pavlou, Stelios Vrachimis, Demetrios G. Eliades, Marios M. Polycarpou
Moreover, ML approaches require large amounts of labeled input data to train their models, which are typically not available for a single household, while usage characteristics may vary in different regions.
1 code implementation • 13 Oct 2022 • Kleanthis Malialis, Dimitris Papatheodoulou, Stylianos Filippou, Christos G. Panayiotou, Marios M. Polycarpou
Second, learning models have access to more labelled data without the need to increase the active learning budget and / or the original memory size.
no code implementations • 10 Oct 2022 • Kleanthis Malialis, Manuel Roveri, Cesare Alippi, Christos G. Panayiotou, Marios M. Polycarpou
In real-world applications, the process generating the data might suffer from nonstationary effects (e. g., due to seasonality, faults affecting sensors or actuators, and changes in the users' behaviour).
1 code implementation • 3 Oct 2022 • Kleanthis Malialis, Christos G. Panayiotou, Marios M. Polycarpou
We conduct an extensive study that compares the role of different active learning budgets and strategies, the performance with/without memory, the performance with/without ensembling, in both synthetic and real-world datasets, under different data nonstationarity characteristics and class imbalance levels.
no code implementations • 9 Jan 2022 • Farzaneh Tatari, Aquib Mustafa, Majid Mazouchi, Hamidreza Modares, Christos G. Panayiotou, Marios M. Polycarpou
This work presents a rigorous analysis of the adverse effects of cyber-physical attacks on the performance of multi-agent consensus with event-triggered control protocols.
no code implementations • 4 Oct 2020 • Kleanthis Malialis, Christos G. Panayiotou, Marios M. Polycarpou
In this paper we investigate learning from limited labelled, nonstationary and imbalanced data in online classification.
1 code implementation • 24 Sep 2020 • Kleanthis Malialis, Christos G. Panayiotou, Marios M. Polycarpou
An enormous and ever-growing volume of data is nowadays becoming available in a sequential fashion in various real-world applications.
1 code implementation • 27 Sep 2018 • Kleanthis Malialis, Christos G. Panayiotou, Marios M. Polycarpou
Online class imbalance learning constitutes a new problem and an emerging research topic that focusses on the challenges of online learning under class imbalance and concept drift.