With the evolution of human–robot collaboration in advanced manufacturing, multisensor integration has increasingly become a critical component for ensuring safety during human–robot interactions. Given the disparities in range scales, densities, ...

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• A method for sensor fusion in human–robot environment is proposed.
• A coarse localization algorithm is proposed to address point cloud scale differences.
• An improved FPFH algorithm based on overlap ratio for coarse registration.

Control moment gyroscopes (CMGs) are the most common control actuators in spacecraft. Their predictive maintenance is crucial for on-orbit operations. However, due to the scarcity of CMG fault data, constructing a diagnosis system for predictive ...

• Control moment gyroscope diagnosis using neural network without fault samples.
• A dual-level learning paradigm is proposed with theoretical derivation.
• Faulty control moment gyroscopes show lower similarity.
• Experiments ...

• A novel temporal-spatial graph construction strategy is proposed.
• Different sensors are regarded as distinct nodes, representing spatial information. The features of time-series data are considered node attributes, representing ...

Multi-sensor time-series data at different locations contains not only temporal correlation information but also spatial correlation information which is treasure for machine fault diagnosis. Existing graph construction methods mainly apply ...

Complete traffic data collection is crucial for intelligent transportation system, but due to various factors such as cost, it is not possible to deploy sensors at every location. Using spatial interpolation, the traffic data for unobserved ...

• Designed a Dynamic Graph Learning (DGL) module based on self-attention mechanism.
• Proposed a new dual branch architecture to model the diffusion mechanism among nodes.
• Explored a novel auxiliary branch to model the local details of ...

Trajectory prediction of surrounding traffic participants is vital for the driving safety of Intelligent Connected Vehicles (ICVs). It has been enabled with the help of the availability of multi-sensor information collected by ICVs. For ...

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• Modeling hyper-relational interaction boosts multi-modal trajectory prediction.
• The devised hypergraph attention networks perform better than vanilla GATs.
• The proposed model achieves the best performance and yields great ...

3D occupancy perception technology aims to observe and understand dense 3D environments for autonomous vehicles. Owing to its comprehensive perception capability, this technology is emerging as a trend in autonomous driving perception systems, ...

• Systematic review of 3D occupancy perception in autonomous driving.
• A taxonomy of 3D occupancy perception based on input modalities.
• Elaboration on methodologies: pipelines, information fusion, and network training.
• ...

• A new framework, Deep-TCP, is introduced to enhance the accuracy of tropical cyclone intensity predictions.
• The Deep-TCP framework incorporates various innovative modules designed to improve prediction accuracy and overcome traditional ...

Tropical cyclones (TC) exert a profound impact on cities, causing extensive damage and losses. Thus, TC Intensity Prediction is crucial for creating sustainable cities as it enables proactive measures to be taken, including evacuation planning, ...

Inadequate training poses a significant risk of injury among young firefighters. Although Human Activity Recognition (HAR) algorithms have shown potential in monitoring and evaluating performance, most existing studies focus on daily activities ...

• A novel multimodal sensor data fusion for firefighting activity recognition.
• Hybrid fusion models with inertial and sEMG data from multiple positions.
• Wearable sensors integrated into lightweight and custom-designed devices.
• ...

This review comprehensively investigates the current state and emerging trends of autonomous vehicle terrain detection and segmentation. By systematically reviewing literature from various databases, this study outlines the evolution of detection ...

• Explores terrain detection and segmentation technologies in autonomous vehicles.
• Evaluates current methods’ effectiveness and limitations across environments.
• Identifies challenges in advancing terrain detection and segmentation ...

High-precision fault diagnosis of rotating machinery plays an important role in industrial systems. Today, rotating machinery often has multiple sensors to monitor equipment condition, so it is important to fuse data from multiple rotating ...

• A multi-layer graph data construction method is proposed.
• A multi-type feature fusion mechanism based on the attention mechanism is proposed.
• A decision fusion strategy based on information entropy is proposed.
• A multi-level ...

Addressing the complexity of multi-task trajectory prediction, this study introduces a novel Deep Multimodal Network (DMN), which integrates a shared feature extractor and a multi-task prediction module with translational encoders to capture both ...

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• Joint prediction of trajectory locations, travel time and travel modes.
• Dependencies and interdependencies within and across multimodal features of data.
• Incorporation of both inter- and intra-modal attention mechanisms.
• Decent ...

In many processes, ranging from medical treatments to supply chains and employee management, there is a growing need to gather information with the objective of enhancing the efficiency of the process in question. Often, the information gathered ...

• PALADIN is source-agnostic language to define process-based constraints.
• The methodology to specify constraints enables the identification of the ambiguity.
• PALADIN architecture uses different evaluation strategies.
• 18 ...

The present research develops a framework to refine the classification of an individual’s activities and recognize wellness associated with their routine. The framework improves the accuracy of the classification of routine activities of a person,...

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• IoMT seamlessly integrates multimodal sensors, revolutionizing healthcare through real-time ADL monitoring.
• Multimodal ADL data fusion provides comprehensive insights into elderly living patterns and events.
• AI algorithms enable ...

In this paper, we introduce a novel coordination strategy for a group of autonomous robots tasked with estimating the source term of an airborne chemical release. This strategy integrates distributed Bayesian filtering, coverage control, ...

• Fully distributed multi-robot coordination to search an unknown release source.
• Consensus-based belief update rule to ensure a unified belief across the robots.
• Spatial diversity and collision avoidance embedded in the coordination ...

The inevitable multi-component assembly errors and complex data collection sites lead to coupling fault information and global distribution differences among individuals, making fault diagnosis of machine-level motors more challenging. This ...

• A cross-machine model for machine-level motor compound fault diagnosis.
• A lightweight coarse-fine signal pruning transformer with interpretable design.
• A convolutional tokenizer for multiscale information interaction and fusion.

This work is devoted to the problem of distributed target tracking when a team of robots detect the target through a variable perception-latency mechanism. A reference for the robots to track is constructed in terms of a desired formation around ...

• A novel continuous-discrete estimator with sub-optimal smooth output.
• Asymptotic stability is preserved by virtue of the smoothness in the estimation.
• A novel information fusion strategy based on exact dynamic consensus.
• ...

• Using multiple sensors to enable SUR to obtain high-precision estimated data.
• Building MSIF model with multisource factors to enhance SUR's practicality in tasks.
• Considering multi-sensor information to offset position, velocity ...

Currently, most of the existing fusion methods ignore the rich multi-source information of different types of sensor nodes in the underwater unknown environment, which makes it challenging for Autonomous Underwater Vehicles (AUVs) to accurately ...

Navigation/positioning systems have become critical to many applications, such as autonomous driving, Internet of Things (IoT), Unmanned Aerial Vehicle (UAV), and smart cities. However, it is difficult to provide a robust, accurate, and seamless ...

• Classifying integrated navigation systems with sources, algorithms, and scenarios.
• Classifying multi-sensor fusion based on absolute and relative positioning sources.
• Analytics-based and learning-based algorithms are discussed and ...

Human intent prediction (HIP) and human activity recognition (HAR) are important for human–robot interactions. However, human–robot interface signals are user-dependent. A classifier trained on labeled source subjects performs poorly ...

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• A novel, accurate, and efficient unsupervised cross-subject adaptation method.
• ...

Multi-Gas source localization and mapping is a challenging problem because multiple measurements must be taken to ensure accurate localization. This paper presents a novel flocking control strategy for multi-robot exploration and gas field ...

• Present a collaborative Sequential Monte Carlo method for estimating gas fields.
• Propose a flocking model to keep the robots close enough to avoid disconnections.
• Include an active sensing method for moving a robot swarm towards ...