282 papers
This tutorial survey synthesizes machine learning methodologies across all network layers to address the unique challenges of the Internet of Underwater Things, demonstrating significant performance gains in localization, routing, and data processing while outlining implementation barriers and future research directions based on a review of 300 studies.
This review paper proposes a constraint-coupled perspective on underwater embodied intelligence, arguing that reliable autonomy requires integrating sensing, communication, and resource limitations into planning and control to address the interdependent challenges of real ocean environments.
This paper proposes a Multi-Agentic AI framework utilizing specialized LLM agents and retrieval-augmented generation to automate conflict-aware rApp policy orchestration in Open RAN, achieving significant improvements in deployment accuracy and cost reduction while enabling zero-shot generalization.
This paper provides a comprehensive review of network-aware modeling, optimization, and control methods for Integrated Energy Systems, highlighting the critical role of network constraints in addressing operational challenges and outlining future research directions to achieve scalable, efficient, and low-carbon energy operations.
This paper proposes a soft actor-critic-based reinforcement learning framework for vehicle-to-grid voltage regulation that effectively coordinates single and multi-hub charging systems while prioritizing battery health and fleet availability, demonstrating robust performance comparable to standard droop controllers under both nominal and aggressive overloading conditions.
This tutorial provides a control-oriented introduction to aided inertial navigation systems by utilizing a Lie-group formulation based on the extended Special Euclidean group SE(2,3) to establish a clear, geometric framework for fusing inertial and aiding measurements while explicitly leveraging invariance and symmetry principles.
This paper proposes ACLM, an ADMM-based distributed model predictive control framework that enables scalable, real-time collaborative loco-manipulation of heavy payloads by decomposing the global optimization problem into parallel robot-level subproblems while preserving dynamic coupling through consensus constraints.
This paper introduces GuideTWSI, a diverse dataset combining synthetic and real-world images to address the scarcity of Tactile Walking Surface Indicator (TWSI) data, specifically bridging the gap between East Asian directional bars and North American/European truncated domes to improve navigation safety for blind and low-vision individuals.
This paper presents a user-friendly framework that enables domain scientists to generate 3D animations of petascale, time-varying climate data on commodity hardware using an LLM-assisted conversational interface, thereby eliminating the need for specialized visualization expertise and high-performance computing resources.
This paper proposes a communication network-aware framework that integrates routing constraints with low-rank matrix completion to mitigate spatially correlated data losses and significantly improve missing data recovery accuracy in power distribution grids compared to traditional measurement-only approaches.
This paper proposes a rotating equilibrium strategy for collaborative tethered aerial transport, where steady circular motion generates centrifugal forces to maintain tether tension, thereby enabling quadrotors to produce purely vertical thrust and reducing total power consumption by up to 20% compared to conventional static lifting methods.
This tutorial critically examines the gap between theoretical Control Barrier Function (CBF) guarantees and practical implementation in robotics, revealing how common misuses and hidden assumptions often lead to tautological safety claims in passively safe systems while offering guidelines and interactive tools to construct valid safety arguments for systems with input constraints.
Space-Control is a hardware-software co-design that addresses the critical security gap of missing process-level isolation in CXL-based disaggregated memory by authenticating execution contexts and enforcing fine-grained access control with minimal performance overhead.
This paper introduces work schedule-aware robust and chance-constrained optimization formulations to demonstrate that intelligently coordinating EV charging with hybrid work schedules and rooftop PV generation can effectively increase EV hosting capacity and manage grid demands on residential distribution transformers.
This paper proposes an Adaptive Gain Nonlinear Observer (AGNO) that leverages a full nonlinear dynamic model to accurately and robustly estimate external interaction wrenches in human-UAV physical payload transportation without dedicated force-torque sensors, demonstrating superior performance over Extended Kalman Filters through rigorous stability analysis and simulation.
This paper introduces the BOUNDS method and the Augmented Information Kalman Filter (AI-KF) to systematically discover active sensing motifs and dynamically fuse neural network with model-based estimation, thereby improving state estimation for nonlinear autonomous systems in GPS-denied environments.
This paper introduces a unified Davis-Wielandt shell framework for the graphical stability analysis of MIMO LTI systems, proposing a novel rotated scaled relative graph (-SRG) concept that yields the least conservative closed-loop stability criterion among existing two-dimensional graphical conditions.
This paper establishes robustness bounds for discrete-time stochastic optimal control under Wasserstein model approximation, demonstrating that the performance loss of policies derived from approximate models is controlled by the Wasserstein-1 distance between transition kernels, thereby enabling rigorous sample complexity analysis for empirical model and noise distribution learning where stronger convergence criteria may fail.
This paper derives explicit solutions for a multi-disturbance minimax Linear Regulator problem applied to positive linear time-invariant systems in continuous time, utilizing dynamic programming and a fixed-point method to address both finite and infinite horizons while demonstrating scalability through a large-scale water management network application.
This paper proposes a model predictive control policy for home battery management under tiered peak power charges that, using simple forecasts, achieves electricity costs within 1.7% of the theoretical minimum derived from perfect foresight.