289 papers
This paper demonstrates that applying dimensional analysis and data scaling to large-scale agent-based simulations of autonomous drone swarms reveals predictable, counterintuitive scaling laws and sharp success-failure boundaries, enabling rapid, budget-aware optimization of agent counts, platform parameters, and path planning strategies across diverse mission scenarios.
This paper establishes a control-theoretic framework that formalizes agentic systems as hierarchical decision authorities within feedback loops, providing a unified dynamical representation to analyze the stability, safety, and performance of AI agents that adapt controllers, strategies, and objectives in real-time.
This paper proposes an AI-driven framework that improves 5G/6G traffic demand prediction accuracy and spatial generalization by employing a context-aware two-stage splitting strategy and residual error correction to mitigate neighborhood leakage, as validated by experiments across five Canadian cities.
This paper proposes a hierarchical Graph Attention Network (HR-GAT) model that leverages public deployment records to accurately estimate fine-grained spectrum demand across multiple cities, significantly outperforming existing baselines and providing regulators with actionable insights for efficient spectrum sharing and allocation.
This paper proposes distributed dynamical algorithms that enable agents with only local data and no raw data sharing to collectively compute global system certificates, specifically solving the Lyapunov equation for stability verification and the algebraic Riccati equation for optimal LQR control, with guarantees of exact convergence and robustness.
This paper proposes a distributed safety-critical control framework for multi-agent systems with uncontrollable agents, utilizing a novel reconstructed Control Barrier Function based on distributed adaptive state estimates and prescribed performance parameters to decouple safety constraints and rigorously guarantee system safety.
By analyzing real-world Californian BEV usage data to identify distinct driver profiles, this study demonstrates that Vehicle-to-Grid (V2G) adoption is most viable for "Daily Chargers" and that its impact on battery lifetime varies significantly based on calendar aging sensitivity and charging habits, ranging from increased capacity loss to potential retention improvements.
This paper presents a novel robotic gripper featuring an active dented-roller fingertip and compliant fingers that reliably separates and opens sterile medical pouch flaps, offering a solution to automate a physically demanding and injury-prone manual task performed by hospital staff.
This paper proposes a method to immerse non-polynomial Port-Hamiltonian systems into higher-dimensional polynomial representations that preserve key structural and energy properties, thereby enabling the design of stabilizing feedback laws through sum-of-squares optimization and passivity-based control.
This paper presents a reference architecture and roadmap for Quantum-Centric Supercomputing (QCSC) systems that integrate quantum, GPU, and CPU resources to overcome current isolation challenges and enable seamless, high-performance hybrid workflows across three evolutionary phases.
This paper proposes and analyzes two adaptive algorithms for maximizing time-average utility in multi-channel multiple access systems with unknown link failure probabilities, offering a trade-off between fast convergence with high computational cost and slower convergence with efficient implementation, while also providing specialized solutions for single-channel settings and non-adaptive approaches.
This paper introduces Generative Predictive Control, a supervised learning framework that leverages flow matching and sampling-based predictive control to enable high-frequency, dynamic robotic tasks by eliminating the need for difficult-to-obtain expert demonstrations.
This paper demonstrates that a simple iterative LQR algorithm using MuJoCo dynamics and finite-difference derivatives can achieve effective, real-time whole-body model-predictive control for quadruped and humanoid robots in the real world with minimal sim-to-real tuning, thereby lowering the barrier for future research.
This paper introduces VISKY, a computationally efficient semi-active control strategy for magnetorheological suspensions that enhances ride comfort and stability by implementing a virtual inertia matrix through acceleration feedback, effectively outperforming conventional Skygroundhook methods across various road conditions.
This paper introduces a novel SCC-constrained bilevel model to demonstrate that synchronous generators at advantageous grid locations can exploit the localized nature of short-circuit current services to exercise market power, significantly inflating revenues and consumer costs, thereby highlighting the urgent need for specific market design mitigations.
The paper introduces MARLIN, a decentralized reservoir management framework that combines multi-agent reinforcement learning inspired by starling murmurations with LLM-guided reward shaping to effectively handle environmental uncertainties, significantly improving flood response times and computational efficiency compared to traditional methods.
This paper introduces ROSflight 2.0, a modular, open-source ROS 2-based autopilot ecosystem designed to lower barriers for UAV research and accelerate the transition from simulation to hardware, featuring a lean architecture that successfully controls multirotors at 400 Hz with all loops running on a companion computer.
ROSplane 2.0 is an open-source, ROS 2-based fixed-wing autopilot framework designed by researchers to accelerate UAV experimentation through a lean, modular architecture, enhanced control algorithms, and a streamlined aerodynamic modeling pipeline that simplifies the transition from simulation to real-world testing.
This paper proposes a bio-inspired, digital pheromone-based framework that mimics ant colony behavior to dynamically classify industrial production lines as In-Control or Out-of-Control and forecast maintenance needs by aggregating real-time temperature data, threat signals, and decaying environmental scores into a single adaptive indicator.
This paper proposes a robust, data-driven framework that utilizes constrained nonlinear optimization with iteratively reweighted least squares and sliding-window strategies to enable accurate online estimation of quadrotor motor efficiency for applications in fault detection and predictive maintenance.