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arxiveess.SY2026-07-02

Docking of Autonomous Vehicles with a Stationary Docking Station in 3D Space

Ram Milan Kumar Verma, Shashi Ranjan Kumar, Hemendra Arya

In this letter, we present a strategy for autonomous docking of autonomous vehicles in three-dimensional space. Docking is a safety-critical task and requires expert piloting skills. Vehicles with autonomous docking capabilities are highly desirable in various applications, such as marine vehicle docking, aerial vehicle docking, spacecraft docking, and landing. To dock autonomously with the docking station, the vehicle must align itself to a specific desired orientation relative to the docking station and also reduce speed as it approaches. The vehicle achieves near-zero speed to dock successfully and safely without colliding with the docking station. Inspired by the philosophies from the guidance literature, we present a finite-time sliding mode-based strategy to achieve the same. The range and line-of-sight kinematics relations describing the motion of the vehicle with respect to the stationary docking station are used to steer the vehicle to achieve the desired orientation for docking. This docking strategy is validated in MATLAB\textsuperscript{\textregistered} simulations for various initial locations and orientations of both the vehicle and the docking station.

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arxiveess.SY2026-07-10

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arxivcs.ROeess.SY2026-07-20

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In autonomous driving development, a perception dataset is crucial, as it provides fundamental data for training, testing, and validating algorithms for an autonomous vehicle's multimodal perception systems. So far, most research has concentrated on providing datasets for well-st…

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arxiveess.SY2026-07-16

A Model Predictive Control Framework for Assisted Vehicle Drifting

Marco Cortese, Antonio Gallina, Matteo Grandin, Giovanni Righetti, Mattia Bruschetta, Basilio Lenzo

Model Predictive Control (MPC) has been widely applied to autonomous vehicle drifting. Assisted drifting, that is where the driver remains in the loop, is still comparatively underexplored. Existing approaches often rely on restrictive assumptions, such as precomputed drift equil…

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arxiveess.SPeess.SY2026-07-12

Fuse-then-Detect for Passive UAV Localization Using Multi-UE 5G Uplink Signals

Wenyu Huang, Nuria González-Prelcic, Vishnu Ratnam, Murat Bayraktar, Charlie Jianzhong Zhang

Low-altitude uncrewed aerial vehicles (UAVs) can pose growing risks to airspace safety, security, and privacy. Cellular infrastructure can passively sense them without dedicated radar hardware by exploiting integrated sensing and communication (ISAC) technology. Most prior work e…

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arxiveess.SY2026-07-16

Integrated Discovery and State-Aware Servicing for Mobile AUVs With UOWC: Modeling and Performance Analysis

Qiyu Ma, Jiajie Xu, Mohamed-Slim Alouini

Underwater wireless optical communication (UWOC) is an enabling technology for high-throughput subsea networks, yet its long-term deployment is constrained by the finite energy budget of underwater nodes. To address this challenge, we investigate a mobile system wherein an autono…

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arxivcs.ROcs.AIcs.LGcs.NIeess.SY2026-07-21

Intelligent Multi-UAV Navigation in ITNTNs: A Hierarchical LLM Approach

Zijiang Yan, Hao Zhou, Wael Jaafar, Jianhua Pei, Ping Wang, Halim Yanikomeroglu, et al.

The deployment of high-speed Uncrewed Aerial Vehicles (UAVs) in 3D aerial highways necessitates robust coordination of physical flight kinematics and multi-tier network handovers. While Deep Reinforcement Learning (DRL) offers rapid tactical control, it lacks the zero-shot strate…

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