Dynamic Objects Research Articles

Synthesis of the neuro-fuzzy regulator with genetic algorithm

Real-acting objects are characterized by the presence of various types of random perturbations, which significantly reduce the quality of the control process, which determines the use of modern methods of intellectual technology to solve the problem of synthesis of control systems of structurally complex dynamic objects, allowing to compensate the influence of external factors with the properties of randomness and partial uncertainty. The article considers issues of synthesis of the automatic control system of dynamic objects by applying the theory of intelligent control. In this case, a neural network based on radial-basis functions is used at each discrete interval for neuro-fuzzy approximation of the control system, allowing real-time adjustment of the regulator parameters. The radial basis function is designed to approximate functions defined in the implicit form of pattern sets. The neuro-fuzzy regulator's parameter configuration is accomplished using a genetic algorithm, enabling more efficient computation to determine the regulator's set parameters. The regulator's parameters are represented as a vector, facilitating their application to multidimensional objects. To determine the optimal tuning parameters of the neuro-fuzzy regulator, characterized by high convergence and the possibility of determining global extrema, a genetic algorithm was used. The effectiveness of the neuro-fuzzy regulator is explained by the possibility of providing quality control of the dynamic object under random perturbations and uncertainty of input data.

Optimization of Ship Power Supply Network and Intelligent Energy Management Strategy Under Multiple Energy Modes

<p>Purpose: This article aims to address the issues of incomplete management models and slow convergence speed of optimization models in energy management in ship multi energy systems, and to construct a comprehensive dynamic optimization objective model. Method: Firstly, establish an optimization design model with the objective functions of energy storage system cost, grid power fluctuation smoothing, and energy supply and demand balance; Then, in the optimization process of the objective function, a bus voltage coordination control strategy is adopted, and for the parameter optimization problem in the strategy, a cuckoo search algorithm based on population feature feedback is used for optimization. Result: Through simulation experiments, the method proposed in this article provides effective guidance for capacity configuration and energy management of multi energy ship microgrids, improving quality and efficiency.</p> <p> </p>

Scalar fields features of video sequences energy characteristics

To improve the methods of spatio-temporal filtering, an approach based on the analysis of finitary images energy spectra can be used. The purpose of this work is to study the possibility of using energy spectra for motion identification in video sequence frames. Each frame of a video sequence is a spatially finite image, the energy spectrum of which contains information about the phase components. This information is of particular importance for determining the location of dynamic objects. The most important information is also contained in the harmonic amplitude increments of the frame energy spectra, i.e. the energy spectrum generates a scalar discrete field. The energy functions describe inter-frame changes in the amplitudes of harmonics of the energy spectrum. To apply energy spectra in motion identification algorithms, it is necessary to analyze the properties of these scalar fields. Scalar fields generated by energy spectra of frames of test video sequences are studied. Test video sequences with an object moving in an arbitrary direction are used. Scalar fields of energy functions are presented graphically. Characteristic regions of scalar fields are identified and their projections are built using isolines. The boundaries of the characteristic areas and the coordinates of extrema of energy functions are determined. From them, all coordinates of the dynamic object and its horizontal/vertical displacements can be found. The dependence of phase-energy function extrema on the position of the object before the motion and on the direction of motion is studied. When moving in an arbitrary direction, different types of extrema are observed: point and extended. At vertical or horizontal movements, the energy functions graphs have only two-dimensional pulses. Additional criteria for eliminating ambiguities are proposed. The ambiguities are due to the fact that the number and nature of extrema may coincide for different directions of motion and location of objects in different quadrants of the coordinate plane. The extrema of energy functions for object movements in all possible directions and locations in any quadrant of a video sequence frame are determined. The obtained results showed that energy functions for object motion in arbitrary direction have a large number of characteristic regions containing point and extended extrema. The configuration of extrema depends both on the direction of motion and on the quadrant of the image in which the object is located. Therefore, it is most advisable to begin to determine the spatial coordinates of an object and the magnitude of its horizontal/vertical movement by finding the maximum and minimum boundaries of energy functions, and then refine it using the coordinates of the extrema. Consideration of characteristic regions and extrema of scalar fields of energy functions can be used in the development of algorithms effective in terms of "accuracy/speed" ratio, and can increase the performance of image and video processing systems. Algorithms for determining the object position, velocity and direction of its movement by analyzing scalar fields of energy spectra components can be used as an additional or main tool for motion identification in technical vision systems.

Intelligent algorithm for tracking highly dynamic aeroballistic objects based on motion trajectory estimates

Intelligent algorithm for tracking highly dynamic aeroballistic objects based on motion trajectory estimates

Estimation of a dynamic object location in dense and high-rise urban development using auxiliary information

Estimation of a dynamic object location in dense and high-rise urban development using auxiliary information

Understanding Human Dynamic Sampling Objectives to Enable Robot-assisted Scientific Decision Making

Truly collaborative scientific field data collection between human scientists and autonomous robot systems requires a shared understanding of the search objectives and tradeoffs faced when making decisions. Therefore, critical to developing intelligent robots to aid human experts is an understanding of how scientists make such decisions and how they adapt their data collection strategies when presented with new information in situ . In this study, we examined the dynamic data collection decisions of 108 expert geoscience researchers using a simulated field scenario. Human data collection behaviors suggested two distinct objectives: an information-based objective to maximize information coverage and a discrepancy-based objective to maximize hypothesis verification. We developed a highly simplified quantitative decision model that allows the robot to predict potential human data collection locations based on the two observed human data collection objectives. Predictions from the simple model revealed a transition from information-based to discrepancy-based objective as the level of information increased. The findings will allow robotic teammates to connect experts’ dynamic science objectives with the adaptation of their sampling behaviors and, in the long term, enable the development of more cognitively compatible robotic field assistants.

Design of lateral dynamic control objectives for multi-wheeled distributed drive electric vehicles

The lateral dynamic control system for electric vehicles aims to improve vehicle performance, making it critical to propose reasonable lateral dynamic control objectives. Therefore, in this study, a novel lateral dynamic control objectives is proposed for multi-wheeled distributed drive electric vehicles. Initially, utilizing the classical two-degrees-of-freedom (2DOF) vehicle model, two initial yaw rate references were established for handling characteristics of manoeuvrability and stability, respectively. The yaw rate references consider the vehicle velocity, steering wheel angle and road friction coefficient limitations. Considering the vital role of the sideslip angle in ensuring vehicle stability, a sophisticated weighting factor was developed based on a detailed analysis of the vehicle dynamics within the sideslip angle-sideslip angle rate phase plane. The weighting factor was determined based on the position of the vehicle state in the phase plane. This factor was adeptly applied to harmonise our dual control objectives, culminating in an integrated control aim that encapsulates the individual benefits of each. Finally, the lateral dynamic control objective was realised using a direct yaw moment control (DYC) system. The effectiveness of the proposed control objective was validated through hardware-in-the-loop (HIL) simulations.

ARD-SLAM: Accurate and robust dynamic SLAM using dynamic object identification and improved multi-view geometrical approaches

In the evolving landscape of autonomous navigation, traditional Visual Simultaneous Localization and Mapping (SLAM) systems often encounter challenges in dynamic environments, primarily due to their reliance on assumptions of static surroundings. In response to these limitations, we introduce ARD-SLAM, a groundbreaking approach to dynamic SLAM that innovatively combines global dense optical tracking with sophisticated geometric methodologies. The core innovation of ARD-SLAM lies in its dynamic object identification technique, which harmoniously integrates geometric motion information with prospective motion data. This integration facilitates effective segmentation of moving objects, thereby substantially diminishing their impact on camera ego-motion estimation. ARD-SLAM is further enhanced by an advanced multi-view geometry method that emphasizes the selection of well-matched feature points. This approach is instrumental in efficiently managing dynamic scenarios while also reducing computational load. Rigorous testing on the TUM RGB-D and Bonn RGB-D benchmark datasets has established ARD-SLAM's superiority over established techniques like ORB-SLAM2/3, DynaSLAM, SD-SLAM, DGS-SLAM, and OVD-SLAM. Notably, ARD-SLAM achieves a substantial average reduction in Absolute Trajectory Error (ATE) by 86.1% and in Relative Pose Error (RPE) by 88.0% compared to ORB-SLAM3. The results from the Bonn RGB-D Dataset further underscore ARD-SLAM's effectiveness. Compared to other SLAM methods, ARD-SLAM shows remarkable improvements: 37.8% and 66.4% over DynaSLAM, 41.2% and 73.1% over DGS-SLAM, and 48.9% and 79.7% over OVD-SLAM in ATE and RPE metrics, respectively. This robust performance in dynamically changing environments solidifies ARD-SLAM as a significant advancement in SLAM technology, offering a more precise and adaptable solution for the complex challenges of real-world autonomous navigation.

UDS-SLAM: real-time robust visual SLAM based on semantic segmentation in dynamic scenes

PurposeExisting Simultaneous Localization and Mapping (SLAM) algorithms have been relatively well developed. However, when in complex dynamic environments, the movement of the dynamic points on the dynamic objects in the image in the mapping can have an impact on the observation of the system, and thus there will be biases and errors in the position estimation and the creation of map points. The aim of this paper is to achieve more accurate accuracy in SLAM algorithms compared to traditional methods through semantic approaches.Design/methodology/approachIn this paper, the semantic segmentation of dynamic objects is realized based on U-Net semantic segmentation network, followed by motion consistency detection through motion detection method to determine whether the segmented objects are moving in the current scene or not, and combined with the motion compensation method to eliminate dynamic points and compensate for the current local image, so as to make the system robust.FindingsExperiments comparing the effect of detecting dynamic points and removing outliers are conducted on a dynamic data set of Technische Universität München, and the results show that the absolute trajectory accuracy of this paper's method is significantly improved compared with ORB-SLAM3 and DS-SLAM.Originality/valueIn this paper, in the semantic segmentation network part, the segmentation mask is combined with the method of dynamic point detection, elimination and compensation, which reduces the influence of dynamic objects, thus effectively improving the accuracy of localization in dynamic environments.

A Review of Dynamic Object Filtering in SLAM Based on 3D LiDAR.

SLAM (Simultaneous Localization and Mapping) based on 3D LiDAR (Laser Detection and Ranging) is an expanding field of research with numerous applications in the areas of autonomous driving, mobile robotics, and UAVs (Unmanned Aerial Vehicles). However, in most real-world scenarios, dynamic objects can negatively impact the accuracy and robustness of SLAM. In recent years, the challenge of achieving optimal SLAM performance in dynamic environments has led to the emergence of various research efforts, but there has been relatively little relevant review. This work delves into the development process and current state of SLAM based on 3D LiDAR in dynamic environments. After analyzing the necessity and importance of filtering dynamic objects in SLAM, this paper is developed from two dimensions. At the solution-oriented level, mainstream methods of filtering dynamic targets in 3D point cloud are introduced in detail, such as the ray-tracing-based approach, the visibility-based approach, the segmentation-based approach, and others. Then, at the problem-oriented level, this paper classifies dynamic objects and summarizes the corresponding processing strategies for different categories in the SLAM framework, such as online real-time filtering, post-processing after the mapping, and Long-term SLAM. Finally, the development trends and research directions of dynamic object filtering in SLAM based on 3D LiDAR are discussed and predicted.

The typical AV accident scenarios in the urban area obtained by clustering and association rule mining of real-world accident reports

Automated Vehicles (AVs) based on a collection of advanced technologies such as big data and artificial intelligence have opened an opportunity to reduce traffic accidents caused by human drivers. Nevertheless, traffic accidents of AVs continue to occur, which raises safety and reliability concerns about AVs. AVs are particularly vulnerable to accidents on urban roads than on highways due to various dynamic objects and more complex infrastructure. Several studies proposed a scenario-based approach of experimenting with the response of AVs to specific situations as a way to test their safety. Reliable and concrete scenarios are necessary to test AV safety under critical conditions accurately. This study aims to derive a typical accident scenario for evaluating the safety of AVs, specifically in urban areas, by analysing collisions reported by the DMV of California, USA. We applied a hierarchical clustering method to find groups of similar reports and then executed association rule mining on each cluster to correlate between accident factors and collision types. We combined statistically significant association rules to constitute a total of 14 scenarios that are described according to an adapted PEGASUS framework. The newly obtained scenarios exhibit significantly different accident patterns than the typical Human-driven Vehicles (HVs) in urban areas reported by National Highway Traffic Safety Administration. Our discovery urges AV safety to be tested reliably under scenarios more relevant than the existing HV accident scenarios.

Spatiotemporal image reconstruction to enable high-frame-rate dynamic photoacoustic tomography with rotating-gantry volumetric imagers.

Dynamic photoacoustic computed tomography (PACT) is a valuable imaging technique for monitoring physiological processes. However, current dynamic PACT imaging techniques are often limited to two-dimensional spatial imaging. Although volumetric PACT imagers are commercially available, these systems typically employ a rotating measurement gantry in which the tomographic data are sequentially acquired as opposed to being acquired simultaneously at all views. Because the dynamic object varies during the data-acquisition process, the sequential data-acquisition process poses substantial challenges to image reconstruction associated with data incompleteness. The proposed image reconstruction method is highly significant in that it will address these challenges and enable volumetric dynamic PACT imaging with existing preclinical imagers. The aim of this study is to develop a spatiotemporal image reconstruction (STIR) method for dynamic PACT that can be applied to commercially available volumetric PACT imagers that employ a sequential scanning strategy. The proposed reconstruction method aims to overcome the challenges caused by the limited number of tomographic measurements acquired per frame. A low-rank matrix estimation-based STIR (LRME-STIR) method is proposed to enable dynamic volumetric PACT. The LRME-STIR method leverages the spatiotemporal redundancies in the dynamic object to accurately reconstruct a four-dimensional (4D) spatiotemporal image. The conducted numerical studies substantiate the LRME-STIR method's efficacy in reconstructing 4D dynamic images from tomographic measurements acquired with a rotating measurement gantry. The experimental study demonstrates the method's ability to faithfully recover the flow of a contrast agent with a frame rate of 10 frames per second, even when only a single tomographic measurement per frame is available. The proposed LRME-STIR method offers a promising solution to the challenges faced by enabling 4D dynamic imaging using commercially available volumetric PACT imagers. By enabling accurate STIRs, this method has the potential to significantly advance preclinical research and facilitate the monitoring of critical physiological biomarkers.

Development of Vector Systems Applied Theory for Robotic Technology as an Alternative for Quantum One

The technology is based on the idea of simultaneously processing information on many categories of attributes in the minimized basis of a t-dimensional coordinate system, namely the concept of vector systems theory. The underlying mathematical principle is related to the optimal placement of structural elements in spatially or temporally distributed systems, where the basis is a subset of the set of the total number of coordinate sets of this system, which generates it by the combinational addition of the latter, and the set of the processed vector sets mutually uniquely corresponds to the set of their combinational sums, as well as to the set of binary code combinations. In other words, the mathematical models of the systems with elements distributed in space are studied in such a way that the set of all spatial distances, as well as all their possible combinational sums, mutually uniquely correspond to a set of binary code in the minimized basis of the coordinate system that wraps the surface of a manifold of corresponding dimensions and dimensionality with a common reference point in this system. All algebraic operations are performed by taking into account the numerical values of the modules that correspond to the dimensions, and their quantity, which is determined by the number of dimensions of the coordinate system. The theory includes the development of methods for improving the quality indicators of robotic engineering devices and systems in terms of such indexes as reliability, accuracy, resolution, and other significant characteristics of systems (for example, for constructing specified phase trajectories when controlling dynamic objects in spatial coordinates according to several variable parameters at the same time). The processing of multidimensional data sets on the minimized basis of the t-dimensional coordinate system opens up prospects for development of the vector systems applied theory for robotic technology as an alternative to quantum ones.

What is moving where? Infants' visual attention to dynamic objects may assist with processing of spatial relations.

A central question in infant spatial cognition concerns how infants form abstract categories of spatial relations such as support (on) and containment (in). Prior work suggests two different possibilities regarding the role of attention to objects in infants' formation of abstract categories of spatial relations: Attention to objects may compete with (and thus hamper) attention to the spatial relations between them, or assist with encoding of the spatial relation information. Using eye-tracking, we examined how infants' visual attention to objects related to their successful formation of an abstract category of support relations (i.e., an object on another). Thirty-eight 8-month-old infants' eye movements were recorded during a support categorization task, where infants were habituated to four dynamic events depicting support relations (e.g., resting a block on a box) and then presented with test events that depicted either a support or containment relation with objects that they had seen or not seen in the habituation phase. Based on their looking time to the familiar versus novel spatial relation in the test, infants were classified into two groups: categorizers, who formed an abstract category of a support relation, and non-categorizers, who did not do so. During their initial phase of learning (i.e., the first habituation trial), categorizers paid greater attention to the object moved by a hand (i.e., the dynamic object) in comparison to non-categorizers, whereas their attention to the static object or their gaze shifts between the two objects did not differ. In addition, when presented with novel objects in a novel spatial relation after habituation, only categorizers displayed asymmetric attention between the objects, attending to the dynamic object more than the static object. Gaze shifts and attention to the concave area (i.e., hole) of the container did not differ between categorizers and non-categorizers. These findings suggest that infants' focused attention to an object in motion may play a key role in young infants' spatial category learning, and support the idea that attention to objects can assist with encoding of the spatial relational information.

Semantic geometric fusion multi-object tracking and lidar odometry in dynamic environment

AbstractSimultaneous localization and mapping systems based on rigid scene assumptions cannot achieve reliable positioning and mapping in a complex environment with many moving objects. To solve this problem, this paper proposes a novel dynamic multi-object lidar odometry (MLO) system based on semantic object recognition technology. The proposed system enables the reliable localization of robots and semantic objects and the generation of long-term static maps in complex dynamic scenes. For ego-motion estimation, the proposed system extracts environmental features that take into account both semantic and geometric consistency constraints. Then, the filtered features can be robust to the semantic movable and unknown dynamic objects. In addition, we propose a new least-squares estimator that uses geometric object points and semantic box planes to realize the multi-object tracking (SGF-MOT) task robustly and precisely. In the mapping module, we implement dynamic semantic object detection using the absolute trajectory tracking list. By using static semantic objects and environmental features, the system eliminates accumulated localization errors and produces a purely static map. Experiments on the public KITTI dataset show that the proposed MLO system provides more accurate and robust object tracking performance and better real-time localization accuracy in complex scenes compared to existing technologies.

Integrated triangular fuzzy KE-GRA-TOPSIS method for dynamic ranking of products of customers’ fuzzy Kansei preferences

As society evolves, companies produce more homogeneous products, shifting customers’ needs from functionality to emotions. Therefore, how quickly customers select products that meet their Kansei preferences has become a key concern. However, customer Kansei preferences vary from person to person and are ambiguous and uncertain, posing a challenge. To address this problem, this paper proposes a TF-KE-GRA-TOPSIS method that integrates triangular fuzzy Kansei engineering (TF-KE) with Grey Relational Analysis (GRA) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Firstly, a Kansei evaluation system is constructed based on KE and fuzzy theory. A dynamic triangular fuzzy Kansei preference similarity decision matrix (TF-KPSDM) is defined to quantify customer satisfaction with fuzzy Kansei preferences. Secondly, dynamic objective weights are derived using Criteria Importance Though Intercrieria Correlation (CRITIC) and entropy, optimized through game theory to achieve superior combined weights. Thirdly, the GRA-TOPSIS method utilizes the TF-KPSDM and combined weights to rank products. Finally, taking the case of Kansei preference selection for electric bicycles, results indicate that the proposed method robustly avoids rank reversal and achieves greater accuracy than comparative models. This study can help companies dynamically recommend products to customers based on their Kansei preferences, increasing customer satisfaction and sales.

Investigation of the shapes and sizes of soft molecular aggregates in solution via partial molar volume and dynamic light scattering measurements

Soft molecular aggregates are naturally dynamic and deformable nanoscale objects of various shapes and sizes. However, soft nanoparticle shape can be difficult to measure and often requires advanced instrumentation or methods. Yet, foreknowledge of soft aggregate overall shape is often critically important in many experiments that measure the aggregate’s internal structure and properties. Small-angle neutron and x-ray scattering are often used to measure soft nanoparticle shape. Molecular dynamics simulations can also give an indication of soft molecular shape but, like small-angle scattering methods, requires considerable expense and training. To complement these approaches a more straightforward experimental method of determining soft molecular aggregate shape using a combination of partial molar volume and dynamic light scattering measurements (PMV-DLS) is presented. PMV-DLS is inexpensive, requires modest instrumentation, and can be performed in any laboratory. In this article, the PMV-DLS technique is demonstrated on two separate soft molecular aggregates: AOT reverse micelles and Triton X-100 micelles. The shapes of these aggregates were investigated over a range of solution properties. In both cases, the penetration of continuous phase solvent molecules into the aggregate was found to play an important role in shape determination. AOT reverse micelles were observed to transition from ellipsoidal to spherical as the solution water content increased. Triton X-100 micelle shape was found to be critically dependent upon the amount of water that partitions into the surfactant polyethylene oxide head group region.

GLOCAL: A self-supervised learning framework for global and local motion estimation

Motions in videos are typically a mixture of local dynamic object motions and global camera motion, which are inconsistent in some cases, and even interfere with each other, causing difficulties in various downstream applications, such as video stabilization that requires the global motion, and action recognition that consumes local motions. Therefore, it is crucial to estimate them separately. Existing methods separate two motions from the mixed motion fields, such as optical flow. However, the quality of mixed motion determines the higher bounds of the performance. In this work, we propose a framework, GLOCAL, to directly estimate global and local motions simultaneously from adjacent frames in a self-supervised manner. Our GLOCAL consists of a Global Motion Estimation (GME) module and a Local Motion Estimation (LME) module. The GME module involves a mixed motion estimation backbone, an implicit bottleneck structure for feature dimension reduction, and an explicit bottleneck for global motion recovery based on the global motion bases with foreground mask under the training guidance of proposed global reconstruction loss. An attention U-Net is adopted for LME which produces local motions while excluding motion of irrelevant regions under the guidance of proposed local reconstruction loss. Our method can achieve better performance than the existing methods on the homography estimation dataset DHE and the action recognition dataset NCAA and UCF-101.

Visual simultaneous localization and mapping (vSLAM) algorithm based on improved Vision Transformer semantic segmentation in dynamic scenes

Abstract. Identifying dynamic objects in dynamic scenes remains a challenge for traditional simultaneous localization and mapping (SLAM) algorithms. Additionally, these algorithms are not able to adequately inpaint the culling regions that result from excluding dynamic objects. In light of these challenges, this study proposes a novel visual SLAM (vSLAM) algorithm based on improved Vision Transformer semantic segmentation in dynamic scenes (VTD-SLAM), which leverages an improved Vision Transformer semantic segmentation technique to address these limitations. Specifically, VTD-SLAM utilizes a residual dual-pyramid backbone network to extract dynamic object region features and a multiclass feature transformer segmentation module to enhance the pixel weight of potential dynamic objects and to improve global semantic information for precise identification of potential dynamic objects. The method of multi-view geometry is applied to judge and remove the dynamic objects. Meanwhile, according to static information in the adjacent frames, the optimal nearest-neighbor pixel-matching method is applied to restore the static background, where the feature points are extracted for pose estimation. With validation in the public dataset TUM (The Entrepreneurial University Dataset) and real scenarios, the experimental results show that the root-mean-square error of the algorithm is reduced by 17.1 % compared with dynamic SLAM (DynaSLAM), which shows better map composition capability.

A review of perception sensors, techniques, and hardware architectures for autonomous low-altitude UAVs in non-cooperative local obstacle avoidance

Unmanned Aerial Vehicles (UAVs) can detect and communicate with cooperative obstacles through established protocols. However, non-cooperative obstacles pose a significant threat to UAVs during low-flight operations. These obstacles include static obstacles like buildings, trees, or communication towers and dynamic objects like other UAVs. The application of autonomous UAVs in low-altitude surveillance has motivated research into non-cooperative local obstacle avoidance. This paper provides an overview of such solutions that have been proposed within the last decade. Unlike most literature that limits obstacle avoidance to algorithms, this work provides an in-depth review of obstacle avoidance components, namely the perception sensor, techniques, and hardware architecture of the obstacle avoidance system. This review categorizes the non-cooperative obstacle avoidance techniques into four groups: gap-based methods, geometric methods, repulsive force-based methods, and Artificial Intelligence (AI) based methods. This paper provides a comprehensive resource for researchers working on collision-free surveillance by autonomous UAVs at low altitudes.