arXiv subjects

Yihao Dong

Publications and source records attributed to Yihao Dong.

9 recordsLinked to original sources

arXiv2026

AUSLUN: A Fixed-Hover UAV--USV System for GNSS-Denied Maritime Search and Navigation

Global navigation satellite system (GNSS) denial can prevent an unmanned surface vehicle (USV) from both finding a distant vessel and maintaining a globally referenced approach. This paper presents AUSLUN (Automatic UAV Search, Localization, and USV Navigation), a fixed-hover aerial-surface system that uses a coastal unmanned aerial vehicle (UAV), which estimates its own pose through visual-inertial odometry (VIO), as a long-range sensing and navigation anchor. The central design shifts sensing motion from UAV translation to a zoom pod and closes the loop through three coupled elements: polygon-aware annular pod scanning, modality-aware bearing-range localization, and target-relative USV guidance with visual-loss recovery. The same gated recursive estimator uses laser range for the non-cooperative target and datalink range for the cooperative USV. Search-planning simulations show that the adaptive yaw bounds reduce scan time and redundant coverage relative to a matched fixed-sector scan, and GPS-referenced field data show that the gated recursive estimator outperforms non-recursive baselines in localization accuracy. An integrated maritime mission further demonstrates the complete search-to-navigation sequence, including a deliberately triggered visual-loss recovery. These results establish the feasibility and operating boundary of fixed-hover UAV assistance for stationary-target approach in coastal GNSS-denied environments. The source code and a video demonstration are publicly available at https://github.com/xirhxq/pod_search and https://youtu.be/S-5RkJs35JI.

cs.RO↗

arXiv2026

TactDeform: Finger Pad Deformation Inspired Spatial Tactile Feedback for Virtual Geometry Exploration

Spatial tactile feedback can enhance the realism of geometry exploration in virtual reality applications. Current vibrotactile approaches often face challenges with the spatial and temporal resolution needed to render different 3D geometries. Inspired by the natural deformation of finger pads when exploring 3D objects and surfaces, we propose TactDeform, a parametric approach to render spatio-temporal tactile patterns using a finger-worn electro-tactile interface. The system dynamically renders electro-tactile patterns based on both interaction contexts (approaching, contact, and sliding) and geometric contexts (geometric features and textures), emulating deformations that occur during real-world touch exploration. Results from a user study \rr{(N=24)} show that the proposed approach enabled high texture discrimination and geometric feature identification compared to a baseline. Informed by results from a free 3D-geometry exploration phase, we provide insights that can inform future tactile interface designs.

cs.HC↗

arXiv2026

SRL Proxemics: Spatial Guidelines for Supernumerary Robotic Limbs in Near-Body Interactions

Wearable supernumerary robotic limbs (SRLs) sit at the intersection of human augmentation and embodied AI, transforming into extensions of the human body. However, their movements within the intimate near-body space raise unresolved challenges for perceived safety, user control, and trust. In this paper, we present results from a Wizard-of-Oz study (n=18), where participants completed near-body collaboration tasks with SRLs to explore these challenges. We collected qualitative data through think-aloud protocols and semi-structured interviews, complemented by physiological signals and post-task ratings. Findings indicate that greater autonomy did not inherently enhance perceived safety or trust. Instead, participants identified near-body zones and paired them with clear coordination rules. They also expressed expectations for how different arm components should behave, shaping preferences around autonomy, perceived safety, and trust. Building on these insights, we introduce SRL Proxemics, a zone- and segment-level design framework showing that autonomy is not monolithic: perceived safety hinges on spatially calibrated, legible behaviors, not higher autonomy.

cs.HC↗

arXiv2025

A Survey on Methodological Approaches to Collaborative Embodiment in Virtual Reality

The application and implementation of collaborative embodiment in virtual reality (VR) are a critical aspect of the computer science landscape, aiming to enhance multi-user interaction and teamwork in immersive environments. A notable and enduring area of collaborative embodiment research focuses on approaches that enable multiple users to share control, interact, and investigate scenarios involving supernumerary arms in virtual spaces. In this survey, we will present an extensive overview of the methodologies employed in the past decade to enable collaboration in VR environments, particularly through embodiment. Using the PRISMA guidelines, we plan to analyze the study details from over 137 relevant research papers. Through this analysis, a critical assessment of the effectiveness of these methodologies will be conducted, highlighting current challenges and limitations in implementing collaborative embodiment in VR. Lastly, we discuss potential future research directions and opportunities for enhancing collaboration embodiment in virtual environments.

cs.HC↗

arXiv2025

Maritime Mission Planning for Unmanned Surface Vessel using Large Language Model

Unmanned Surface Vessels (USVs) are essential for various maritime operations. USV mission planning approach offers autonomous solutions for monitoring, surveillance, and logistics. Existing approaches, which are based on static methods, struggle to adapt to dynamic environments, leading to suboptimal performance, higher costs, and increased risk of failure. This paper introduces a novel mission planning framework that uses Large Language Models (LLMs), such as GPT-4, to address these challenges. LLMs are proficient at understanding natural language commands, executing symbolic reasoning, and flexibly adjusting to changing situations. Our approach integrates LLMs into maritime mission planning to bridge the gap between high-level human instructions and executable plans, allowing real-time adaptation to environmental changes and unforeseen obstacles. In addition, feedback from low-level controllers is utilized to refine symbolic mission plans, ensuring robustness and adaptability. This framework improves the robustness and effectiveness of USV operations by integrating the power of symbolic planning with the reasoning abilities of LLMs. In addition, it simplifies the mission specification, allowing operators to focus on high-level objectives without requiring complex programming. The simulation results validate the proposed approach, demonstrating its ability to optimize mission execution while seamlessly adapting to dynamic maritime conditions.

cs.RO↗

arXiv2025

Drone Carrier: An Integrated Unmanned Surface Vehicle for Autonomous Inspection and Intervention in GNSS-Denied Maritime Environment

This paper introduces an innovative drone carrier concept that is applied in maritime port security or offshore rescue. This system works with a heterogeneous system consisting of multiple Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs) to perform inspection and intervention tasks in GNSS-denied or interrupted environments. The carrier, an electric catamaran measuring 4m by 7m, features a 4m by 6m deck supporting automated takeoff and landing for four DJI M300 drones, along with a 10kg-payload manipulator operable in up to level 3 sea conditions. Utilizing an offshore gimbal camera for navigation, the carrier can autonomously navigate, approach and dock with non-cooperative vessels, guided by an onboard camera, LiDAR, and Doppler Velocity Log (DVL) over a 3 km$^2$ area. UAVs equipped with onboard Ultra-Wideband (UWB) technology execute mapping, detection, and manipulation tasks using a versatile gripper designed for wet, saline conditions. Additionally, two UAVs can coordinate to transport large objects to the manipulator or interact directly with them. These procedures are fully automated and were successfully demonstrated at the Mohammed Bin Zayed International Robotic Competition (MBZIRC2024), where the drone carrier equipped with four UAVS and one manipulator, automatically accomplished the intervention tasks in sea-level-3 (wave height 1.25m) based on the rough target information.

cs.RO↗

arXiv2024

Benchmarking Vision-Based Object Tracking for USVs in Complex Maritime Environments

Vision-based target tracking is crucial for unmanned surface vehicles (USVs) to perform tasks such as inspection, monitoring, and surveillance. However, real-time tracking in complex maritime environments is challenging due to dynamic camera movement, low visibility, and scale variation. Typically, object detection methods combined with filtering techniques are commonly used for tracking, but they often lack robustness, particularly in the presence of camera motion and missed detections. Although advanced tracking methods have been proposed recently, their application in maritime scenarios is limited. To address this gap, this study proposes a vision-guided object-tracking framework for USVs, integrating state-of-the-art tracking algorithms with low-level control systems to enable precise tracking in dynamic maritime environments. We benchmarked the performance of seven distinct trackers, developed using advanced deep learning techniques such as Siamese Networks and Transformers, by evaluating them on both simulated and real-world maritime datasets. In addition, we evaluated the robustness of various control algorithms in conjunction with these tracking systems. The proposed framework was validated through simulations and real-world sea experiments, demonstrating its effectiveness in handling dynamic maritime conditions. The results show that SeqTrack, a Transformer-based tracker, performed best in adverse conditions, such as dust storms. Among the control algorithms evaluated, the linear quadratic regulator controller (LQR) demonstrated the most robust and smooth control, allowing for stable tracking of the USV.

cs.CV↗

arXiv2024

An Aerial Transport System in Marine GNSS-Denied Environment

This paper presents an autonomous aerial system specifically engineered for operation in challenging marine GNSS-denied environments, aimed at transporting small cargo from a target vessel. In these environments, characterized by weakly textured sea surfaces with few feature points, chaotic deck oscillations due to waves, and significant wind gusts, conventional navigation methods often prove inadequate. Leveraging the DJI M300 platform, our system is designed to autonomously navigate and transport cargo while overcoming these environmental challenges. In particular, this paper proposes an anchor-based localization method using ultrawideband (UWB) and QR codes facilities, which decouples the UAV's attitude from that of the moving landing platform, thus reducing control oscillations caused by platform movement. Additionally, a motor-driven attachment mechanism for cargo is designed, which enhances the UAV's field of view during descent and ensures a reliable attachment to the cargo upon landing. The system's reliability and effectiveness were progressively enhanced through multiple outdoor experimental iterations and were validated by the successful cargo transport during the 2024 Mohamed BinZayed International Robotics Challenge (MBZIRC2024) competition. Crucially, the system addresses uncertainties and interferences inherent in maritime transportation missions without prior knowledge of cargo locations on the deck and with strict limitations on intervention throughout the transportation.

cs.RO↗

arXiv2024

Long-Range Vision-Based UAV-assisted Localization for Unmanned Surface Vehicles

The global positioning system (GPS) has become an indispensable navigation method for field operations with unmanned surface vehicles (USVs) in marine environments. However, GPS may not always be available outdoors because it is vulnerable to natural interference and malicious jamming attacks. Thus, an alternative navigation system is required when the use of GPS is restricted or prohibited. To this end, we present a novel method that utilizes an Unmanned Aerial Vehicle (UAV) to assist in localizing USVs in GNSS-restricted marine environments. In our approach, the UAV flies along the shoreline at a consistent altitude, continuously tracking and detecting the USV using a deep learning-based approach on camera images. Subsequently, triangulation techniques are applied to estimate the USV's position relative to the UAV, utilizing geometric information and datalink range from the UAV. We propose adjusting the UAV's camera angle based on the pixel error between the USV and the image center throughout the localization process to enhance accuracy. Additionally, visual measurements are integrated into an Extended Kalman Filter (EKF) for robust state estimation. To validate our proposed method, we utilize a USV equipped with onboard sensors and a UAV equipped with a camera. A heterogeneous robotic interface is established to facilitate communication between the USV and UAV. We demonstrate the efficacy of our approach through a series of experiments conducted during the ``Muhammad Bin Zayed International Robotic Challenge (MBZIRC-2024)'' in real marine environments, incorporating noisy measurements and ocean disturbances. The successful outcomes indicate the potential of our method to complement GPS for USV navigation.

cs.RO↗