321 papers
This paper presents the first direct comparison between gate-based quantum computing (using QAOA) and adiabatic quantum computing (via Ising models) for solving AC power flow problems, demonstrating through numerical experiments on a 4-bus system how these paradigms and quantum-inspired solvers trade off accuracy, scalability, and practical viability for future electricity grid optimization.
This paper establishes conservative probabilistic bounds for the spectrum of admittance matrices and linear power flow models under uncertain network parameters by leveraging random matrix concentration inequalities, thereby providing a theoretical framework to quantify approximation errors and analyze how uncertainty concentrates at critical nodes.
This paper proposes a novel, computationally efficient interval-based reachability analysis method for Neural ODEs that leverages continuous-time mixed monotonicity to trade off tightness for scalability, making it particularly suitable for high-dimensional and safety-critical real-time applications.
This paper proposes a multi-UAV flood monitoring strategy using Centroidal Voronoi Tessellation with a Gaussian Mixture of Density Functions to model inundation areas, demonstrating through ROS/Gazebo simulations that this approach achieves superior coverage rates and spatial distribution compared to conventional axis-aligned Gaussian models across various fleet sizes.
This paper presents a real-time online framework that utilizes modified sliding-window Hankel Dynamic Mode Decomposition with singular-value hard thresholding and Cadzow projection to denoise partial measurements and construct predictive models for dynamic obstacle motion, enabling stable, variance-aware forecasting suitable for robotic motion planning.
This paper introduces the concept of dependent reachable sets for two-agent pursuit scenarios, characterizing their geometric bounds and shape through theoretical analysis and simulations of the constant bearing pursuit strategy.
This paper introduces XR-DT, an Extended Reality-enhanced Digital Twin framework that integrates a novel Human-Aware Model Predictive Path Integral (HA-MPPI) controller with an attention-based trajectory prediction model to enable safe, efficient, and interpretable motion planning for mobile robots operating alongside humans.
This paper introduces SORS, a modular, high-fidelity simulator based on the finite element method and constrained nonlinear optimization that accurately models complex soft robot dynamics and contact interactions, effectively bridging the sim-to-real gap for prototyping and control optimization.
StochasticBarrier.jl is an open-source Julia toolbox that efficiently synthesizes Stochastic Barrier Functions for verifying the safety of discrete-time stochastic systems using Sum-of-Squares and piecewise constant optimization methods, demonstrating superior speed, scalability, and safety bounds compared to state-of-the-art tools across over 30 case studies.
This paper proposes an online least-squares algorithm for model-free cooperative filtering under asynchronous observations, establishing an O(log³ N) regret bound and proving conditions under which the method outperforms optimal model-based predictors relying solely on local data.
This paper introduces the NC-tALC dataset, a high-fidelity trajectory collection of 152 controlled experiments designed to characterize the interaction dynamics and decision-making of transitional autonomous vehicles during mandatory lane-changing maneuvers.
This paper proposes a comprehensive modeling framework to address the limitations of existing test cases by integrating realistic wildfire propagation, infrastructure-hazard interactions, and community impacts to better analyze and enhance the resilience of electrical power networks against wildfire risks.
This paper introduces a distribution-agnostic state space framework based on differential algebraic equations to quantify and propagate uncertainties in coupled hydrologic and hydrodynamic systems, enabling real-time probabilistic flood forecasting under partial measurements without requiring specific input distribution assumptions.
This paper proposes a hybrid safety-critical coordination architecture for multi-agent systems that utilizes a mixed-integer linear program to assign collision-avoidance responsibilities among agents, thereby eliminating redundant constraint enforcement and reducing computational complexity while maintaining formal safety guarantees via control barrier functions.
This paper introduces a novel frequency response formulation for nonlinear systems within the Koopman operator framework, deriving a generalized complex-valued function via Laplace transforms and resolvent theory to enable Bode plot analysis under specific existence conditions.
This paper proposes a simple, low-cost control strategy for small hopping robots that combines slope-adaptive touchdown angles and pre-takeoff corrective torque to effectively cancel destabilizing rotations and maintain stable hopping on inclined terrain.
This paper proposes a dual-AoI-based Markov decision process framework to optimize transmission scheduling in wireless monitoring systems with random data arrivals and unreliable channels, deriving a low-complexity threshold policy that outperforms existing approaches by addressing the inefficiency of conventional methods that ignore asynchronous AoI evolution.
This paper introduces Conversational Demand Response (CDR), a bidirectional coordination framework leveraging agentic AI to enable natural language interactions between aggregators and prosumers, thereby combining automated scalability with enhanced user transparency and agency to sustain residential demand response participation.
This paper proposes and validates an autonomous star-based navigation method for deep-space missions up to 250 AU that utilizes stellar parallactic shifts to achieve sub-AU position accuracy, thereby reducing reliance on Earth-based radiometric tracking.
The paper introduces AIRT, an end-to-end deep-learning framework that generates high-quality, deliverable single-arc VMAT prostate treatment plans in under one second directly from CT images and contours, demonstrating non-inferiority to standard clinical planning systems while significantly accelerating workflow efficiency.