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With the rapid development of the indoor spaces positioning technologies such as the radio-frequency identification (RFID), Bluetooth and WI-FI, the locations of indoor spatial objects (static or moving) constitute an important foundation for a variety of applications. However, there are many challenges and limitations associated with the structuring and querying of spatial objects in indoor spaces. The purpose of this study is to address the current trends, limitations and future challenges associated with the structuring and querying of spatial objects in indoor spaces. Also it addresses the related features of indoor spaces such as indoor structures, positioning technologies and others.

In this paper, the author focuses on understanding the aspects and challenges of spatial database managements in indoor spaces. The author explains the differences between indoor spaces and outdoor spaces. Also examines the issues pertaining to indoor spaces positioning and the impact of different shapes and structures within these spaces. In addition, the author considers the varieties of spatial queries that relate specifically to indoor spaces.

Most of the research on data management in indoor spaces does not consider the issues and the challenges associated with indoor positioning such as the overlapping of Wi-Fi. The future trend of the indoor spaces includes included different shapes of indoors beside the current 2D indoor spaces on which the majority of the data structures and query processing for spatial objects have focused on. The diversities of the indoor environments features such as directed floors, multi-floors cases should be considered and studied. Furthermore, indoor environments include many special queries besides the common ones queries that used in outdoor spaces such as KNN, range and temporal queries. These special queries need to be considered in data management and querying of indoor environments.

To the best of the author’s knowledge, this paper successfully addresses the current trends, limitations and future challenges associated with the structuring and querying of spatial objects in indoor spaces.

The purpose of this paper is to develop an intelligent robot assembly system for the moving production line. Moving production lines are widely used in many manufacturing factories, including automotive and general industries. Industrial robots are hardly used to perform any tasks on the moving production lines. One of the main reasons is that it is difficult for conventional industrial robots to adjust to any sort of change. Therefore, more intelligent industrial robotic systems have to be developed to adopt the random motion of the moving production lines. This paper presents an intelligent robotics system that performs an assembly process while the object is moving, using synergic combination of visual servoing and force control technology.

The developed intelligent robotic system includes some rules to ensure the success of the assembly processes. Also visual servoing and force control are used to deal with the random motion of the moving objects. Since the objects on the moving production lines are moving with random speed, visual servoing is adopted to tracking the motion of the moving object. Force control is also integrated to control the motion of the robot and keep the robotic system compliant with the moving objects to avoid the damage of the whole system.

The developed intelligent robotic technology has been successfully implemented. The wheel loading process is used as example.

Since the developed technology is based on the low‐level motion control, safety has to be considered. Currently, it is done by motion supervision.

The developed technology can be used to perform assemblies in the moving production lines. Since the developed platform is based on the synergic combination of visual servoing and force control technology, it can be used in other areas, such as seam tracking and seat loading, etc.

This paper provides a practical solution of performing assemblies on the moving production lines, which is not available on the current industrial robot market.

Mobile robots become more and more important for many potential applications such as navigation and surveillance. The paper proposes an image processing scheme for moving object detection from a mobile robot with a single camera. It especially aims at intruder detection for the security robot on either smooth paved surfaces or uneven ground surfaces.

The core of the proposed scheme is the template matching with basis image reconstruction for the alignment between two consecutive images in the video sequence. The most representative template patches in one image are first automatically selected based on the gradient energies in the patches. The chosen templates then form a basis matrix, and the instances of the templates in the subsequent image are matched by evaluating their reconstruction error from the basis matrix. For the two well-aligned images, a simple and fast temporal difference can thus be applied to identify moving objects from the background.

The proposed template matching can tolerate in rotation (±10°) and (±10°) in scaling. By adding templates with larger rotational angles in the basis matrixes, the proposed method can be further extended for the match of images from severe camera vibrations. Experimental results of video sequences from a non-stationary camera have shown that the proposed scheme can reliably detect moving objects from the scenes with either minor or severe geometric transformation changes. The proposed scheme can achieve a fast processing rate of 32 frames per second for an image of size 160×120.

The basic approaches for moving object detection with a mobile robot are feature-point match and optical flow. They are relatively computational intensive and complicated to implement for real-time applications. The proposed template selection and template matching are very fast and easy to implement. Traditional template matching methods are based on sum of squared differences or normalized cross correlation. They are very sensitive to minor displacement between two images. The proposed new similarity measure is based on the reconstruction error from the test image and its reconstruction from the linear combination of the templates. It is thus robust under rotation and scale changes. It can be well suited for mobile robot surveillance.

Aims to develop a robotic platform to autonomously track a moving object

This robotic platform, based on a modular system known as SafeBot, uses two sensors: a visual CCD camera and a laser‐based range sensor. The rigidly mounted camera tracks an object in front of the platform and generates appropriate drive commands to keep the object in view, even if the object itself moves. The range sensor detects other objects as the platform moves to provide real‐time obstacle avoidance while continuously tracking the original object.

The current approach successfully tracks an object, particularly a human subject, and avoids reasonably sized obstacles, but on‐board processing limitations restrict the speed of the object to approximately 5 km/h.

The core technology – a moving object tracked by a mobile robot with real‐time obstacle avoidance – is an integrated system comprising object tracking on a mobile platform and real‐time obstacle avoidance with robotic control. This system is applicable to a variety of automated applications such as inventory management, industrial palette distribution, and intruder surveillance.

Surveillance is the emerging concept in the current technology, as it plays a vital role in monitoring keen activities at the nooks and corner of the world. Among which moving object identifying and tracking by means of computer vision techniques is the major part in surveillance. If we consider moving object detection in video analysis is the initial step among the various computer applications. The main drawbacks of the existing object tracking method is a time-consuming approach if the video contains a high volume of information. There arise certain issues in choosing the optimum tracking technique for this huge volume of data. Further, the situation becomes worse when the tracked object varies orientation over time and also it is difficult to predict multiple objects at the same time. In order to overcome these issues here, we have intended to propose an effective method for object detection and movement tracking. In this paper, we proposed robust video object detection and tracking technique. The proposed technique is divided into three phases namely detection phase, tracking phase and evaluation phase in which detection phase contains Foreground segmentation and Noise reduction. Mixture of Adaptive Gaussian (MoAG) model is proposed to achieve the efficient foreground segmentation. In addition to it the fuzzy morphological filter model is implemented for removing the noise present in the foreground segmented frames. Moving object tracking is achieved by the blob detection which comes under tracking phase. Finally, the evaluation phase has feature extraction and classification. Texture based and quality based features are extracted from the processed frames which is given for classification. For classification we are using J48 ie, decision tree based classifier. The performance of the proposed technique is analyzed with existing techniques k-NN and MLP in terms of precision, recall, f-measure and ROC.

The purpose of this paper is to analyze the unsteady conjugate mixed convective heat transfer characteristics in a vented porous cavity under the combined effects of moving conductive elliptic object and magnetic field.

The finite element method and arbitrary Lagrangian-Eulerian (ALE), impacts of Reynolds number, Hartmann number, aspect ratio of the conductive ellipse and moving speed of the object on the hydro-thermal performance are analyzed.

It was observed that the dynamic characteristics of the local and average Nu number of each hot wall are different. Magnetic field strength increment resulted in the enhancement of average Nu number for bot steady and transient case while the optimum case for best hydro-thermal performance is achieved for highest Ha number and non-dimensional time of 10. Higher value of average Nu and lower pressure coefficient are achieved for aspect ratio of 4 and non-dimensional time of 10. When the moving velocity of the conductive ellipse is considered, 42% enhancement in the average Nu is obtained at non-dimensional time of 20 and object velocity equals to 0.012 times entering fluid velocity in the negative y direction while the pressure coefficient is higher. The moving object is used as a useful tool to control the dynamic features of heat transfer in a vented cavity.

The present method of convective heat transfer control inside a vented cavity with a moving elliptic object is novel and can be used as an effective tool with magnetic field effects owing to diverse use of convection in cavities with vented ports in many practical thermal engineering systems.

Multi-domain convolutional neural network (MDCNN) model has been widely used in object recognition and tracking in the field of computer vision. However, if the objects to be tracked move rapid or the appearances of moving objects vary dramatically, the conventional MDCNN model will suffer from the model drift problem. To solve such problem in tracking rapid objects under limiting environment for MDCNN model, this paper proposed an auto-attentional mechanism-based MDCNN (AA-MDCNN) model for the rapid moving and changing objects tracking under limiting environment.

First, to distinguish the foreground object between background and other similar objects, the auto-attentional mechanism is used to selectively aggregate the weighted summation of all feature maps to make the similar features related to each other. Then, the bidirectional gated recurrent unit (Bi-GRU) architecture is used to integrate all the feature maps to selectively emphasize the importance of the correlated feature maps. Finally, the final feature map is obtained by fusion the above two feature maps for object tracking. In addition, a composite loss function is constructed to solve the similar but different attribute sequences tracking using conventional MDCNN model.

In order to validate the effectiveness and feasibility of the proposed AA-MDCNN model, this paper used ImageNet-Vid dataset to train the object tracking model, and the OTB-50 dataset is used to validate the AA-MDCNN tracking model. Experimental results have shown that the augmentation of auto-attentional mechanism will improve the accuracy rate 2.75% and success rate 2.41%, respectively. In addition, the authors also selected six complex tracking scenarios in OTB-50 dataset; over eleven attributes have been validated that the proposed AA-MDCNN model outperformed than the comparative models over nine attributes. In addition, except for the scenario of multi-objects moving with each other, the proposed AA-MDCNN model solved the majority rapid moving objects tracking scenarios and outperformed than the comparative models on such complex scenarios.

This paper introduced the auto-attentional mechanism into MDCNN model and adopted Bi-GRU architecture to extract key features. By using the proposed AA-MDCNN model, rapid object tracking under complex background, motion blur and occlusion objects has better effect, and such model is expected to be further applied to the rapid object tracking in the real world.

The purpose of this paper is to address the online localization of mobile (service) robots in real world dynamic environments. Most of the techniques developed so far have been designed for static environments. What is presented here is a novel technique for mobile robot localization in quasi‐dynamic environments.

The proposed approach employs a probability grid map and Baye's filtering techniques. The former is used for representing the possible changes in the surrounding environment which a robot might have to face.

Simulation and experimental results show that this approach has a high degree of robustness by taking into account both sensor and world uncertainty. The methodology has been tested under different environment scenarios where diverse complex objects having different sizes and shapes were used to represent movable and non‐movable entities.

The results can be applied to diverse robotic systems that need to move in changing indoor environments such as hospitals and places where people might require assistance from autonomous robotic devices. The methodology is fast, efficient and can be used in fast‐moving robots, allowing them to perform complex operations such as path planning and navigation in real time.

What is proposed here is a novel mobile robot localization approach that enables unmanned vehicles to effectively move in real time and know their current location in dynamic environments. Such an approach consists of two steps: a generation of the probability grid map; and a recursive position estimation methodology employing a variant of the Baye's filter.

Classifying moving objects in video sequences has been extensively studied, yet it is still an ongoing problem. In this paper, we propose to solve moving objects classification problem via an extended version of two-dimensional principal component analysis (2DPCA), named as category-wise 2DPCA (CW2DPCA). A key component of the CW2DPCA is to independently construct optimal projection matrices from object-specific training datasets and produce category-wise feature spaces, wherein each feature space uniquely captures the invariant characteristics of the underlying intra-category samples. Consequently, on one hand, CW2DPCA enables early separation among the different object categories and, on the other hand, extracts effective discriminative features for representing both training datasets and test objects samples in the classification model, which is a nearest neighbor classifier. For ease of exposition, we consider human/vehicle classification, although the proposed CW2DPCA-based classification framework can be easily generalized to handle multiple objects classification. The experimental results prove the effectiveness of CW2DPCA features in discriminating between humans and vehicles in two publicly available video datasets.

The purpose of this paper is to present a new vision-based control method, which enables delta-type parallel robots to track and manipulate objects moving in arbitrary trajectories. This constitutes an enhanced variant of the linear camera model-camera space manipulation (LCM-CSM).

After obtaining the LCM-CSM view parameters, a moving target’s position and its velocity are estimated in camera space using Kalman filter. The robot is then commanded to reach the target. The proposed control strategy has been experimentally validated using a PARALLIX LKF-2040, an academic delta-type parallel platform and seven different target trajectories for which the positioning errors were recorded.

For objects that moved manually along a sawtooth, zigzag or increasing spiral trajectory with changing velocities, a maximum positioning error of 4.31 mm was found, whereas objects that moved on a conveyor belt at constant velocity ranging from 7 to 12 cm/s, average errors between 2.2-2.75 mm were obtained. For static objects, an average error of 1.48 mm was found. Without vision-based control, the experimental platform used has a static positioning accuracy of 3.17 mm.

The LCM-CSM method has a low computational cost and does not require calibration or computation of Jacobians. The new variant of LCM-CSM takes advantage of aforementioned characteristics and applies them to vision-based control of parallel robots interacting with moving objects.

A new variant of the LCM-CSM method, traditionally used only for static positioning of a robot’s end-effector, was applied to parallel robots enabling the manipulation of objects moving along unknown trajectories.