Journal of Field Robotics最新文献

This paper proposes a monocular visual-based navigation state estimator designed to operate onboard cost-effective Autonomous Underwater Vehicles (AUVs) in monitoring and inspection applications. The estimator exploits a monocular visual odometry solution, named Mono UVO (Monocular Underwater Visual Odometry), integrating acoustic range information to make the scale observable and provide an estimate of the robot linear velocity in complex and unstructured underwater scenarios. By utilizing an Extended Kalman Filter, the visual-based linear velocity is fused with robot attitude and depth measurements to retrieve the AUV navigation state. The proposed navigation framework was extensively tested offline using heterogeneous data sets of real underwater images collected during several experimental campaigns. Moreover, online validation of the navigation state estimator was performed onboard an AUV to accomplish a closed-loop autonomous survey at sea. The performance of the state estimator is evaluated by comparing the estimation output with reference signals obtained from Doppler Velocity Log measurements and GPS when available. The results demonstrate the feasibility of the proposed visual-based state estimator in providing reliable AUV navigation state in very different and challenging underwater environments. Among the contributions, the source code of the Mono UVO algorithm is made available online, together with the release of an underwater data set.

Complex physicochemical environmental effects result in highly intricate and diverse backgrounds in underwater object images, posing significant challenges for detecting structural defects. Besides, current methods overlook the tradeoff between the detection accuracy and computational cost. To effectively address the aforementioned issues, we present a lightweight multiscale feature fusion network (LMFFNet) for underwater structural defect detection. Aiming to enhance the feature representability, spatial attention, and bidirectional pyramid modules are jointly employed for fusing multiscale features. A parameter-sharing header module is designed to reduce the model parameters. The dynamic nonmonotonic focusing mechanism is introduced in the loss function, which can improve the defect detection performance on degraded underwater images. Comprehensive experiments on a real-world underwater data set demonstrate the superiority of the LMFFNet over existing state-of-the-art methods.

The grape and wine industry suffers substantial losses annually due to diseases like downy mildew and grapevine leafroll-associated virus 3. Effective control of these diseases hinges on precise and timely diagnosis, which is often hindered by the shortage of highly skilled disease scouts. This highlights the urgent need for alternative, scalable solutions. We introduce PhytoPatholoBot (PPB), a fully autonomous ground robot equipped with a custom imaging system and onboard analysis pipeline for near-real-time disease detection and severity quantification, enabling rapid disease assessments in vineyards. The imaging system uses active illumination to enhance image quality and consistency, addressing a key challenge in ensuring the generalizability of analysis models. The analysis pipeline incorporates a disease mapping near-real-time model, a custom segmentation model designed for deployment on low-power edge computing devices, allowing near-real-time inference. PPB was deployed in both research and commercial vineyards for field-based disease scouting. Experimental results demonstrated that its disease detection and severity quantification performance was comparable to those of experienced human scouts and advanced offline computer vision models, while maintaining high computational efficiency and low-power consumption suited to field robots. PPB's ability to map disease progression over the growing season and manage multiple disease types in previously unseen vineyards highlights its potential to advance agricultural research and improve vineyard disease management practices.

The detection of floating garbage on the water surface significantly aids unmanned surface vessels in quickly perceiving their surrounding environment, which is crucial for the development of water surface garbage monitoring and automated debris collection. However, the relatively small size of the detection target compared to the water surface background, along with its susceptibility to noise interference such as light, water waves, and reflections, significantly increases the difficulty of detection. To address the above challenges, this paper proposes a shallow information-injected pyramidal network, SI-FPN, and integrates it with the YOLOv11 target detection network to create the SI-FloatDet framework for complex water surface scenarios. Firstly, to better capture the detailed features of small targets, we design a plug-and-play pyramid network (SI-FPN) that can help solve the problem of information interaction between neighboring feature layers. Secondly, to suppress the noise interference on surface targets, we develop an adaptive spatial refinement module (ASRM). We conduct experiments on the Flow-Img dataset, which contains a large number of small targets. The results show that compared to the original YOLOv11 model, SI-FloatDet improves by 6.1% and 4.6% in [email protected] and [email protected]:0.95, respectively, and outperforms the existing model in detecting both small and medium targets. Additionally, field experiments were conducted on a water surface trash-cleaning robot equipped with a vision system. The results show that SI-FloatDet maintains high accuracy in complex scenarios (e.g., bright light, reflective interference), verifying its reliability and effectiveness in practical applications. This method provides an efficient and reliable solution for detecting water surface litter.

Unmanned aerial vehicles (UAVs) have become indispensable assets across military, commercial, and civil domains due to their operational flexibility, cost-efficiency, and real-time sensing capabilities. However, the increasing integration of UAVs into critical infrastructure, combined with their reliance on wireless communications, Global Positioning System, and embedded control systems, has significantly expanded their cybersecurity attack surface. Despite growing research efforts, a comprehensive understanding of the unique security challenges facing UAV systems remains fragmented. This survey systematically analyzes the multifaceted cybersecurity threats targeting UAV platforms, encompassing communication links, navigation subsystems, onboard controllers, and ground control stations. We develop a unified threat taxonomy that classifies attacks such as spoofing, jamming, denial-of-service, hijacking, and malware injection, and assess their impacts on the core security pillars of confidentiality, integrity, and availability. Furthermore, existing defense mechanisms—including cryptographic protocols, intrusion detection systems, machine learning-based anomaly detection, secure routing algorithms, and authentication schemes—are critically evaluated with respect to their effectiveness, scalability, resource consumption, and suitability for UAV-specific operational constraints. Unlike previous surveys, this study synthesizes cross-layer defense strategies and highlights open research gaps that remain underexplored. Finally, emerging directions such as blockchain integration, federated learning, and quantum-resistant cryptographic frameworks are discussed, aiming to inspire robust and adaptive security architectures for next-generation UAV ecosystems. This survey offers valuable insights for researchers, system architects, and policymakers committed to advancing UAV cybersecurity in increasingly contested and dynamic environments.