Sensor Tracking System Research Articles

Structural design and adaptive tracking control of automatic welding robot for liquefied natural gas containment system

Liquefied natural gas (LNG) is commonly transported by LNG carriers and stored in cargo containment systems. The primary barrier of the MARK III cargo containment system is welded to a closed space with corrugated stainless steel plates. To meet the requirements of excellent sealing and thermal insulation for cargo containment, the welding process of the corrugated plate need to be strictly controlled, which poses a challenge to the development of related welding equipment. In this paper, we present a new five-axis automatic welding robot system used for plasma arc welding on corrugated surfaces. The moment transfer scheme of the dual linkage mechanism makes the rotary movement of the robot more accurate and stable which gives the system with simpler control algorithm and better overall force characteristics. To ensure tracking accuracy, a novel sensing method based on a LASER sensor, two contact sensors, and an angle sensor is proposed to implement multiple functions such as tracking the welding arc length, identifying corrugation shapes, and detecting welding gun posture. Based on the devised tracking sensor system and welding robot, a servo-control system with a surface-tracking welding control algorithm is established. The experimental results show that the robot system's welding speed is about 7 times that of hand welding and the welding qualification rate was 99%, significantly improving welding efficiency and quality as a critical equipment technology in the process of corrugated plate welding.

Improvement of Control Strategy for Optoelectronic Sensor Tracking System

Improvement of Control Strategy for Optoelectronic Sensor Tracking System

Fusion of Heterogenous Sensor Data in Border Surveillance.

Wide area surveillance has become of critical importance, particularly for border control between countries where vast forested land border areas are to be monitored. In this paper, we address the problem of the automatic detection of activity in forbidden areas, namely forested land border areas. In order to avoid false detections, often triggered in dense vegetation with single sensors such as radar, we present a multi sensor fusion and tracking system using passive infrared detectors in combination with automatic person detection from thermal and visual video camera images. The approach combines weighted maps with a rule engine that associates data from multiple weighted maps. The proposed approach is tested on real data collected by the EU FOLDOUT project in a location representative of a range of forested EU borders. The results show that the proposed approach can eliminate single sensor false detections and enhance accuracy by up to 50%.

Development and integration of laser sensor tracking system in robotic arm for path correction during welding operation

<span lang="EN-US">An industrial robot is mainly used for manufacturing. Industrial robots are 6 or more axes, which can be automatically controlled by programming. Typical applications of robots include welding, painting, picking, and placing printed circuit boards, packaging and labelling, palletizing, product inspection, and testing with high accuracy, precision, and fast speed. Robotic welding is a complex, nonlinear and time varying process which can be affected by various natural or any random disturbances. Due to the effect of various factors, the actual welding path may differ. So, welding robots should be able to detect the actual welding path, then adjust the difference in welding path and complete the welding process accurately. Laser welding is one of the most important technologies in the manufacturing field. It is the most frequently used technology which has made new demands. So, the manufacturer ensures to meet the quality of laser welding and improve the production efficiency. Due to the increase in demand of quality, accuracy, precise, productivity, flexibility and adaptive control of welding robot, an automatic laser seam tracking system is developed with welding robot to precisely follow the welding path and make the necessary corrections during welding operations.</span>

CB+NN Ensemble to Improve Tracking Accuracy in Air Surveillance

Finding or tracking the location of an object accurately is a crucial problem in defense applications, robotics and computer vision. Radars fall into the spectrum of high-end defense sensors or systems upon which the security and surveillance of the entire world depends. There has been a lot of focus on the topic of Multi Sensor Tracking in recent years, with radars as the sensors. The Indian Air Force uses a Multi Sensor Tracking (MST) system to detect flights pan India, developed and supported by BEL(Bharat Electronics Limited), a defense agency we are working with. In this paper, we describe our Machine Learning approach, which is built on top of the existing system, the Air force uses. For purposes of this work, we trained our models on about 13 million anonymized real Multi Sensor tracking data points provided by radars performing tracking activity across the Indian air space. The approach has shown an increase in the accuracy of tracking by 5 percent from 91 to 96. The model and the corresponding code were transitioned to BEL, which has been tested in their simulation environment with a plan to take forward for ground testing. Our approach comprises of 3 steps: (a) We train a Neural Network model and a CatBoost model and ensemble them using a Logistic Regression model to predict one type of error, namely Splitting error, which can help to improve the accuracy of tracking. (b) We again train a Neural Network model and a CatBoost model and ensemble them using a different Logistic Regression model to predict the second type of error, namely Merging error, which can further improve the accuracy of tracking. (c) We use cosine similarity to find the nearest neighbour and correct the data points, predicted to have Splitting/Merging errors, by predicting the original global track of these data points.

Investigation of an Autonomous Tracking System for Localization of Radio-Tagged Flying Insects

The tracking of flying insects is one of the main challenges in the field of ecosystem and biodiversity protection. In the case of invasive insects such as Asian hornets, extensive considerations must be made in estimating the habitat because of the limitations of available sensors and environmental uncertainty. In this study, we propose an approach for localizing and autonomously tracking radio-tagged flying insects and developing an unmanned aerial vehicle (UAV)-based robotic system. The extended Kalman filter is applied to the received signal strength emitted from a radio telemetry transmitter to estimate the insect’s position while reducing the measurement error and noise. The proposed autonomous tracking strategy involves a method in which the UAV rotates around one point to measure the signal strength and control its position in relation to the direction of the strongest signal. We also designed a system architecture that includes a tracking sensor system and a UAV system for radio-tagged flying insects. The effectiveness of the proposed system for estimating and autonomously tracking the target position was evaluated via numerical dynamics simulation. Furthermore, feasibility tests with unmanned ground vehicle replacements for insects and UAVs were performed to validate the proposed approach. Finally, to verify the UAV system, field tests were conducted to track a fixed radio-tagged flying target on a vast flatland. This study is unique because we propose and validate a novel mobile robot-based tracking system to localize radio tags of flying insects.

An Evaluation of Motion Trackers with Virtual Reality Sensor Technology in Comparison to a Marker-Based Motion Capture System Based on Joint Angles for Ergonomic Risk Assessment.

The reproduction and simulation of workplaces, and the analysis of body postures during work processes, are parts of ergonomic risk assessments. A commercial virtual reality (VR) system offers the possibility to model complex work scenarios as virtual mock-ups and to evaluate their ergonomic designs by analyzing motion behavior while performing work processes. In this study a VR tracking sensor system (HTC Vive tracker) combined with an inverse kinematic model (Final IK) was compared with a marker-based optical motion capture system (Qualisys). Marker-based optical motion capture systems are considered the gold standard for motion analysis. Therefore, Qualisys was used as the ground truth in this study. The research question to be answered was how accurately the HTC Vive System combined with Final IK can measure joint angles used for ergonomic evaluation. Twenty-six subjects were observed simultaneously with both tracking systems while performing 20 defined movements. Sixteen joint angles were analyzed. Joint angle deviations between and were identified. These high deviations must be considered in ergonomic risk assessments when using a VR system. The results show that commercial low-budget tracking systems have the potential to map joint angles. Nevertheless, substantial weaknesses and inaccuracies in some body regions must be taken into account. Recommendations are provided to improve tracking accuracy and avoid systematic errors.

An Autonomous Hand Hygiene Tracking Sensor System for Prevention of Hospital Associated Infections

Hospital associated infection (HAI) can lead to serious medical issues in healthcare systems, which is a major concern in the recent era. Hand hygiene is considered as an effective measure to prevent cross infections in hospital environments. Research efforts in the field of Internet of Things (IoT) can provide reliable edge-based solutions in hospitals to prevent HAI by adopting sensing, networking, and computing technologies. In this paper, we present a prototype for autonomous hand hygiene tracking combining different IoT technologies, which can be applied in healthcare environments to monitor hand hygiene activities and better prevent HAIs. The system can record hand-washing activities and provide prompt feedback if the hand-washing is not performed as required using wearable devices and smart dispensers. Collected data are transferred to a gateway via Bluetooth Low Energy (BLE) and Long Range (LoRa) wireless technologies for long-range network coverage. The hand-washing data are processed at the edge of the network (i.e., router and gateway) to provide low-latency feedback. The data can also be forwarded to a cloud server for storage, analysis, and visualization. The proposed hand hygiene tracking prototype has been tested and evaluated under different scenarios in a mock ward, demonstrating its applicability in hospital environments.

AN RGB-D DATA PROCESSING FRAMEWORK BASED ON ENVIRONMENT CONSTRAINTS FOR MAPPING INDOOR ENVIRONMENTS

Abstract. The adoption of RGB and depth (RGB-D) sensors for surveying applications (i.e., building information modeling [BIM], indoor navigation, and three-dimensional [3D] models) to replace expensive and time-consuming methods (e.g., stereo cameras, laser scanners) has recently attracted great attention. Due to the distinctive structure and scalability of indoor environments, the depth quality produced from RGB-D cameras and the simultaneous localization and mapping (SLAM) system responsible for the cameras pose estimation are substantial problems with existing RGB-D mapping systems. This study introduces a new RGB-D data processing framework that adopts two-dimensional and 3D features from RGB and depth images. To overcome the self-repetitive structure of indoor environments, the proposed framework uses novel description functions for both line and plane features extracted from RGB and depth images for further matching between successive RGB-D frame features. Also, the framework estimates the camera pose by minimizing the combined geometric distance of both two-dimensional and 3D features. Using the previously known structure of the indoor environment, the framework leverages the structural constraints to enhance 3D model precision. The framework also adopts a graph-based optimization technique to distribute the closure error to the graphs nodes and edges when a loop closure is detected. The visual RGB-D SLAM system and the default sensor tracking system (SensorFusion) were used to assess the performance of the proposed framework. The results show that the proposed framework can achieve significant improvement in 3D model accuracy.

IPAC: Integrate Pedestrian Dead Reckoning and Computer Vision for Indoor Localization and Tracking

Indoor localization is of great importance in the era of mobile computing. Smartphone-based pedestrian tracking is essential to a wide range of applications in shopping malls, industries, office buildings, and other public places. Current mainstream solutions rely on radio fingerprints and/or inertial sensors to distinguish and track pedestrians. However, these methods suffer from considerable deployment efforts and large accumulative errors. In recent years, the increasing numbers of security surveillance cameras installed in public areas provide a fresh perspective to overcome these drawbacks. However, in the real dynamic environments, fusing camera-based and inertial sensors-based pedestrian tracking is non-trivial due to the low robustness of visual tracking, incorrespondence of identifications and high complexity of computation. This paper presents the design and implementation of iPAC, an integrated inertial sensor and camera-based indoor localization and tracking system that achieves high accuracy in dynamic indoor environments with zero human effort. iPAC designs a robust visual detection and tracking algorithm to differentiate and track pedestrians in dynamic environments. Furthermore, iPAC employs a motion sequence-based matching algorithm to fuse raw estimates from both systems. By doing so, iPAC outputs enhanced accuracy, while overcoming the respective drawbacks of each sub-system. We implement iPAC on commodity smartphones and validate its performance in complex environments (including a laboratory, a classroom building, and a office building). The result shows that iPAC achieves a remarkable detection success rate of 93% and tracking success rate of 95% even suffering from severe line-of-sight blockages.

Multi-Sensor Multi-Target Tracking Using Probability Hypothesis Density Filter

Compared with the single sensor tracking system, the multi-sensor tracking system has several advantages in target tracking, such as a larger field of view and higher tracking accuracy. Different from the multi-sensor filters based on the random finite set (RFS) theory, the product multi-sensor probability hypothesis density (PM-PHD) filter with a modified cardinality coefficient performs well in estimating the number of targets. Since the PM-PHD filter employs the iterative fusion structure, its state estimation is sensitive to the sensor parameters. Furthermore, to improve the cardinality estimation, the PM-PHD filter may estimate some false targets when miss-detection occurs. Addressing the above problems, this paper changes the fusion structure of the PM-PHD filter and presents a novel version of the PM-PHD filter. The main idea of the proposed algorithm is the combinations of measurement subsets and other factors. Both the cardinality estimation and the state estimation are obtained by fusing the target numbers and normalized PHDs of these combinations. Compared with other multi-sensor PHD filters, the proposed algorithm can handle the problems of miss-detection and false alarm effectively. Moreover, the simulation results and the theoretical analysis indicate that the new PM-PHD filter can deal with a harsh tracking environment.

COASTAL FLOODING-INDUCED DEBRIS MOTION

Extreme coastal events such as the 2004 Indian Ocean Tsunami, the 2011 Tohoku Tsunami as well as the 2005 Katrina and 2012 Sandy hurricanes demonstrated that, hydrodynamic loading aside, debris loading represents a major factor in the extreme loading conditions experienced by inland infrastructure. While extreme hydrodynamic loading due to coastal flooding events has been the object of intense research over the past decade, few studies dealing with debris impact due to coastal flooding have been conducted. Post-tsunami forensic engineering field investigations conducted by the authors (Nistor et al. 2005; Shibayama et al., 2006; Nistor et al. 2010) or other researchers (Chock et al. 2012, Sato et al. 2014;) revealed that debris loading and debris damming have a significant effect on the structural integrity of buildings and infrastructure in general, especially in high-density urban areas. Current design guidelines (FEMA P-55, 2011; FEMA P-646, 2012) and the new ASCE-7 Chapter 6 - Tsunami Effects and Loads suggest conservative estimations of the debris spatial spreading based on limited sets of field data to assess debris impact potential (Chock et al., 2012; Naito et al., 2014). The primary objective of this study was to investigate the spatial and temporal displacement of floating debris due to rapid coastal flooding in a built-in port environment using two novel different measuring techniques: (1) high-accuracy video debris detection and tracking system and (2) a real-time locating “smart” sensor-tracking system. Results are compared with prescriptions of the ASCE7 Chapter 6 pertaining to debris motion.

Credit-Based Multiple Human Location for Passive Binary Pyroelectric Infrared Sensor Tracking System: Free From Region Partition and Classifier

We present a credit-based multiple human location method for passive binary pyroelectric infrared sensor tracking system. Different from the traditional Fresnel-lens pyroelectric infrared (PIR) system, we use a special optical cone to design a small and flat PIR sensor network system and propose a petal-shape sensing model for sensor node. The angular bisector of one petal is a detection line and all intersection points of activated detection lines are measurement pointes. We return to the essence of sensing and propose the credit to represent the probability of measurement points falling into the field of view of the sensors. Multiple human targets can be located by cluster analyzing and data association with the highest credit, which makes the human location free from region partition and region classifier. The simulation and experimental results show that the proposed location method has improved the location accuracy, and meanwhile, reduced the execution time and the dependence on the layout of sensor network.

Development of Automated Guidance Tracking Sensor System Based on Laser Distance Sensors

Development of Automated Guidance Tracking Sensor System Based on Laser Distance Sensors

Commissioning of the 4-D treatment delivery system for organ motion management in synchrotron-based scanning ion beams

PurposeThe aim of this work was the commissioning of delivery procedures for the treatment of moving targets in scanning pencil beam hadrontherapy. MethodsEBT3 films fixed to the Anzai Respiratory Phantom were exposed to carbon ion scanned homogeneous fields (E=332MeV/u). To evaluate the interplay effect, field size and flatness for 3 different scenarios were compared to static condition: gated irradiation or repainting alone and combination of both. Respiratory signal was provided by Anzai pressure sensor or optical tracking system (OTS). End-exhale phase and 1s gating window were chosen (2.5mm residual motion). Dose measurements were performed using a PinPoint ionization chamber inserted into the Brainlab ET Gating Phantom. A sub-set of tests was also performed using proton beams. ResultsThe combination of gating technique and repainting (N=5) showed excellent results (6.1% vs 4.3% flatness, identical field size and dose deviation within 1.3%). Treatment delivery time was acceptable. Dose homogeneity for gated irradiation alone was poor. Both Anzai sensor and OTS appeared suitable for providing respiratory signal. Comparisons between protons and carbon ions showed that larger beam spot sizes represent more favorable condition for minimizing motion effect. ConclusionResults of measurements performed on different phantoms showed that the combination of gating and layered repainting is suitable to treat moving targets using scanning ion beams. Abdominal compression using thermoplastic masks, together with multi-field planning approach and multi-fractionation, have also been assessed as additional strategies to mitigate the effect of patient respiration in the clinical practice.

Distributed Multi-Human Location Algorithm Using Naive Bayes Classifier for a Binary Pyroelectric Infrared Sensor Tracking System

This paper presents a distributed multi-human location algorithm for a binary pyroelectric infrared sensor tracking system. The tracking space of our system is divided into many uniform static sub-regions. A two-level regional location, static partitioning and dynamic partitioning, is proposed. A Naive Bayes classifier is used to simplify the human location in a static sub-region, and we achieve the initial location of human by fusing all the internal measuring points of infrared sensors. Taking this initial location as the center, a new secondary dynamic sub-region is defined and all its internal measuring points of infrared sensors are fused again to get the ultimate human location. The simulation and experimental results demonstrate that the proposed method has improved the locating accuracy of multiple human targets with low computational cost in infrared sensor tracking system.

The Role of High Performance Computing and Communication for Real-Time Biofeedback in Sport

This paper studies the main technological challenges of real-time biofeedback in sport. We identified communication and processing as two main possible obstacles for high performance real-time biofeedback systems. We give special attention to the role of high performance computing with some details on possible usage of DataFlow computing paradigm. Motion tracking systems, in connection with the biomechanical biofeedback, help in accelerating motor learning. Requirements about various parameters important in real-time biofeedback applications are discussed. Inertial sensor tracking system accuracy is tested in comparison with a high performance optical tracking system. Special focus is given on feedback loop delays. Real-time sensor signal acquisitions and real-time processing challenges, in connection with biomechanical biofeedback, are presented. Despite the fact that local processing requires less energy consumption than remote processing, many other limitations, most often the insufficient local processing power, can lead to distributed system as the only possible option. A multiuser signal processing in football match is recognised as an example for high performance application that needs high-speed communication and high performance remote computing. DataFlow computing is found as a good choice for real-time biofeedback systems with large data streams.

Fuzzy decision method to improve the information exchange in a vehicle sensor tracking system

Internet of Things is based on the identification of real-world objects in a unique way that interconnects them by means of communication interfaces. Such a simple idea allows the emergence of a huge number of new applications in almost any domain of knowledge. One of the most prominent areas of application is road vehicles, which currently have, on average, more than 50 sensors inside and which information can be accessed through a standard protocol. With this, vehicles have become real smart objects that can interact with other objects or any software system. To allow that, our previous work focused on proposing and developing the Vitruvius platform, where users with no programming knowledge can design and quickly generate Web applications based on the real-time data consumption from interconnected vehicles. The problem is that the sending of such information is out of control, being unable to filter out when the best time to send the information is and what information should be sent at any moment in order to minimize the resource consumption of the mobile device that acts as a bride between the vehicle and the database in which all the information is stored. Thus, in this work we propose a fuzzy algorithm that allows to optimize the resources that are used by real-time applications that constantly send data while maintaining data quality, contextualized in vehicle sensor tracking systems and the applications that can be built above them.

A novel multi-human location method for distributed binary pyroelectric infrared sensor tracking system: Region partition using PNN and bearing-crossing location

This paper proposes a novel multi-human location method for distributed binary pyroelectric infrared sensor tracking system based on region partition using probabilistic neural network and bearing-crossing location. The detection space of system is divided into many sub-regions and encoded uniformly. The human region is located by an integrated neural network classifier, which is developed based on the probabilistic neural network ensembles and the Bagging algorithm. The location of a human target can be achieved by first determining a coarse location by this classifier and then a fine location using our previous bearing-crossing location method. Simulation and experimental results have shown that the human region can be judged rapidly and the false detection points of multi-human location can be eliminated effectively. Compared with the bearing-crossing location method, the novel method has significantly improved the locating and tracking accuracy of multiple human targets in infrared sensor tracking system.

Multiple sensor tracking architecture comparison

There are several multiple sensor tracking architectures referenced in the literature [1]-[11]. The basic architectures have individual sensors observing the same targets and sending their information about those targets to a fusion center, where a system track, which is a fusion of the individual sensor data, is formed. The fusion center could be a distributed architecture itself, but this article will only examine the roles of the individual sensors, the fusion center (whether at one central location or distributed), and the information passed between them. These architectures range from each sensor tracking the targets and sending a state vector and covariance matrix to the fusion center to each sensor sending all measurements (targets and clutter) to the fusion center for tracking and state estimation. Choosing which architecture to use involves decisions between optimal estimation, practical use of communication bandwidth between the sensors and the fusion center, and computer resources required at either location. A comparative study of architectures in the literature has been limited to varieties of one general architecture (track-to-track fusion) [4]-[11]. This article will examine advantages and disadvantages of each system and show that there is one architecture approach rarely mentioned in the literature that has more advantages than the others for a general multiple sensor tracking system. This article uses the term tracking architectures to describe the different approaches to sensor fusion, which is used elsewhere in the literature [2]. The term tracking configurations has also been used [1]. The use of the term architectures in this article is equivalent to the term configurations found in [1].