Motion Prediction Algorithm Research Articles

Proximal Policy-Optimized Regularized Least Squares Algorithm for Noise-Resilient Motion Prediction of UMVs

Proximal Policy-Optimized Regularized Least Squares Algorithm for Noise-Resilient Motion Prediction of UMVs

Model experimental studies on active heave compensation control strategy for electric-driven offshore cranes

Ship-mounted heave compensation offshore cranes are indispensable for isolating connected payloads from the support vessel during lifting operations under harsh sea conditions. In this paper, an innovative adaptive robust control strategy is presented for the electric-driven active heave compensation (EDAHC) system, combining an equivalent model predictive control (EMPC) method with a bias proportional integral derivative (BPID) framework to effectively mitigate the adverse effects of wave-induced heave motions from the support vessel on the suspended payload. Building upon the inherent field-oriented control in the permanent magnet synchronous motor (PMSM), the BPID-based control structure is introduced, motivated by its prompt responsiveness and robust resistance against model discrepancies and irregular disturbances. Facilitated by a torque compensation mechanism, the EMPC-based control scheme, synthesized with an autoregressive integrated moving average (ARIMA)-based heave motion prediction algorithm, is subsequently developed to achieve nonlinear friction deadzone correction and adaptive regulation of BPID parameters, thereby ensuring optimal performance of the EDAHC system within specified state and input constraints. Comparative experimental tests conducted on a scaled EDAHC testbed validate the superior capabilities of the proposal in station-keeping, position tracking, constraint satisfaction, and robustness against parametric uncertainties.

A dynamic target tracking framework of UGV for UAV recovery under random disturbances

PurposeDynamically tracking the target by unmanned ground vehicles (UGVs) plays a critical role in mobile drone recovery. This study aims to solve this challenge under diverse random disturbances, proposing a dynamic target tracking framework for UGVs based on target state estimation, trajectory prediction, and UGV control.Design/methodology/approachTo mitigate the adverse effects of noise contamination in target detection, the authors use the extended Kalman filter (EKF) to improve the accuracy of locating unmanned aerial vehicles (UAVs). Furthermore, a robust motion prediction algorithm based on polynomial fitting is developed to reduce the impact of trajectory jitter caused by crosswinds, enhancing the stability of drone trajectory prediction. Regarding UGV control, a dynamic vehicle model featuring independent front and rear wheel steering is derived. Additionally, a linear time-varying model predictive control algorithm is proposed to minimize tracking errors for the UGV.FindingsTo validate the feasibility of the framework, the algorithms were deployed on the designed UGV. Experimental results demonstrate the effectiveness of the proposed dynamic tracking algorithm of UGV under random disturbances.Originality/valueThis paper proposes a tracking framework of UGV based on target state estimation, trajectory prediction and UGV predictive control, enabling the system to achieve dynamic tracking to the UAV under multiple disturbance conditions.

73‐1: Implementation of Eye‐Tracking Technology Based on Vehicle Glasses‐Free 3D Prism Display

With the continuous development of in‐car display applications, the demand for in‐car glasses‐free 3D display screens is gradually increasing. This article adopts a high‐resolution 4K display screen based on prism technology， and is equipped with a human eye tracking system implemented using a regular monocular camera. This configuration enables the in‐car instrument display to provide a high‐quality glasses‐free 3D display effect within a large visual range, addressing the limited viewing area issue associated with traditional glasses‐free 3D displays. Due to constraints in camera hardware conditions, we introduced a motion compensation and motion prediction algorithm. Through experimental validation, this algorithm significantly enhances the performance of human eye tracking using a regular camera. A comparison with traditional eye‐tracking methods demonstrates the substantial advantages of this algorithm in improving the performance of in‐car 3D display technology.

3D object tracking using integral imaging with mutual information and Bayesian optimization

Integral imaging has proven useful for three-dimensional (3D) object visualization in adverse environmental conditions such as partial occlusion and low light. This paper considers the problem of 3D object tracking. Two-dimensional (2D) object tracking within a scene is an active research area. Several recent algorithms use object detection methods to obtain 2D bounding boxes around objects of interest in each frame. Then, one bounding box can be selected out of many for each object of interest using motion prediction algorithms. Many of these algorithms rely on images obtained using traditional 2D imaging systems. A growing literature demonstrates the advantage of using 3D integral imaging instead of traditional 2D imaging for object detection and visualization in adverse environmental conditions. Integral imaging’s depth sectioning ability has also proven beneficial for object detection and visualization. Integral imaging captures an object’s depth in addition to its 2D spatial position in each frame. A recent study uses integral imaging for the 3D reconstruction of the scene for object classification and utilizes the mutual information between the object’s bounding box in this 3D reconstructed scene and the 2D central perspective to achieve passive depth estimation. We build over this method by using Bayesian optimization to track the object’s depth in as few 3D reconstructions as possible. We study the performance of our approach on laboratory scenes with occluded objects moving in 3D and show that the proposed approach outperforms 2D object tracking. In our experimental setup, mutual information-based depth estimation with Bayesian optimization achieves depth tracking with as few as two 3D reconstructions per frame which corresponds to the theoretical minimum number of 3D reconstructions required for depth estimation. To the best of our knowledge, this is the first report on 3D object tracking using the proposed approach.

NMPC-PID control of secondary regulated active heave compensation system for offshore crane

In this paper, an offshore crane driven by a secondary controlled unit (SCU) which offers advantages in terms of high dynamic behavior and energy efficiency is investigated. To counteract the negative impact of the support vessel's heave motion on the station-keeping and trajectory tracking performance of heavy payload, an adaptive robust control scheme for the secondary regulated active heave compensation (AHC) system is proposed based on the combination of a nonlinear model predictive control (NMPC) strategy, a conventional proportional-integral-derivative (PID) control scheme and a vessel heave motion prediction algorithm. The motivation of utilizing the PID-based control structure is given by the excellent resistance against irregular external disturbances and robust capability to eliminate the uncertainties, with real-time tuning gains provided by the NMPC synthesized with the autoregressive integrated moving average (ARIMA) model to ensure optimal evolution that satisfies state and input constraints while accounting for model nonlinearities. Comparative simulation studies in the practical Simulink-based mechanical-electrical-hydraulic research system demonstrate the superior tracking and heave compensation performance, strong robustness, constraint satisfaction, and energy efficiency of the proposal.

GPU-Accelerated Interaction-Aware Motion Prediction

Before their massive deployment, autonomous vehicles need to prove in complex scenarios such that they can reach human driving proficiency while guaranteeing higher safety levels. One of the most important human traits to negotiating traffic is the ability to predict the future behavior of surrounding vehicles as a basis for agile and safe navigation. This capability is particularly challenging for an autonomous system in highly interactive driving situations, such as intersections or roundabouts. In this paper, a set of techniques to bring a computationally expensive state-of-the-art motion prediction algorithm to real-time execution are presented with the goal of meeting a standard motion-planning algorithm execution frequency of 5 Hz, which is the primary consumer of motion predictions. This is achieved by applying novel and existing parallelization algorithms that take advantage of graphic processing units (GPUs) through the compute unified device architecture (CUDA) programming language and managing to produce an average 5× speedup over raw C++ in the cases studied. The optimizations are then evaluated in public datasets and a real vehicle on a test track.

Energy-Efficient Relay Tracking and Predicting Movement Patterns with Multiple Mobile Camera Sensors

Camera sensor networks (CSN) have been widely used in different applications such as large building monitoring, social security, and target tracking. With advances in visual and actuator sensor technology in the last few years, deploying mobile cameras in CSN has become a possible and efficient solution for many CSN applications. However, mobile camera sensor networks still face several issues, such as limited sensing range, the optimal deployment of camera sensors, and the energy consumption of the camera sensors. Therefore, mobile cameras should cooperate in order to improve the overall performance in terms of enhancing the tracking quality, reducing the moving distance, and reducing the energy consumed. In this paper, we propose a movement prediction algorithm to trace the moving object based on a cooperative relay tracking mechanism. In the proposed approach, the future path of the target is predicted using a pattern recognition algorithm by applying data mining to the past movement records of the target. The efficiency of the proposed algorithms is validated and compared with another related algorithm. Simulation results have shown that the proposed algorithm guarantees the continuous tracking of the object, and its performance outperforms the other algorithms in terms of reducing the total moving distance of cameras and reducing energy consumption levels. For example, in terms of the total moving distance of the cameras, the proposed approach reduces the distance by 4.6% to 15.2% compared with the other protocols that do not use prediction.

A Novel Short-Term Ship Motion Prediction Algorithm Based on EMD and Adaptive PSO–LSTM with the Sliding Window Approach

Under the influence of variable sea conditions, a ship will have an oscillating motion comprising six degrees of freedom, all of which are connected to each other. Among these degrees of freedom, rolling and pitching motions have a severe impact on a ship’s maritime operations. An accurate and effective ship motion attitude prediction method that makes the prediction in a short period of time is required to guarantee the safety and stability of the ship’s maritime operations. Traditional methods are based on time domain analysis, such as the autoregressive moving average (ARMA) models. However, these models have limitations when it comes to predicting the nonlinear and nonstationary characteristics of real ship motion attitude data. Many intelligent algorithms continue to be applied in nonlinear and nonstationary ship attitude prediction, such as extreme learning machines (ELMs) and the long short-term memory (LSTM) neural network, as well as other deep learning methods, showing promising results. By using the sliding window approach, the time-varying dynamic characteristics of the ship’s motion attitude can be preserved better. The simulation results demonstrate that the proposed model performs well in terms of predicting the nonlinear and nonstationary ship motion attitude.

Vehicle Motion Prediction Algorithm Based on Artificial Potential Field Correction and Fuzzy C-Mean Driving Intention Classification

Predicting the trajectories of surrounding vehicles plays an important role in the driving safety of autonomous vehicles. It impacts the decision making, path planning, and vehicle motion control process in autonomous vehicles. However, due to the uncertainty of vehicle dynamics, driving intention, and the complexity of the surrounding environment, there are interactions between vehicles and other issues, and their motion prediction faces great challenges. This paper proposes a trajectory prediction algorithm combining driving intention classification and environmental interaction correction to overcome the leading vehicle movement prediction problem. In order to solve the problems of uncertainty in predicting vehicle driving intention and nonlinearity between future vehicle movements and the environment, a driving intention recognition based on the Fuzzy C-mean algorithm and a forward vehicle motion prediction algorithm combining multi-model prediction results are proposed. The artificial potential field method is also used to model vehicle interaction and correct the trajectory prediction results. Finally, the real vehicle data validation proves that this algorithm has high prediction accuracy.

Measuring motion-to-photon latency for sensorimotor experiments with virtual reality systems

Consumer virtual reality (VR) systems are increasingly being deployed in research to study sensorimotor behaviors, but properties of such systems require verification before being used as scientific tools. The ‘motion-to-photon’ latency (the lag between a user making a movement and the movement being displayed within the display) is a particularly important metric as temporal delays can degrade sensorimotor performance. Extant approaches to quantifying this measure have involved the use of bespoke software and hardware and produce a single measure of latency and ignore the effect of the motion prediction algorithms used in modern VR systems. This reduces confidence in the generalizability of the results. We developed a novel, system-independent, high-speed camera-based latency measurement technique to co-register real and virtual controller movements, allowing assessment of how latencies change through a movement. We applied this technique to measure the motion-to-photon latency of controller movements in the HTC Vive, Oculus Rift, Oculus Rift S, and Valve Index, using the Unity game engine and SteamVR. For the start of a sudden movement, all measured headsets had mean latencies between 21 and 42 ms. Once motion prediction could account for the inherent delays, the latency was functionally reduced to 2–13 ms, and our technique revealed that this reduction occurs within ~25–58 ms of movement onset. Our findings indicate that sudden accelerations (e.g., movement onset, impacts, and direction changes) will increase latencies and lower spatial accuracy. Our technique allows researchers to measure these factors and determine the impact on their experimental design before collecting sensorimotor data from VR systems.

Brain-Computer Interface using neural network and temporal-spectral features.

Brain-Computer Interfaces (BCIs) are increasingly useful for control. Such BCIs can be used to assist individuals who lost mobility or control over their limbs, for recreational purposes such as gaming or semi-autonomous driving, or as an interface toward man-machine integration. Thus far, the performance of algorithms used for thought decoding has been limited. We show that by extracting temporal and spectral features from electroencephalography (EEG) signals and, following, using deep learning neural network to classify those features, one can significantly improve the performance of BCIs in predicting which motor action was imagined by a subject. Our movement prediction algorithm uses Sequential Backward Selection technique to jointly choose temporal and spectral features and a radial basis function neural network for the classification. The method shows an average performance increase of 3.50% compared to state-of-the-art benchmark algorithms. Using two popular public datasets our algorithm reaches 90.08% accuracy (compared to an average benchmark of 79.99%) on the first dataset and 88.74% (average benchmark: 82.01%) on the second dataset. Given the high variability within- and across-subjects in EEG-based action decoding, we suggest that using features from multiple modalities along with neural network classification protocol is likely to increase the performance of BCIs across various tasks.

Trajectory Online Adaption Based on Human Motion Prediction for Teleoperation

In this work, a human motion intention prediction method based on an autoregressive (AR) model for teleoperation is developed. Based on this method, the robot’s motion trajectory can be updated in real time through updating the parameters of the AR model. In the teleoperated robot’s control loop, a virtual force model is defined to describe the interaction profile and to correct the robot’s motion trajectory in real time. The proposed human motion prediction algorithm acts as a feedforward model to update the robot’s motion and to revise this motion in the process of human–robot interaction (HRI). The convergence of this method is analyzed theoretically. Comparative studies demonstrate the enhanced performance of the proposed approach. Note to Practitioners—In general, the robot trajectory is predetermined and it does not consider the influence of the interaction profiles in terms of position and interaction force between the human and the robot. In addition, it is hard to quantify the influence of interaction profile for the robot trajectory. For teleoperation, an AR-based model is proposed to predict the trajectory of the human and then to update the trajectory of the robot. The developed method includes the following aspects: 1) the robot trajectory can be regulated based on the interaction profiles; 2) the feedforward model can estimate the trajectory of the human to achieve the purpose of human intention recognition in advance for the robot; and 3) the proposed method can be potentially utilized for telerehabilitation, microsurgery, and so on.

Vehicle Motion Prediction Algorithm with Driving Intention Classification

The future motion prediction of vehicles in the front is widely valued for its great potential to improve a vehicle’s safety, fuel consumption, and efficiency. However, due to the uncertainty of a driver’s driving intentions and vehicle dynamics, future motion prediction faces great challenges. In order to break the bottleneck in the prediction of leading vehicle motion, this paper proposes a prediction idea of decoupling the prediction of leading vehicle motion into vertical vehicle speed prediction based on the Gaussian process regression algorithm and horizontal heading angle prediction based on the long short-term memory method, which combines the predicted vehicle speed and heading angle to derive the future trajectory of the leading vehicle. Moreover, we propose a prediction algorithm of the leading vehicle motion based on the combination of driving intention recognition and multimodel prediction results by the Fuzzy C-means algorithm, which tries to solve the problem of the unclear driving intention of the predicted object and the nonlinearity between the future motion of the vehicle and the environment. Finally, the algorithm is validated using real vehicle data, proving that it has high prediction accuracy.

Jomodevi: A joint motion and depth visibility prediction algorithm for perceived stereoscopic 3D quality

In this paper, we introduce a completely blind and unsupervised no-reference model that performs the quality assessment of stereoscopic 3D videos by using joint motion and depth visibility. In order to achieve such objective metric, first we compute the correlation map between the motion vector map and the depth map, and then we study the natural scene statistics of the estimated correlation map. We empirically model these statistics with Univariate Generalized Gaussian Distribution (UGGD) and compute the UGGD parameters at multi-scale and multi-orient steerable subband decomposition. It is shown that the estimated UGGD features are capable of discriminating distortions. We then compute the mean vector and the covariance matrix from the estimated UGGD features of the correlation maps of motion vector and the depth maps of undistorted and distorted S3D videos. This is followed by the computation of the Wave Hedges distance between the mean vectors of the undistorted and distorted contents, and the Bhattacharyya distance is measured between the corresponding covariance matrices. We pool these distance measures to achieve the overall joint motion and depth quality of S3D videos. The efficiency of the proposed algorithm is evaluated on the well-known IRCCYN, LFOVIAS3DPh1 and LFOVIAS3DPh2 S3D video datasets. Our model demonstrates robust and consistent performance across all distortion types and shows competitive performance against other 2D and 3D image and video quality assessment algorithms. Furthermore, since the proposed algorithm is completely blind, it does not require any training and testing analysis on the content features and the subjective scores.

Conflict resolution for connected automated vehicles at unsignalized roundabouts considering personalized driving behaviours

To address the driving conflicts of connected automated vehicles (CAVs) at unsignalized roundabouts, a cooperative decision-making framework is proposed. The personalized driving preferences of CAVs are considered in the decision-making algorithm, which are reflected by different driving styles. A motion prediction algorithm is used to improve the decision-making performance. The effect of the motion prediction algorithm on the decision-making performance is evaluated, including the advancement of driving safety and the computational load for the hardware. The cooperative game theoretic approach is applied to the interaction modelling and collaborative decision making of CAVs. Finally, hardware-in-the-loop (HIL) tests are carried out to evaluate the feasibility and real-time performance of the proposed algorithm.

Continuous Estimation of Finger and Wrist Joint Angles Using a Muscle Synergy Based Musculoskeletal Model

Recently, many muscle synergy-based human motion prediction models and algorithms have been proposed. In this study, the muscle synergies extracted from electromyography (EMG) data were used to construct a musculoskeletal model (MSM) to predict the joint angles of the wrist, thumb, index finger, and middle finger. EMG signals were analyzed using independent component analysis to reduce signal noise and task-irrelevant artifacts. The weights of each independent component (IC) were converted into a heat map related to the motion pattern and compared with human anatomy to find a different number of ICs matching the motion pattern. Based on the properties of the MSM, non-negative matrix factorization was used to extract muscle synergies from selected ICs that represent the extensor and flexor muscle groups. The effects of these choices on the prediction accuracy was also evaluated. The performance of the model was evaluated using the correlation coefficient (CC) and normalized root-mean-square error (NRMSE). The proposed method has a higher prediction accuracy than those of traditional methods, with an average CC of 92.0% and an average NRMSE of 10.7%.

A Collision Avoidance Algorithm for Human Motion Prediction Based on Perceived Risk of Collision: Part 2-Application

Occupational Application Digital human models have been widely used for occupational assessments to reduce potential injury risk, such as automotive assembly lines, box lifting, and in the mining industry. Human motion prediction is one of the important capabilities in digital human models, and collision avoidance is involved in human motion prediction. An algorithm proposed earlier was implemented for human motion prediction, and simulated results were found to have a good correlation with the experimental studies. Use of this algorithm can help ensure that human motion is predicted realistically, and thus can impact the accuracy of injury risk assessments. Background: With any type of human movement, there is the potential for a collision with other objects. In addition to the objects presented in the environment surrounding one’s body and surrounding the objects to be manipulated, one's own body can become an obstacle. Therefore, consideration of the methods available for avoiding obstacles is necessary to comprehensively describe the way human movements are planned. Purpose: This paper evaluates a collision avoidance algorithm for human motion prediction based on the perceived risk of collision, specifically the application to human motion prediction. Method: Human motion prediction is formulated as an optimization problem with dynamic effort as the cost function, and the perceived risk of collision is considered as one constraint among other constraints. Performance using the new formulation was compared to observed performance from an experiment. Result: Based on the results, the new formulation can account for the suboptimal behavior observed in real subjects while still optimizing biomechanical cost. The predicted motion is much more realistic compared with that from purely biomechanically optimized formulation. Application: The developed collision avoidance algorithm can be applied to optimization-based manual movement prediction in which obstacles need to be navigated.

Hierarchical NMPC–ISMC of active heave motion compensation system for TMS–ROV recovery

This study reveals the operating risk of cable breaking when the TMS–ROV recovery is carried out beneath rough ocean waves. To release the negative impact of the support vessels vertical heave motion on the station-keeping and position tracking performance of TMS, a robust hierarchical control scheme for a hydraulic-driven winch-based active heave compensation (AHC) system is proposed based on the combination of a nonlinear model predictive control (NMPC) strategy, an integral sliding mode control (ISMC) scheme and a vessel heave motion prediction algorithm. The motivation of utilizing the ISMC-based internal level combined with the integral state transformation technique is given by the robust capability to eliminate the uncertainties, which provides the nominal model for NMPC-based external level to guarantee the optimal evolution satisfying the state and input constraints and resisting the dynamical hysteresis caused by the employed extremely long cable length for deep-sea operation. Comparative simulation studies contribute to the analysis of the surge in cable tension during the TMS–ROV recovery and demonstrate the superior tracking and heave compensation performance, strong robustness and constraint satisfaction of the proposal.

A Collision Avoidance Algorithm for Human Motion Prediction Based on Perceived Risk of Collision: Part 1-Model Development

OCCUPATIONAL APPLICATIONS Digital human models have been widely used in occupational biomechanics assessments to prevent potential injury risks, such as automotive assembly lines, box lifting, patient repositioning, and the mining industry. Motion prediction is one of the important capabilities in digital human models, and collision avoidance is involved in human motion prediction. We propose an algorithm that will ensure human motions are predicted realistically, and finally, use of this algorithm could help enhance the accuracy of injury risk assessments using digital human models.