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Journal articles on the topic 'Online motion prediction'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Zhang, Daiyong, Xiumin Chu, Chenguang Liu, Zhibo He, Pulin Zhang, and Wenxiang Wu. "A Review on Motion Prediction for Intelligent Ship Navigation." Journal of Marine Science and Engineering 12, no. 1 (2024): 107. http://dx.doi.org/10.3390/jmse12010107.

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In recent years, as intelligent ship-navigation technology has advanced, the challenge of accurately modeling and predicting the dynamic environment and motion status of ships has emerged as a prominent area of research. In response to the diverse time scales required for the prediction of ship motion, various methods for modeling ship navigation environments, ship motion, and ship traffic flow have been explored and analyzed. Additionally, these motion-prediction methods are applied for motion control, collision-avoidance planning, and route optimization. Key issues are summarized regarding s

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Ding, Zhen, Chifu Yang, Zhipeng Wang, Xunfeng Yin, and Feng Jiang. "Online Adaptive Prediction of Human Motion Intention Based on sEMG." Sensors 21, no. 8 (2021): 2882. http://dx.doi.org/10.3390/s21082882.

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Accurate and reliable motion intention perception and prediction are keys to the exoskeleton control system. In this paper, a motion intention prediction algorithm based on sEMG signal is proposed to predict joint angle and heel strike time in advance. To ensure the accuracy and reliability of the prediction algorithm, the proposed method designs the sEMG feature extraction network and the online adaptation network. The feature extraction utilizes the convolution autoencoder network combined with muscle synergy characteristics to get the high-compression sEMG feature to aid motion prediction.

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Xu, Chang-Zhou, and Zao-Jian Zou. "Online Prediction of Ship Roll Motion in Waves Based on Auto-Moving Gird Search-Least Square Support Vector Machine." Mathematical Problems in Engineering 2021 (January 12, 2021): 1–11. http://dx.doi.org/10.1155/2021/2760517.

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A novel method based on auto-moving grid search-least square support vector machine (AGS-LSSVM) is proposed for online predicting ship roll motion in waves. To verify the method, simulation data are used, which are obtained by solving the second-order nonlinear differential equation of ship roll motion using the fourth-order Runge–Kutta method, while the Pierson–Moskowitz spectrum (P–M spectrum) is used to simulate the irregular waves. Combining the sliding time window with the least square support vector machine (LS-SVM), the samples in the time window are used to train the LS-SVM model, and

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Hafeez, Farrukh, Usman Ullah Sheikh, Asif Iqbal, and Muhammad Naveed Aman. "Incoherent and Online Dictionary Learning Algorithm for Motion Prediction." Electronics 11, no. 21 (2022): 3525. http://dx.doi.org/10.3390/electronics11213525.

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Accurate model development and efficient representations of multivariate trajectories are crucial to understanding the behavioral patterns of pedestrian motion. Most of the existing algorithms use offline learning approaches to learn such motion behaviors. However, these approaches cannot take advantage of the streams of data that are available after training has concluded, and typically are not generalizable to data that they have not seen before. To solve this problem, this paper proposes two algorithms for learning incoherent dictionaries in an offline and online manner by extending the off

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Yin, Jian-Chuan, and Ni-Ni Wang. "ONLINE GREY PREDICTION OF SHIP ROLL MOTION USING VARIABLE RBFN." Applied Artificial Intelligence 27, no. 10 (2013): 941–60. http://dx.doi.org/10.1080/08839514.2013.848753.

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Wei, Qian, Peng Su, Lin Zhou, and Wentao Shi. "Online Tracking of Maneuvering Target Trajectory Based on Chaotic Time Series Prediction." Entropy 24, no. 11 (2022): 1668. http://dx.doi.org/10.3390/e24111668.

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Online prediction of maneuvering target trajectory is one of the most popular research directions at present. Specifically, the primary factors balancing, between prediction accuracy and response time, will give the research substance. This paper presents an online trajectory prediction algorithm based on small sample chaotic time series (OTP-SSCT). First, we optimize in terms of data breadth. The dynamic split window is built according to the motion characteristics of the maneuvering target, thus realizing trajectory segmentation and constructing a small sample chaotic time series prediction

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Kim, Wangdo, and Eunice Ortiz. "Algorithmic Implementation of Visually Guided Interceptive Actions: Enhancing Motion Perception in Virtual and Augmented Reality Systems." International Journal of Media and Networks 2, no. 10 (2024): 01–17. https://doi.org/10.33140/ijmn.02.10.03.

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This research introduces an innovative algorithmic framework designed to enhance motion perception and visually guided interceptive actions in virtual and augmented reality (VR/AR) environments. By applying harmonic ratios and stimulation invariants, the proposed algorithms enable real-time prediction of interception points and improve the responsiveness of VR/AR systems. This methodology translates complex theories of visual perception and motion into practical algorithmic solutions, providing dynamic prediction capabilities critical for applications such as online gaming, virtual simulations

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Liu, Yujie, Changhong Dou, Qilu Zhao, and Zongmin Li. "Online Multiple Object Tracking Based on State Prediction and Motion Structure." Journal of Computer-Aided Design & Computer Graphics 30, no. 2 (2018): 289. http://dx.doi.org/10.3724/sp.j.1089.2018.16263.

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Park, Jae Sung, Chonhyon Park, and Dinesh Manocha. "I-Planner: Intention-aware motion planning using learning-based human motion prediction." International Journal of Robotics Research 38, no. 1 (2018): 23–39. http://dx.doi.org/10.1177/0278364918812981.

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We present a motion planning algorithm to compute collision-free and smooth trajectories for high-degree-of-freedom (high-DOF) robots interacting with humans in a shared workspace. Our approach uses offline learning of human actions along with temporal coherence to predict the human actions. Our intention-aware online planning algorithm uses the learned database to compute a reliable trajectory based on the predicted actions. We represent the predicted human motion using a Gaussian distribution and compute tight upper bounds on collision probabilities for safe motion planning. We also describe

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Chong, Yue Linn, Christina Dao Wen Lee, Liushifeng Chen, Chongjiang Shen, Ken Kok Hoe Chan, and Marcelo H. Ang. "Online Obstacle Trajectory Prediction for Autonomous Buses." Machines 10, no. 3 (2022): 202. http://dx.doi.org/10.3390/machines10030202.

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We tackle the problem of achieving real-world autonomous driving for buses, where the task is to perceive nearby obstacles and predict their motion in the time ahead, given current and past information of the object. In this paper, we present the development of a modular pipeline for the long-term prediction of dynamic obstacles’ trajectories for an autonomous bus. The pipeline consists of three main tasks, which are the obstacle detection task, tracking task, and trajectory prediction task. Unlike most of the existing literature that performs experiments in the laboratory, our pipeline’s modu

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Liu, Yanhua, Haiwen Yuan, Zeyu Xiao, and Changshi Xiao. "An Offshore Self-Stabilized System Based on Motion Prediction and Compensation Control." Journal of Marine Science and Engineering 11, no. 4 (2023): 745. http://dx.doi.org/10.3390/jmse11040745.

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The swaying motion of ships can always be generated due to the influence of complex sea conditions. A novel offshore Self-Stabilized system based on motion prediction and compensation control was studied. Firstly, an autoregressive model of ship motion exposed to various sea conditions was established, and the parameters of the model were initialized and updated by offline and online learning historical data. Using the autoregressive model with the acquired parameters, the prediction of the ship’s motion was achieved. Then, a Self-Stabilized system platform composed of six electric cylinders i

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Wang, Juxing, and Linyong Shen. "Semi-Adaptable Human Hand Motion Prediction Based on Neural Networks and Kalman Filter." Journal of Physics: Conference Series 2029, no. 1 (2021): 012091. http://dx.doi.org/10.1088/1742-6596/2029/1/012091.

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Abstract This paper focuses on predicting trajectories of the human hand in order to improve the safety for human-robot interactions. In this work, the position and orientation are represented by two curves in the operation space such that the same algorithm can be used for both position and orientation prediction. The motion prediction is achieved in two steps. Firstly, the neural network (NN) model is applied for offline training to model the human hand motion. Secondly, the Kalman filter is added to adjust the weight coefficients of the NN model’s output layer online when a set of new data

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Lombardo, Elia, Moritz Rabe, Yuqing Xiong, et al. "Offline and online LSTM networks for respiratory motion prediction in MR-guided radiotherapy." Physics in Medicine & Biology 67, no. 9 (2022): 095006. http://dx.doi.org/10.1088/1361-6560/ac60b7.

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Abstract Objective. Gated beam delivery is the current clinical practice for respiratory motion compensation in MR-guided radiotherapy, and further research is ongoing to implement tracking. To manage intra-fractional motion using multileaf collimator tracking the total system latency needs to be accounted for in real-time. In this study, long short-term memory (LSTM) networks were optimized for the prediction of superior–inferior tumor centroid positions extracted from clinically acquired 2D cine MRIs. Approach. We used 88 patients treated at the University Hospital of the LMU Munich for trai

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Yin, Jian-Chuan, Zao-Jian Zou, Feng Xu, and Ni-Ni Wang. "Online ship roll motion prediction based on grey sequential extreme learning machine." Neurocomputing 129 (April 2014): 168–74. http://dx.doi.org/10.1016/j.neucom.2013.09.043.

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Ruan, Dan, and Paul Keall. "Online prediction of respiratory motion: multidimensional processing with low-dimensional feature learning." Physics in Medicine and Biology 55, no. 11 (2010): 3011–25. http://dx.doi.org/10.1088/0031-9155/55/11/002.

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Gao, Nan, Zhenju Chuang, and Ankang Hu. "Online Data-Driven Integrated Prediction Model for Ship Motion Based on Data Augmentation and Filtering Decomposition and Time-Varying Neural Network." Journal of Marine Science and Engineering 12, no. 12 (2024): 2287. https://doi.org/10.3390/jmse12122287.

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Online prediction for ship motion with strong nonlinear characteristics under harsh sea states will significantly reduce the damage of large accidents. Therefore, an integrated ship motion online prediction model consisting of a data augmentation algorithm based on the Improved Temporal Convolutional Network and Time Generative Adversarial Network (ITCN-TGAN), and an Improved Empirical Mode Decomposition (IEMD) and a Time-Varying Neural Network based on Global Time Pattern Attention (GTPA-TNN), is proposed in this article. The results of the validation tests in which the container ship KCS is

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Weidmann, Alexander, Bertram Taetz, Matthias Andres, Felix Laufer, and Gabriele Bleser. "Force Shadows: An Online Method to Estimate and Distribute Vertical Ground Reaction Forces from Kinematic Data." Sensors 20, no. 19 (2020): 5709. http://dx.doi.org/10.3390/s20195709.

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Kinetic models of human motion rely on boundary conditions which are defined by the interaction of the body with its environment. In the simplest case, this interaction is limited to the foot contact with the ground and is given by the so called ground reaction force (GRF). A major challenge in the reconstruction of GRF from kinematic data is the double support phase, referring to the state with multiple ground contacts. In this case, the GRF prediction is not well defined. In this work we present an approach to reconstruct and distribute vertical GRF (vGRF) to each foot separately, using only

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Li, Hongfei, and Maolin Zhang. "Artificial Intelligence and Neural Network-Based Shooting Accuracy Prediction Analysis in Basketball." Mobile Information Systems 2021 (June 3, 2021): 1–11. http://dx.doi.org/10.1155/2021/4485589.

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In order to improve the accuracy of shooting in basketball. A shooting accuracy prediction method based on the convergent improved resource allocating network (CIRAN) online radial basis function neural network (RBFNN) is proposed, and the RBFNN learning algorithm is improved. Through the collection of shooting motion images, feature point extraction, and edge contour feature extraction, the shooting motion trajectory is obtained. Using the online neural network based on the CIRAN learning algorithm to predict the accuracy of shooting, this method analyzes the radial basis function (RBF) netwo

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Casser, Vincent, Soeren Pirk, Reza Mahjourian, and Anelia Angelova. "Depth Prediction without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8001–8. http://dx.doi.org/10.1609/aaai.v33i01.33018001.

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Learning to predict scene depth from RGB inputs is a challenging task both for indoor and outdoor robot navigation. In this work we address unsupervised learning of scene depth and robot ego-motion where supervision is provided by monocular videos, as cameras are the cheapest, least restrictive and most ubiquitous sensor for robotics. Previous work in unsupervised image-to-depth learning has established strong baselines in the domain. We propose a novel approach which produces higher quality results, is able to model moving objects and is shown to transfer across data domains, e.g. from

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Meng, Yao, Xianku Zhang, Guoqing Zhang, Xiufeng Zhang, and Yating Duan. "Sparse Bayesian Relevance Vector Machine Identification Modeling and Its Application to Ship Maneuvering Motion Prediction." Journal of Marine Science and Engineering 11, no. 8 (2023): 1572. http://dx.doi.org/10.3390/jmse11081572.

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In order to establish a sparse and accurate ship motion prediction model, a novel Bayesian probability prediction model based on relevance vector machine (RVM) was proposed for nonparametric modeling. The sparsity, effectiveness, and generalization of RVM were verified from two aspects: (1) the processed Sinc function dataset, and (2) the tank test dataset of the KRISO container ship (KCS) model. The KCS was taken as the main research plant, and the motion prediction models of KCS were obtained. The ε-support vector regression and υ-support vector regression were taken as the compared algorith

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Mounasri, Mrs, V. Ujwala, and R. Gowthami. "Motion Pattern Classification on Online/Active Data-Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 8 (2022): 1013–16. http://dx.doi.org/10.22214/ijraset.2022.45338.

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Abstract: Ship behaviour recognition and prediction is very important for the early warning of risky behaviour, identifying potential ship collision, improving maritime traffic efficiency etc., and thus is a very active topic in the intelligent maritime navigation community. The high flow of vessel traffic affects the difficulty of monitoring vessel in the middle of the sea because of limited human visibility, occurrence of vessel accidents at the sea and other illegal activities that illustrate abnormal vessel behaviour such as oil bunkering, piracy, illegal fishing and other crimes that will

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Biemelt, Patrick, Christopher Link, Sandra Gausemeier, and Ansgar Trächtler. "A Model-Based Online Reference Prediction Strategy for Model Predictive Motion Cueing Algorithms." IFAC-PapersOnLine 53, no. 2 (2020): 6082–88. http://dx.doi.org/10.1016/j.ifacol.2020.12.1681.

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Yang, Bo, Chao Liu, Wenfeng Zheng, and Shan Liu. "Motion prediction via online instantaneous frequency estimation for vision-based beating heart tracking." Information Fusion 35 (May 2017): 58–67. http://dx.doi.org/10.1016/j.inffus.2016.09.004.

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Yue, Wancheng, and Junsheng Ren. "Improved Online Kalman Smoothing Method for Ship Maneuvering Motion Data Using Expectation-Maximization Algorithm." Journal of Marine Science and Engineering 13, no. 6 (2025): 1018. https://doi.org/10.3390/jmse13061018.

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Despite the pivotal role of filtering and smoothing techniques in the preprocessing of ship maneuvering data for robust identification, persistent challenges in reconciling noise suppression with dynamic fidelity preservation have limited algorithmic advancements in recent decades. We propose an online smoothing method enhanced by the Expectation-Maximization (EM) algorithm framework that effectively extracts high-fidelity dynamic features from raw maneuvering data, thereby enhancing the fidelity of subsequent ship identification systems. Our method effectively addresses the challenges posed b

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Hu, Zhenbang, Gedong Jiang, Xuesong Mei, Xialun Yun, and Yun Zhang. "Online Prediction of Milling Inner Hole Roundness Error Based on Accurate SSEM Value Extraction." Shock and Vibration 2019 (August 5, 2019): 1–16. http://dx.doi.org/10.1155/2019/6049316.

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To improve the machining accuracy and production efficiency of precision components with deep hole structures, an online prediction method of the inner hole roundness error, which cannot be directly measured in real time during the machining process, is proposed in this paper. For online prediction of the workpiece roundness error (WRE) during machining, a predictive model based on correlation analysis and a proportional method is proposed according to the spindle synchronous error motion (SSEM) by three-probe method testing. To improve the prediction accuracy of the WRE, a particle swarm opti

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Tang, Chuanbo, Xihua Sheng, Zhuoyuan Li, Haotian Zhang, Li Li, and Dong Liu. "Offline and Online Optical Flow Enhancement for Deep Video Compression." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 6 (2024): 5118–26. http://dx.doi.org/10.1609/aaai.v38i6.28317.

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Video compression relies heavily on exploiting the temporal redundancy between video frames, which is usually achieved by estimating and using the motion information. The motion information is represented as optical flows in most of the existing deep video compression networks. Indeed, these networks often adopt pre-trained optical flow estimation networks for motion estimation. The optical flows, however, may be less suitable for video compression due to the following two factors. First, the optical flow estimation networks were trained to perform inter-frame prediction as accurately as possi

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Zheng, Pu, Pierre-Brice Wieber, Junaid Baber, and Olivier Aycard. "Human Arm Motion Prediction for Collision Avoidance in a Shared Workspace." Sensors 22, no. 18 (2022): 6951. http://dx.doi.org/10.3390/s22186951.

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Industry 4.0 transforms classical industrial systems into more human-centric and digitized systems. Close human–robot collaboration is becoming more frequent, which means security and efficiency issues need to be carefully considered. In this paper, we propose to equip robots with exteroceptive sensors and online motion generation so that the robot is able to perceive and predict human trajectories and react to the motion of the human in order to reduce the occurrence of the collisions. The dataset for training is generated in a real environment in which a human and a robot are sharing their w

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Chen, Lijia, Peiyi Yang, Shigang Li, Kezhong Liu, Kai Wang, and Xinwei Zhou. "Online modeling and prediction of maritime autonomous surface ship maneuvering motion under ocean waves." Ocean Engineering 276 (May 2023): 114183. http://dx.doi.org/10.1016/j.oceaneng.2023.114183.

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Chen, Zening, Xianpeng Che, Lihang Wang, and Lijie Zhang. "Machine learning for ship heave motion prediction: Online adaptive cycle reservoir with regular jumps." Ocean Engineering 294 (February 2024): 116767. http://dx.doi.org/10.1016/j.oceaneng.2024.116767.

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Klinger, T., F. Rottensteiner, and C. Heipke. "A GAUSSIAN PROCESS BASED MULTI-PERSON INTERACTION MODEL." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 3, 2016): 271–77. http://dx.doi.org/10.5194/isprsannals-iii-3-271-2016.

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Online multi-person tracking in image sequences is commonly guided by recursive filters, whose predictive models define the expected positions of future states. When a predictive model deviates too much from the true motion of a pedestrian, which is often the case in crowded scenes due to unpredicted accelerations, the data association is prone to fail. In this paper we propose a novel predictive model on the basis of Gaussian Process Regression. The model takes into account the motion of every tracked pedestrian in the scene and the prediction is executed with respect to the velocities of all

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Klinger, T., F. Rottensteiner, and C. Heipke. "A GAUSSIAN PROCESS BASED MULTI-PERSON INTERACTION MODEL." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 3, 2016): 271–77. http://dx.doi.org/10.5194/isprs-annals-iii-3-271-2016.

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Online multi-person tracking in image sequences is commonly guided by recursive filters, whose predictive models define the expected positions of future states. When a predictive model deviates too much from the true motion of a pedestrian, which is often the case in crowded scenes due to unpredicted accelerations, the data association is prone to fail. In this paper we propose a novel predictive model on the basis of Gaussian Process Regression. The model takes into account the motion of every tracked pedestrian in the scene and the prediction is executed with respect to the velocities of all

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Song, Fei, Yong Li, Wei Cheng, Limeng Dong, Minqi Li, and Junfang Li. "An Improved Kalman Filter Based on Long Short-Memory Recurrent Neural Network for Nonlinear Radar Target Tracking." Wireless Communications and Mobile Computing 2022 (August 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/8280428.

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The target tracking of nonlinear maneuvering radar in dense clutter environments is still an important but difficult problem to be solved effectively. Traditional solutions often rely on motion models and prior distributions. This paper presents a novel improved architecture of Kalman filter based on a recursive neural network, which combines the sequence learning of recurrent neural networks with the precise prediction of Kalman filter in an end-to-end manner. We employ three LSTM networks to model nonlinear motion equation, motion noise, and measurement noise, respectively, and learn their l

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Zhu, Guozhu, and Weirong Hong. "Autonomous Vehicle Motion Control Considering Path Preview with Adaptive Tire Cornering Stiffness Under High-Speed Conditions." World Electric Vehicle Journal 15, no. 12 (2024): 580. https://doi.org/10.3390/wevj15120580.

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The field of autonomous vehicle technology has experienced remarkable growth. A pivotal trend in this development is the enhancement of tracking performance and stability under high-speed conditions. Model predictive control (MPC), as a prevalent motion control method, necessitates an extended prediction horizon as vehicle speed increases and will lead to heightened online computational demands. To address this, a path preview strategy is integrated into the MPC framework that temporarily freezes the vehicle state within the prediction horizon. This approach assumes that the vehicle state will

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Cimarelli, Claudio, Hriday Bavle, Jose Luis Sanchez-Lopez, and Holger Voos. "RAUM-VO: Rotational Adjusted Unsupervised Monocular Visual Odometry." Sensors 22, no. 7 (2022): 2651. http://dx.doi.org/10.3390/s22072651.

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Unsupervised learning for monocular camera motion and 3D scene understanding has gained popularity over traditional methods, which rely on epipolar geometry or non-linear optimization. Notably, deep learning can overcome many issues of monocular vision, such as perceptual aliasing, low-textured areas, scale drift, and degenerate motions. In addition, concerning supervised learning, we can fully leverage video stream data without the need for depth or motion labels. However, in this work, we note that rotational motion can limit the accuracy of the unsupervised pose networks more than the trans

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Zhang, Bing, Changfu Zong, Guoying Chen, and Guiyuan Li. "An adaptive-prediction-horizon model prediction control for path tracking in a four-wheel independent control electric vehicle." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 233, no. 12 (2019): 3246–62. http://dx.doi.org/10.1177/0954407018821527.

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An adaptive-prediction-horizon model prediction control-based path tracking controller for a four-wheel independent control electric vehicle is designed. Unlike traditional model prediction control with fixed prediction horizon, this paper devotes to satisfy the varied path tracking demand by adjusting online the prediction horizon of model prediction control according to its effect on vehicle dynamic characteristics. Vehicle dynamic stability quantized with the vehicle sideslip-feature phase plane is preferentially considered in the prediction horizon adjustment. For stability during switchin

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Rong, Zhiming, Yuxiong Li, Li Wu, Chong Zhang, and Jialin Li. "An Advanced Tool Wear Forecasting Technique with Uncertainty Quantification Using Bayesian Inference and Support Vector Regression." Sensors 24, no. 11 (2024): 3394. http://dx.doi.org/10.3390/s24113394.

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Tool wear prediction is of great significance in industrial production. Current tool wear prediction methods mainly rely on the indirect estimation of machine learning, which focuses more on estimating the current tool wear state and lacks effective quantification of random uncertainty factors. To overcome these shortcomings, this paper proposes a novel method for predicting cutting tool wear. In the offline phase, the multiple degradation features were modeled using the Brownian motion stochastic process and a SVR model was trained for mapping the features and the tool wear values. In the onl

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Tao, Cancan, and Bowen Liu. "Online Autonomous Motion Control of Communication-Relay UAV with Channel Prediction in Dynamic Urban Environments." Drones 8, no. 12 (2024): 771. https://doi.org/10.3390/drones8120771.

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In order to improve the network performance of multi-unmanned ground vehicle (UGV) systems in urban environments, this article proposes a novel online autonomous motion-control method for the relay UAV. The problem is solved by jointly considering unknown RF channel parameters, unknown multi-agent mobility, the impact of the environments on channel characteristics, and the unavailable angle-of-arrival (AoA) information of the received signal, making the solution of the problem more practical and comprehensive. The method mainly consists of two parts: wireless channel parameter estimation and o

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Zhang, Teng, Hao Sun, Fangyu Peng, Xiaowei Tang, Rong Yan, and Runpeng Deng. "An online prediction and compensation method for robot position errors embedded with error-motion correlation." Measurement 234 (July 2024): 114866. http://dx.doi.org/10.1016/j.measurement.2024.114866.

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Faroni, Marco, Lo Bianco Corrado Guarino, Manuel Beschi, and Antonio Visioli. "Predictive joint trajectory scaling for manipulators with kinodynamic constraints." Control Engineering Practice 95, February 2020 (2019): 104264. https://doi.org/10.1016/j.conengprac.2019.104264.

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Trajectory scaling techniques adapt online the robot timing law to preserve the desired geometric path when the desired motion does not respect the robot limits. State-of-the-art non-predictive methods typically provide far-from-optimal solutions, while high computational burdens are the main bottleneck for the implementation of receding horizon strategies. This paper proposes a predictive approach to trajectory scaling subject to joint velocity, acceleration, and torque limitations. Computational complexity is dramatically reduced by means of the parametrization of inputs and outputs and the

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Li, Gang, and Guoheng Ren. "Wearable Power Assistant Robot Sensor Signal Prediction Algorithm and Controller Design." Applied Bionics and Biomechanics 2022 (May 14, 2022): 1–11. http://dx.doi.org/10.1155/2022/4605389.

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The wearable power-assisted robot is a typical auxiliary rehabilitation robot. It is an exoskeleton power-assisted device that helps people to expand their lower limb movement capabilities. Its basic principle is to obtain the motion intention information of the human body through the perception system. Control the DC servo motor installed at the hip joint and the knee joint to drive the movement of the link, so as to achieve the purpose of providing assistance to the human body. In order to improve the dynamic response frequency of the wearable robotic perception system, a sensor signal based

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Serevina, Vina, and Raida Raida. "IMPROVING THE QUALITY OF EDUCATION IN THE COVID-19 ERA THROUGH THE IMPLEMENTATION OF ONLINE LEARNING RESOURCES WITH POE2WE MODEL ON PARABOLIC MOTION." International Journal of Educational Management and Innovation 2, no. 1 (2021): 13. http://dx.doi.org/10.12928/ijemi.v2i1.2976.

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The Covid-19 pandemic has affected all aspects of life, also education. Educators worldwide have had to adapt the situation by developing online learning models to keep education running. The present study aims at developing online learning resources using websites and the POE2WE model of parabolic motion material. The POE2WE model was represented as six stages of online learning that includes Prediction, Observation, Explanation, Elaboration, Write, and Evaluation. 30 students of 10th grade of a high school in Indonesia participated in this study. The ADDIE model (analyzing, designing, develo

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Meng, Lin, Jun Pang, Ziyao Wang, Rui Xu, and Dong Ming. "The Role of Surface Electromyography in Data Fusion with Inertial Sensors to Enhance Locomotion Recognition and Prediction." Sensors 21, no. 18 (2021): 6291. http://dx.doi.org/10.3390/s21186291.

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Locomotion recognition and prediction is essential for real-time human–machine interactive control. The integration of electromyography (EMG) with mechanical sensors could improve the performance of locomotion recognition. However, the potential of EMG in motion prediction is rarely discussed. This paper firstly investigated the effect of surface EMG on the prediction of locomotion while integrated with inertial data. We collected EMG signals of lower limb muscle groups and linear acceleration data of lower limb segments from ten healthy participants in seven locomotion activities. Classificat

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Li, Xin, and Dawei Li. "GPFS: A Graph-based Human Pose Forecasting System for Smart Home with Online Learning." ACM Transactions on Sensor Networks 17, no. 3 (2021): 1–19. http://dx.doi.org/10.1145/3460199.

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Forecasting human poses given a sequence of historical pose frames has several important applications, especially in the domain of smart home safety. Recently, computer vision-based human pose forecasting has made a breakthrough using deep learning technology. However, to implement a practical system deployed on an IoT edge environment, there are still two issues to be addressed. First, existing methods on pose forecasting fail to model the coherent structural information of connected human joints and thus cannot achieve satisfactory prediction accuracy, especially for long-term predictions. S

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Song, Fei, Yong Li, Wei Cheng, and Limeng Dong. "Learning to Track Multiple Radar Targets with Long Short-Term Memory Networks." Wireless Communications and Mobile Computing 2023 (February 15, 2023): 1–9. http://dx.doi.org/10.1155/2023/1033371.

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Radar multitarget tracking in a dense clutter environment remains a complex problem to be solved. Most existing solutions still rely on complex motion models and prior distribution knowledge. In this paper, a new online tracking method based on a long short-term memory (LSTM) network is proposed. It combines state prediction, measurement association, and trajectory management functions in an end-to-end manner. We employ LSTM networks to model target motion and trajectory associations, relying on their strong learning ability to learn target motion properties and long-term dependence of traject

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Zheng, Hongqing, Wujin Deng, Wanqing Song, Wei Cheng, Piercarlo Cattani, and Francesco Villecco. "Remaining Useful Life Prediction of a Planetary Gearbox Based on Meta Representation Learning and Adaptive Fractional Generalized Pareto Motion." Fractal and Fractional 8, no. 1 (2023): 14. http://dx.doi.org/10.3390/fractalfract8010014.

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The remaining useful life (RUL) prediction of wind turbine planetary gearboxes is crucial for the reliable operation of new energy power systems. However, the interpretability of the current RUL prediction models is not satisfactory. To this end, a multi-stage RUL prediction model is proposed in this work, with an interpretable metric-based feature selection algorithm. In the proposed model, the advantages of neural networks and long-range-dependent stochastic processes are combined. In the offline training stage, a general representation of the degradation trend is learned with the meta-long

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Du, Jin Song, and Xin Bi. "An Adaptive Interacting Multiple Model for Vehicle Target Tracking Method." Advanced Materials Research 718-720 (July 2013): 1286–89. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.1286.

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In the field of traffic safety vehicle target tracking prediction as the background, this paper proposes an adaptive interacting multiple model tracking algorithm. According to the field of transportation vehicle movement state characteristics, based on the uniform (CV) and uniformly accelerated motion (CA) model, based on new information structure model of motion of the likelihood function, online adaptive adjustment model of the noise variance and the Markov matrix, realization of maneuvering target movement model and model set adaptation, not only improved IMM algorithm for tracking accurac

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Huang, Bai-Gang, Zao-Jian Zou, and Wei-Wei Ding. "Online prediction of ship roll motion based on a coarse and fine tuning fixed grid wavelet network." Ocean Engineering 160 (July 2018): 425–37. http://dx.doi.org/10.1016/j.oceaneng.2018.04.065.

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Liu, Xixiang, Qiming Wang, Rong Huang, Songbing Wang, and Xianjun Liu. "A prediction method for deck-motion based on online least square support vector machine and genetic algorithm." Journal of Marine Science and Technology 24, no. 2 (2018): 382–97. http://dx.doi.org/10.1007/s00773-018-0557-z.

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Wang, Pengyuan, Pinle Qin, Rui Chai, et al. "End-to-End Online Video Stitching and Stabilization Method Based on Unsupervised Deep Learning." Applied Sciences 15, no. 11 (2025): 5987. https://doi.org/10.3390/app15115987.

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The limited field of view, cumulative inter-frame jitter, and dynamic parallax interference in handheld video stitching often lead to misalignment and distortion. In this paper, we propose an end-to-end, unsupervised deep-learning framework that jointly performs real-time video stabilization and stitching. First, collaborative optimization architecture allows the stabilization and stitching modules to share parameters and propagate errors through a fully differentiable network, ensuring consistent image alignment. Second, a Markov trajectory smoothing strategy in relative coordinates models in

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Ashraf, Adnan, Amin Majd, and Elena Troubitsyna. "Online Path Generation and Navigation for Swarms of UAVs." Scientific Programming 2020 (January 11, 2020): 1–14. http://dx.doi.org/10.1155/2020/8530763.

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With the growing popularity of unmanned aerial vehicles (UAVs) for consumer applications, the number of accidents involving UAVs is also increasing rapidly. Therefore, motion safety of UAVs has become a prime concern for UAV operators. For a swarm of UAVs, a safe operation cannot be guaranteed without preventing the UAVs from colliding with one another and with static and dynamically appearing, moving obstacles in the flying zone. In this paper, we present an online, collision-free path generation and navigation system for swarms of UAVs. The proposed system uses geographical locations of the

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