Relevant bibliographies by topics / Online motion prediction

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Online motion prediction'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Online motion prediction"

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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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Dissertations / Theses on the topic "Online motion prediction"

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Edvardsson, Andreas, and Lucas Grönlund. "Online Predictions of Human Motion." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210845.

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Collaboration between humans and robots is becoming an increasingly commonoccurrence in both industry and homes, more so with every forthcomingtechnological advance. This paper examines the possibilities of performinghuman hand movement predictions on the fly, e.g. by only using informationup to the specific moment in time of which the prediction is carried out.Specifically, data will be collected using a Kinect (v.1).The model used for the predictor developed is the Minimum Jerk model,which states that certain multi-joint reaching movements are planned in sucha way that the hand is to follow
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Garcia, Lopez Felipe [Verfasser], and Alexander [Akademischer Betreuer] Verl. "Predictive and cooperative online motion planning : a contribution to networked mobile robot navigation in industrial applications / Felipe Garcia Lopez ; Betreuer: Alexander Verl." Stuttgart : Universitätsbibliothek der Universität Stuttgart, 2019. http://d-nb.info/120406119X/34.

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Vestin, Albin, and Gustav Strandberg. "Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms." Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160020.

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Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, i
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Books on the topic "Online motion prediction"

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Vaez-Zadeh, Sadegh. Introduction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198742968.003.0001.

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An overview of permanent magnet synchronous (PMS) motors and the related control system are presented in this chapter as introductory materials for the rest of the book. The interconnections of the control system to the power electronic inverter and the motor are emphasized. In addition, the major parts of the system are overviewed. Pulse width-modulated voltage source inverter, as the most commonly used power converter in PMS motor drives, is briefly discussed. PMS motors configurations and operating principles are also presented after considering characteristics of permanent magnet materials
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Book chapters on the topic "Online motion prediction"

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Lau, Rynson W. H., and Addison Chan. "Motion Prediction for Online Gaming." In Motion in Games. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89220-5_11.

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Huisman, Mischa, and Erjen Lefeber. "Online Motion Planning for All-Wheel Drive Autonomous Race Cars." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_27.

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AbstractThe advent of autonomous racing events, such as Formula Student Driverless Cup, requires online motion planning algorithms that push the vehicle to its limits while ensuring vehicle stability and preventing road departure. A popular method to find the optimal control input to drive at the limits of the car is Nonlinear Model Predictive Control (NMPC). However, when NMPC is used, often a trade-off has to be made between performance, accuracy, and computational complexity. In this manuscript, the principle of cascading different vehicle models is used to construct the prediction horizon.
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Scheffe, Patrick, Matheus V. A. Pedrosa, Kathrin Flaßkamp, and Bassam Alrifaee. "Prioritized Trajectory Planning for Networked Vehicles Using Motion Primitives." In Cooperatively Interacting Vehicles. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-60494-2_9.

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AbstractThe computation time required to solve nonconvex, nonlinear optimization problems increases rapidly with their size. This poses a challenge in trajectory planning for multiple networked vehicles with collision avoidance. In the centralized formulation, the optimization problem size increases with the number of vehicles in the networked control system (NCS), rendering the formulation unusable for experiments. We investigate two methods to decrease the complexity of networked trajectory planning. First, we approximate the optimization problem by discretizing the vehicle dynamics with an
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Gafur, Nigora. "3 Online motion control for a single manipulator." In Optimal task planning and predictive online motion control for a multi-manipulator system. VDI Verlag, 2025. https://doi.org/10.51202/9783186281081-33.

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Gafur, Nigora. "4 Online motion control for a multi-manipulator system." In Optimal task planning and predictive online motion control for a multi-manipulator system. VDI Verlag, 2025. https://doi.org/10.51202/9783186281081-57.

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Rajamohana S. P., Dharani A., Anushree P., Santhiya B., and Umamaheswari K. "Machine Learning Techniques for Healthcare Applications." In Advances in Social Networking and Online Communities. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7522-1.ch012.

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Autism spectrum disorder (ASD) is one of the common disorders in brain. Early detection of ASD improves the overall mental health, which is very important for the future of the child. ASD affects social coordination, emotions, and motor activity of an individual. This is due to the difficulties in getting self-evaluation results and expressive experiences. In the first case study in this chapter, an efficient method to automatically detect the expressive states of individuals with the help of physiological signals is explored. In the second case study of the chapter, the authors explore breast
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Lu, Guolin, Shihuan Zhang, and Jinhong Jiang. "Local Motion Planning Subject to Constrained State for Quadrotor with Model Predictive Control." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde221165.

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As for the challenges of local motion planning subject to constrained state for quadrotor in evaluating the health status of photovoltaic power generation in complex environment, including fast replanning, limited onboard computational resources, a practical framework for constructing motion trajectory from the bounded states is proposed in this work. The references and nominal inputs are made by the scheme of receding horizon fashion of model predictive control (MPC) by means of gradient-based method in the continuous domain. In order to avoid the complex calculation of the optimal gradient,
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Gafur, Nigora. "Appendix." In Optimal task planning and predictive online motion control for a multi-manipulator system. VDI Verlag, 2025. https://doi.org/10.51202/9783186281081-137.

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Gafur, Nigora. "7 Conclusion and Outlook." In Optimal task planning and predictive online motion control for a multi-manipulator system. VDI Verlag, 2025. https://doi.org/10.51202/9783186281081-131.

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Gafur, Nigora. "Titelei/Inhaltsverzeichnis." In Optimal task planning and predictive online motion control for a multi-manipulator system. VDI Verlag, 2025. https://doi.org/10.51202/9783186281081-i.

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Conference papers on the topic "Online motion prediction"

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Sun, Haoxiang, and Hanwen Song. "Online Predictive Motion Control of Redundant Robots via Model Predictive Control and Full Null Space Parameterization." In 2024 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2024. https://doi.org/10.1109/robio64047.2024.10907580.

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Kang, Hyungsoo, Christoph Aoun, Isaac Kaminer, and Naira Hovakimyan. "Online Defensive Motion Planning Against Adversarial Swarm Attacks using Bernstein Polynomials-Based Model Predictive Control." In 2025 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2025. https://doi.org/10.1109/icuas65942.2025.11007829.

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Yamada, Takashi, and Matthew Howard. "Online Parameter Estimation of Hydraulic System Based on Stochastic Gradient Descent." In BATH/ASME 2020 Symposium on Fluid Power and Motion Control. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/fpmc2020-2765.

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Abstract In this paper, offline and online parameter estimation methods for hydraulic systems based on stochastic gradient descent are presented. In contrast to conventional approaches, the proposed methods can estimate any parameter in mathematical models based on multi-step prediction error. These advantages are achieved by calculating the gradient of the multi-step error against the estimated parameters using Lagrange multipliers and the calculus of variations, and by forming differentiable models of hydraulic systems. In experiments on a physical hydraulic system, the proposed methods with
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Luo, Ruikun, and Dmitry Berenson. "A framework for unsupervised online human reaching motion recognition and early prediction." In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2015. http://dx.doi.org/10.1109/iros.2015.7353706.

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Ali, Usman, Hong Cai, Yasamin Mostofi, and Yorai Wardi. "Motion and communication co-optimization with path planning and online channel prediction." In 2016 American Control Conference (ACC). IEEE, 2016. http://dx.doi.org/10.1109/acc.2016.7526789.

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Zhang, Jinsong, Zhaoyang Jin, Haihui Ye, and Feng Liu. "Online dynamic magnetic resonance imaging based on an improved motion prediction scheme." In 2014 7th International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2014. http://dx.doi.org/10.1109/bmei.2014.7002745.

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Ahopelto, Miika, and Kalevi Huhtala. "Prediction of Drive Torque in Hydraulic Wheel Loader." In ASME/BATH 2014 Symposium on Fluid Power and Motion Control. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/fpmc2014-7809.

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This research presents method for the online estimation and prediction of drive forces in heavy duty vehicles. The estimation is based on the measured velocity and longitudinal dynamics. The same method can be applied for the prediction but the reference velocity is given as the input of the system instead of actual velocity. The prediction and estimation of drive forces are essential when optimizing the fuel consumption or emission. Especially, the control of the diesel engine requires information about loading torque for the prevent stalling of engine and guarantee rapid reaction of the mach
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Sivanagaraja, Tatinati, and Kalyana C. Veluvolu. "Respiratory motion prediction using moving window based online training approach for LS-SVM." In 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2015. http://dx.doi.org/10.1109/spin.2015.7095297.

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Butepage, Judith, Hedvig Kjellstrom, and Danica Kragic. "Anticipating Many Futures: Online Human Motion Prediction and Generation for Human-Robot Interaction." In 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018. http://dx.doi.org/10.1109/icra.2018.8460651.

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Lu, Yanyan, Zhonghua Xi, and Jyh-Ming Lien. "Collision Prediction: Conservative Advancement Among Obstacles With Unknown Motion." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-35561.

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Collision detection is a fundamental geometric tool for sampling-based motion planners. On the contrary, collision prediction for the scenarios that obstacle’s motion is unknown is still in its infancy. This paper proposes a new approach to predict collision by assuming that obstacles are adversarial. Our new tool advances collision prediction beyond the translational and disc robots; arbitrary polygons with rotation can be used to better represent obstacles and provide tighter bound on predicted collision time. Comparing to an online motion planner that replans periodically at fixed time inte
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