arXiv subjects

Christos G. Panayiotou

Publications and source records attributed to Christos G. Panayiotou.

At least 19 recordsLinked to original sources

arXiv2026

Mitigating Regional Traffic Congestion via School Start Time Scheduling: A Bilevel Alternating Optimization Approach

This paper addresses morning commute congestion caused by concentrated school-related trips in urban networks. We propose a bi-level optimization framework for regulating school start times in a multi-region urban network characterized by Macroscopic Fundamental Diagrams (MFDs), explicitly coupling system-level regulation with multi-class user-equilibrium-based departure-time choices. The Upper-Level problem jointly minimizes total time spent and deviations from current school schedules, while the Lower-Level problem models commuter behavior through a deterministic dynamic multi-class user equilibrium formulation incorporating alpha-beta-gamma preferences for travel time, earliness, and lateness costs. To address the computational challenges arising from the bilevel structure, non-convex traffic dynamics, and endogenous demand responses, an iterative algorithm alternating between the Upper- and Lower-Level problems is developed. The Upper-Level problem is approximated through a formulation solvable with standard mathematical programming solvers, while an iterative algorithm provides an approximate solution to the Lower-Level equilibrium problem. Numerical results demonstrate substantial congestion reductions and characterize the trade-off between school start-time flexibility and traffic efficiency. Sensitivity analyses further examine the effects of MFD uncertainty and scheduling preferences.

math.OC↗

arXiv2026

Adaptive Monitoring of Stochastic Fire Front Processes via Information-seeking Predictive Control

We consider the problem of adaptively monitoring a wildfire front using a mobile agent (e.g., a drone), whose trajectory determines where sensor data is collected and thus influences the accuracy of fire propagation estimation. This is a challenging problem, as the stochastic nature of wildfire evolution requires the seamless integration of sensing, estimation, and control, often treated separately in existing methods. State-of-the-art methods either impose linear-Gaussian assumptions to establish optimality or rely on approximations and heuristics, often without providing explicit performance guarantees. To address these limitations, we formulate the fire front monitoring task as a stochastic optimal control problem that integrates sensing, estimation, and control. We derive an optimal recursive Bayesian estimator for a class of stochastic nonlinear elliptical-growth fire front models. Subsequently, we transform the resulting nonlinear stochastic control problem into a finite-horizon Markov decision process and design an information-seeking predictive control law obtained via a lower confidence bound-based adaptive search algorithm with asymptotic convergence to the optimal policy.

cs.RO↗

arXiv2026

Synergising Human-like Responses and Machine Intelligence for Planning in Disaster Response

In the rapidly changing environments of disaster response, planning and decision-making for autonomous agents involve complex and interdependent choices. Although recent advancements have improved traditional artificial intelligence (AI) approaches, they often struggle in such settings, particularly when applied to agents operating outside their well-defined training parameters. To address these challenges, we propose an attention-based cognitive architecture inspired by Dual Process Theory (DPT). This framework integrates, in an online fashion, rapid yet heuristic (human-like) responses (System 1) with the slow but optimized planning capabilities of machine intelligence (System 2). We illustrate how a supervisory controller can dynamically determine in real-time the engagement of either system to optimize mission objectives by assessing their performance across a number of distinct attributes. Evaluated for trajectory planning in dynamic environments, our framework demonstrates that this synergistic integration effectively manages complex tasks by optimizing multiple mission objectives.

cs.AI↗

arXiv2026

A Cooperative Multi-Agent Probabilistic Framework for Search and Track Missions

In this work a robust and scalable cooperative multi-agent searching and tracking framework is proposed. Specifically, we study the problem of cooperative searching and tracking of multiple moving targets by a group of autonomous mobile agents with limited sensing capabilities. We assume that the actual number of targets present is not known a priori and that target births/deaths can occur anywhere inside the surveillance region thus efficient search strategies are required to detect and track as many targets as possible. To address the aforementioned challenges we recursively compute and propagate in time the searching-and-tracking (SAT) density. Using the SAT-density, we then develop decentralized cooperative look-ahead strategies for efficient searching and tracking of an unknown number of targets inside a bounded surveillance area.

eess.SY↗

arXiv2026

Drift-Aware Variational Autoencoder-based Anomaly Detection with Two-level Ensembling

In today's digital world, the generation of vast amounts of streaming data in various domains has become ubiquitous. However, many of these data are unlabeled, making it challenging to identify events, particularly anomalies. This task becomes even more formidable in nonstationary environments where model performance can deteriorate over time due to concept drift. To address these challenges, this paper presents a novel method, VAE++ESDD, which employs incremental learning and two-level ensembling: an ensemble of Variational AutoEncoder(VAEs) for anomaly prediction, along with an ensemble of concept drift detectors. Each drift detector utilizes a statistical-based concept drift mechanism. To evaluate the effectiveness of VAE++ESDD, we conduct a comprehensive experimental study using real-world and synthetic datasets characterized by severely or extremely low anomalous rates and various drift characteristics. Our study reveals that the proposed method significantly outperforms both strong baselines and state-of-the-art methods.

cs.LG↗

arXiv2025

Data-Driven Predictive Planning and Control for Aerial 3D Inspection with Back-face Elimination

Automated inspection with Unmanned Aerial Systems (UASs) is a transformative capability set to revolutionize various application domains. However, this task is inherently complex, as it demands the seamless integration of perception, planning, and control which existing approaches often treat separately. Moreover, it requires accurate long-horizon planning to predict action sequences, in contrast to many current techniques, which tend to be myopic. To overcome these limitations, we propose a 3D inspection approach that unifies perception, planning, and control within a single data-driven predictive control framework. Unlike traditional methods that rely on known UAS dynamic models, our approach requires only input-output data, making it easily applicable to off-the-shelf black-box UASs. Our method incorporates back-face elimination, a visibility determination technique from 3D computer graphics, directly into the control loop, thereby enabling the online generation of accurate, long-horizon 3D inspection trajectories.

cs.RO↗

arXiv2025

Multiple Target Tracking Using a UAV Swarm in Maritime Environments

Nowadays, unmanned aerial vehicles (UAVs) are increasingly utilized in search and rescue missions, a trend driven by technological advancements, including enhancements in automation, avionics, and the reduced cost of electronics. In this work, we introduce a collaborative model predictive control (MPC) framework aimed at addressing the joint problem of guidance and state estimation for tracking multiple castaway targets with a fleet of autonomous UAV agents. We assume that each UAV agent is equipped with a camera sensor, which has a limited sensing range and is utilized for receiving noisy observations from multiple moving castaways adrift in maritime conditions. We derive a nonlinear mixed integer programming (NMIP) -based controller that facilitates the guidance of the UAVs by generating non-myopic trajectories within a receding planning horizon. These trajectories are designed to minimize the tracking error across multiple targets by directing the UAV fleet to locations expected to yield targets measurements, thereby minimizing the uncertainty of the estimated target states. Extensive simulation experiments validate the effectiveness of our proposed method in tracking multiple castaways in maritime environments.

eess.SY↗

arXiv2025

Rolling Horizon Coverage Control with Collaborative Autonomous Agents

This work proposes a coverage controller that enables an aerial team of distributed autonomous agents to collaboratively generate non-myopic coverage plans over a rolling finite horizon, aiming to cover specific points on the surface area of a 3D object of interest. The collaborative coverage problem, formulated, as a distributed model predictive control problem, optimizes the agents' motion and camera control inputs, while considering inter-agent constraints aiming at reducing work redundancy. The proposed coverage controller integrates constraints based on light-path propagation techniques to predict the parts of the object's surface that are visible with regard to the agents' future anticipated states. This work also demonstrates how complex, non-linear visibility assessment constraints can be converted into logical expressions that are embedded as binary constraints into a mixed-integer optimization framework. The proposed approach has been demonstrated through simulations and practical applications for inspecting buildings with unmanned aerial vehicles (UAVs).

cs.RO↗

arXiv2025

Jointly-optimized Trajectory Generation and Camera Control for 3D Coverage Planning

This work proposes a jointly optimized trajectory generation and camera control approach, enabling an autonomous agent, such as an unmanned aerial vehicle (UAV) operating in 3D environments, to plan and execute coverage trajectories that maximally cover the surface area of a 3D object of interest. Specifically, the UAV's kinematic and camera control inputs are jointly optimized over a rolling planning horizon to achieve complete 3D coverage of the object. The proposed controller incorporates ray-tracing into the planning process to simulate the propagation of light rays, thereby determining the visible parts of the object through the UAV's camera. This integration enables the generation of precise look-ahead coverage trajectories. The coverage planning problem is formulated as a rolling finite-horizon optimal control problem and solved using mixed-integer programming techniques. Extensive real-world and synthetic experiments validate the performance of the proposed approach.

cs.RO↗

arXiv2025

SiameseDuo++: Active Learning from Data Streams with Dual Augmented Siamese Networks

Data stream mining, also known as stream learning, is a growing area which deals with learning from high-speed arriving data. Its relevance has surged recently due to its wide range of applicability, such as, critical infrastructure monitoring, social media analysis, and recommender systems. The design of stream learning methods faces significant research challenges; from the nonstationary nature of the data (referred to as concept drift) and the fact that data streams are typically not annotated with the ground truth, to the requirement that such methods should process large amounts of data in real-time with limited memory. This work proposes the SiameseDuo++ method, which uses active learning to automatically select instances for a human expert to label according to a budget. Specifically, it incrementally trains two siamese neural networks which operate in synergy, augmented by generated examples. Both the proposed active learning strategy and augmentation operate in the latent space. SiameseDuo++ addresses the aforementioned challenges by operating with limited memory and limited labelling budget. Simulation experiments show that the proposed method outperforms strong baselines and state-of-the-art methods in terms of learning speed and/or performance. To promote open science we publicly release our code and datasets.

cs.LG↗

arXiv2025

iCPS-DL: A Description Language for Autonomic Industrial Cyber-Physical Systems

Modern industrial systems require frequent updates to their cyber and physical infrastructures, often demanding considerable reconfiguration effort. This paper introduces the industrial Cyber-Physical Systems Description Language, iCPS-DL, which enables autonomic reconfigurations for industrial Cyber-Physical Systems. The iCPS-DL maps an industrial process using semantics for physical and cyber-physical components, a state estimation model, and agent interactions. A novel aspect is using communication semantics to ensure live interaction among distributed agents. Reasoning on the semantic description facilitates the configuration of the industrial process control loop. A Water Distribution Networks domain case study demonstrates iCPS-DL's application.

eess.SY↗

arXiv2025

Cooperative Aerial Robot Inspection Challenge: A Benchmark for Heterogeneous Multi-UAV Planning and Lessons Learned

We propose the Cooperative Aerial Robot Inspection Challenge (CARIC), a simulation-based benchmark for motion planning algorithms in heterogeneous multi-UAV systems. CARIC features UAV teams with complementary sensors, realistic constraints, and evaluation metrics prioritizing inspection quality and efficiency. It offers a ready-to-use perception-control software stack and diverse scenarios to support the development and evaluation of task allocation and motion planning algorithms. Competitions using CARIC were held at IEEE CDC 2023 and the IROS 2024 Workshop on Multi-Robot Perception and Navigation, attracting innovative solutions from research teams worldwide. This paper examines the top three teams from CDC 2023, analyzing their exploration, inspection, and task allocation strategies while drawing insights into their performance across scenarios. The results highlight the task's complexity and suggest promising directions for future research in cooperative multi-UAV systems.

cs.RO↗

arXiv2024

Model Predictive Control For Multiple Castaway Tracking with an Autonomous Aerial Agent

Over the past few years, a plethora of advancements in Unmanned Areal Vehicle (UAV) technology has paved the way for UAV-based search and rescue operations with transformative impact to the outcome of critical life-saving missions. This paper dives into the challenging task of multiple castaway tracking using an autonomous UAV agent. Leveraging on the computing power of the modern embedded devices, we propose a Model Predictive Control (MPC) framework for tracking multiple castaways assumed to drift afloat in the aftermath of a maritime accident. We consider a stationary radar sensor that is responsible for signaling the search mission by providing noisy measurements of each castaway's initial state. The UAV agent aims at detecting and tracking the moving targets with its equipped onboard camera sensor that has limited sensing range. In this work, we also experimentally determine the probability of target detection from real-world data by training and evaluating various Convolutional Neural Networks (CNNs). Extensive qualitative and quantitative evaluations demonstrate the performance of the proposed approach.

cs.RO↗

arXiv2024

Automated Real-Time Inspection in Indoor and Outdoor 3D Environments with Cooperative Aerial Robots

This work introduces a cooperative inspection system designed to efficiently control and coordinate a team of distributed heterogeneous UAV agents for the inspection of 3D structures in cluttered, unknown spaces. Our proposed approach employs a two-stage innovative methodology. Initially, it leverages the complementary sensing capabilities of the robots to cooperatively map the unknown environment. It then generates optimized, collision-free inspection paths, thereby ensuring comprehensive coverage of the structure's surface area. The effectiveness of our system is demonstrated through qualitative and quantitative results from extensive Gazebo-based simulations that closely replicate real-world inspection scenarios, highlighting its ability to thoroughly inspect real-world-like 3D structures.

cs.RO↗

arXiv2024

Hierarchical Fault-Tolerant Coverage Control for an Autonomous Aerial Agent

Fault-tolerant coverage control involves determining a trajectory that enables an autonomous agent to cover specific points of interest, even in the presence of actuation and/or sensing faults. In this work, the agent encounters control inputs that are erroneous; specifically, its nominal controls inputs are perturbed by stochastic disturbances, potentially disrupting its intended operation. Existing techniques have focused on deterministically bounded disturbances or relied on the assumption of Gaussian disturbances, whereas non-Gaussian disturbances have been primarily been tackled via scenario-based stochastic control methods. However, the assumption of Gaussian disturbances is generally limited to linear systems, and scenario-based methods can become computationally prohibitive. To address these limitations, we propose a hierarchical coverage controller that integrates mixed-trigonometric-polynomial moment propagation to propagate non-Gaussian disturbances through the agent's nonlinear dynamics. Specifically, the first stage generates an ideal reference plan by optimising the agent's mobility and camera control inputs. 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. This is achieved by imposing a set of deterministic constraints on the moments of the system's uncertain states.

eess.SY↗

arXiv2024

Incremental Learning with Concept Drift Detection and Prototype-based Embeddings for Graph Stream Classification

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. Graph structures offer a powerful modelling tool to represent complex systems, such as, critical infrastructure systems and social networks. Learning from graph streams becomes a necessity to understand the dynamics of graph structures and to facilitate informed decision-making. This work introduces a novel method for graph stream classification which operates under the general setting where a data generating process produces graphs with varying nodes and edges over time. The method uses incremental learning for continual model adaptation, selecting representative graphs (prototypes) for each class, and creating graph embeddings. Additionally, it incorporates a loss-based concept drift detection mechanism to recalculate graph prototypes when drift is detected.

cs.LG↗

arXiv2024

Unsupervised Incremental Learning with Dual Concept Drift Detection for Identifying Anomalous Sequences

In the contemporary digital landscape, the continuous generation of extensive streaming data across diverse domains has become pervasive. Yet, a significant portion of this data remains unlabeled, posing a challenge in identifying infrequent events such as anomalies. This challenge is further amplified in non-stationary environments, where the performance of models can degrade over time due to concept drift. To address these challenges, this paper introduces a new method referred to as VAE4AS (Variational Autoencoder for Anomalous Sequences). VAE4AS integrates incremental learning with dual drift detection mechanisms, employing both a statistical test and a distance-based test. The anomaly detection is facilitated by a Variational Autoencoder. To gauge the effectiveness of VAE4AS, a comprehensive experimental study is conducted using real-world and synthetic datasets characterized by anomalous rates below 10\% and recurrent drift. The results show that the proposed method surpasses both robust baselines and state-of-the-art techniques, providing compelling evidence for their efficacy in effectively addressing some of the challenges associated with anomalous sequence detection in non-stationary streaming data.

cs.CE↗

arXiv2024

Cooperative Receding Horizon 3D Coverage Control with a Team of Networked Aerial Agents

This work proposes a receding horizon coverage control approach which allows multiple autonomous aerial agents to work cooperatively in order cover the total surface area of a 3D object of interest. The cooperative coverage problem which is posed in this work as an optimal control problem, jointly optimizes the agents' kinematic and camera control inputs, while considering coupling constraints amongst the team of agents which aim at minimizing the duplication of work. To generate look-ahead coverage trajectories over a finite planning horizon, the proposed approach integrates visibility constraints into the proposed coverage controller in order to determine the visible part of the object with respect to the agents' future states. In particular, we show how non-linear and non-convex visibility determination constraints can be transformed into logical constraints which can easily be embedded into a mixed integer optimization program.

cs.RO↗