Accurate Trajectory Research Articles

Research on Trajectory Planning and Tracking Algorithm of Crawler Paver

The implementation of unmanned intelligent construction on the concrete surfaces of an airport effectively improves construction accuracy and reduces personnel investment. On the basis of three known common tracked vehicle dynamics models, reference trajectory planning and trajectory tracking controller algorithms need to be designed. In this paper, based on the driving characteristics of the tracked vehicle and the requirements of the stepping trajectory, a quartic polynomial trajectory planning algorithm was selected with the stability of the curve as a whole and the end point as the optimization goal, combining the tracked vehicle dynamics model, collision constraints, start–stop constraints and other boundary conditions. The objective function of trajectory planning was designed to effectively plan the reference trajectory of the tracked vehicle’s step-by-step travel. In order to realize accurate trajectory tracking control, a nonlinear model predictive controller with transverse-longitudinal integrated control was designed. To improve the real-time performance of the controller, a linear model predictive controller with horizontal and longitudinal decoupling was designed. MATLAB 2023A and CoppeliaSim V4.5.1 were used to co-simulate the two controller models. The experimental results show that the advantages and disadvantages of the tracked vehicle dynamics model and controller design are verified.

Integrated error modeling for abrasive waterjet cutting at various nozzle tilt angle

The research investigates the specific impact of high-pressure abrasive waterjet (AWJ) on the cutting accuracy of circular trajectories. The coupling relationship between the kerf taper and the jet lag of the cutting front profiles is analyzed. Thus, an integrated error model for abrasive waterjet cutting of circular trajectories is innovatively established, which can predict the deformation of the circular arc trajectory not only at right angles but also at tilt angles. Cutting experiments at vertical angles were conducted, with a maximum prediction accuracy of 2.41%. Besides, arc trajectories with different diameters were conducted with a prediction accuracy of 2.26%. In order to reduce the deformation error of arc trajectories, compensation experiments for nozzle tilt ranging from 76° to 94° were carried out. The experimental results showed that when the nozzle was tilted in the direction opposite to the traverse speed up to half of the maximum deviation angle at the jet outlet point, the maximum deformation of arc trajectories was 1.218 mm, which was reduced by 0.532 mm compared to the case of vertical incidence, and the cylindricity increased by 31.74%. This adjustment can meet the precision requirements for cutting thick materials with abrasive waterjet.

A New Accurate Aircraft Trajectory Prediction in Terminal Airspace Based on Spatio-Temporal Attention Mechanism

Trajectory prediction serves as a prerequisite for future trajectory-based operation, significantly reducing the uncertainty of aircraft movement information within airspace by scientifically forecasting the three-dimensional positions of aircraft over a certain period. As convergence points in the aviation network, airport terminal airspace exhibits the most complex traffic conditions in the entire air route network. It has stronger mutual influences and interactions among aircraft compared to the en-route phase. Current research typically uses the trajectory time series information of a single aircraft as input for subsequent predictions. However, it often lacks consideration of the close-range spatial interactions between multiple aircraft in the terminal airspace. This results in a gap in the study of aircraft trajectory prediction that couples spatiotemporal features. This paper aims to predict the four-dimensional trajectories of aircraft in terminal airspace, constructing a Spatio-Temporal Transformer (ST-Transformer) prediction model based on temporal and spatial attention mechanisms. Using radar aircraft trajectory data from the Guangzhou Baiyun Airport terminal airspace, the results indicate that the proposed ST-Transformer model has a smaller prediction error compared to mainstream deep learning prediction models. This demonstrates that the model can better integrate the temporal sequence correlation of trajectory features and the potential spatial interaction information among trajectories for accurate prediction.

Design and development of closed-loop controllers for trajectory tracking of a planar vibration-driven robot

Vibration-driven robots constitute an innovative paradigm for achieving locomotion, leveraging periodic vibrations to meticulously control the movement of an internal mass, thus affording them a high degree of precision while navigating surfaces with varying friction characteristics. This paper is dedicated to the refinement of trajectory tracking in planar vibration-driven robots, achieved through the meticulous design and implementation of a Proportional-Integral-Derivative (PID) controller and Sliding Mode Controller (SMC). The considered vibration-driven robot is propelled using two parallel reciprocating unbalanced masses which allows the robot to have various maneuvers in two dimensions. The movement of the robot is improved by employing bristles to make non-isotropic Coloumb’s friction on the surfaces. At first, the governing dynamic equations of the robot are derived by considering the stick-slip effect and using the Euler–Lagrange method. Moreover, a PID controller for accurate trajectory tracking within the robot’s natural coordinate system is designed and employed. The fine-tuning of the PID controller’s coefficients is accomplished through the application of the NSGA-II optimization method. Subsequently, a SMC strategy is introduced to enable the robot’s control in an absolute coordinate system. The paper culminates with the presentation, in-depth analysis, and evaluation of the simulation results, shedding light on the significant enhancements in performance and capabilities achieved by vibration-driven robots. In conclusion, the pivotal role of the NSGA II algorithm in optimizing controller parameters is emphasized, and although the PID controller excels in trajectory tracking, challenges with sudden acceleration changes are identified.

Locomotion trajectory optimization for quadruped robots with kinematic parameter calibration and compensation

It is significant to improve the locomotion trajectory accuracy of a quadruped robot for more applications. A compensation approach with kinematic parameter calibration is proposed to enhance the trajectory accuracy. The motion equation of the foot ends in coordinated gait is given. The related kinematic error model is deduced. The model for identifying the joint parameter errors is implemented via the trajectory error in the Z-direction. The trunk trajectory accuracy enhancement is facilitated by compensating the joint parameter error with joint angles as tuning variables. The performance of the methodology is validated in the simulation system by predetermining ten random sets of joint variable errors. The results indicate that both the trajectory and the attitude accuracy of the calibrated robot can be significantly improved. In practical experiments, it is shown that an error reduction rate of 80% in positional deviation and 58% in attitude error can be achieved.

TrajectoryNAS: A Neural Architecture Search for Trajectory Prediction.

Autonomous driving systems are a rapidly evolving technology. Trajectory prediction is a critical component of autonomous driving systems that enables safe navigation by anticipating the movement of surrounding objects. Lidar point-cloud data provide a 3D view of solid objects surrounding the ego-vehicle. Hence, trajectory prediction using Lidar point-cloud data performs better than 2D RGB cameras due to providing the distance between the target object and the ego-vehicle. However, processing point-cloud data is a costly and complicated process, and state-of-the-art 3D trajectory predictions using point-cloud data suffer from slow and erroneous predictions. State-of-the-art trajectory prediction approaches suffer from handcrafted and inefficient architectures, which can lead to low accuracy and suboptimal inference times. Neural architecture search (NAS) is a method proposed to optimize neural network models by using search algorithms to redesign architectures based on their performance and runtime. This paper introduces TrajectoryNAS, a novel neural architecture search (NAS) method designed to develop an efficient and more accurate LiDAR-based trajectory prediction model for predicting the trajectories of objects surrounding the ego vehicle. TrajectoryNAS systematically optimizes the architecture of an end-to-end trajectory prediction algorithm, incorporating all stacked components that are prerequisites for trajectory prediction, including object detection and object tracking, using metaheuristic algorithms. This approach addresses the neural architecture designs in each component of trajectory prediction, considering accuracy loss and the associated overhead latency. Our method introduces a novel multi-objective energy function that integrates accuracy and efficiency metrics, enabling the creation of a model that significantly outperforms existing approaches. Through empirical studies, TrajectoryNAS demonstrates its effectiveness in enhancing the performance of autonomous driving systems, marking a significant advancement in the field. Experimental results reveal that TrajcetoryNAS yields a minimum of 4.8 higger accuracy and 1.1* lower latency over competing methods on the NuScenes dataset.

Reservoir Computing for Drone Trajectory Intent Prediction: A Physics Informed Approach.

The design of accurate trajectory prediction algorithms is crucial to implement adequate countermeasures against drones with anomalous performances. Wrong predictions may cause high-false-positives that compromise safety in national infrastructures. In this article, a physics informed reservoir computing (PIRC) scheme for drone trajectory prediction is proposed. The approach is comprised of two main complementary learning algorithms that enhance the prediction and generalization capabilities: 1) a standard reservoir computing scheme for high-dimensional encoding exploitation and 2) a nonlinear control scheme that gives a physical feedback to the reservoir weights to ensure the prediction error is minimized. The nonlinear control scheme is modeled by the prediction error dynamics and a feedback linearization controller. Two different PIRC schemes are proposed which preserve the reservoir properties and enhance the prediction robustness. Lyapunov stability theory is used to verify the boundedness and convergence of the proposed algorithms. Simulation studies and comparisons are given to verify the proposed approach.

Initial trajectory design of low-thrust spacecraft considering attitude constraints

The fuel carried by deep space exploration spacecraft is crucial for the completion of their exploration missions, and the fuel for attitude control engines is even more precious. In order to reduce the control requirements for attitude control systems, this paper proposes a shape-based trajectory optimization algorithm that considers attitude constraints for low-thrust spacecraft. This method obtains a more accurate transfer trajectory by considering the change rate and change range constraints of the propulsion acceleration direction of spacecraft. By comparing the simulation results without considering spacecraft attitude constraints, it is confirmed that the proposed algorithm considering attitude constraints is very important for the initial design of transfer trajectories. This is of great significance for high-precision initial trajectory optimization of deep space exploration missions.

Interaction aware and multi-modal distribution for ship trajectory prediction with spatio-temporal crisscross hybrid network

Understanding the interactions between a ship and its surrounding ships enables effective trajectory prediction, which is critical to improving the safe navigation of autonomous ships. The prediction of future trajectories is a very challenging problem due to inherent uncertainties and complex spatiotemporal correlations between different ships. However, existing methods ignore the persistence and cross-domain nature of the influence between ships. To address the above challenges, an adaptive learning framework based on spatio-temporal crisscross hybrid network (STCNet) is proposed, which consists of two parts: spatio-temporal interaction aware and multi-modal trajectory prediction. Modeling temporal-dependent features, spatial interaction features and cross-domain features, and performs adaptive fusion to identify important features and capture all dynamic dependencies. Secondly, most methods only focus on the frequent modes of trajectories and cannot cover the actual paths of limited samples. Therefore, we design an augmented sampling method based on fusion knowledge and graph attention mechanism (KGS) to encourage exploration of trajectories in sparse areas of the sample space, and promote more accurate and reasonable future trajectory prediction. Experiments on the Ningbo-Zhoushan Port sea area dataset show that our method achieves better results than other methods.

BA-CLM: A Globally Consistent 3D LiDAR Mapping Based on Bundle Adjustment Cost Factors.

Constructing a globally consistent high-precision map is essential for the application of mobile robots. Existing optimization-based mapping methods typically constrain robot states in pose space during the graph optimization process, without directly optimizing the structure of the scene, thereby causing the map to be inconsistent. To address the above issues, this paper presents a three-dimensional (3D) LiDAR mapping framework (i.e., BA-CLM) based on LiDAR bundle adjustment (LBA) cost factors. We propose a multivariate LBA cost factor, which is built from a multi-resolution voxel map, to uniformly constrain the robot poses within a submap. The framework proposed in this paper applies the LBA cost factors for both local and global map optimization. Experimental results on several public 3D LiDAR datasets and a self-collected 32-line LiDAR dataset demonstrate that the proposed method achieves accurate trajectory estimation and consistent mapping.

An adaptive framework for trajectory following in changing-contact robot manipulation tasks

We describe an adaptive control framework for changing-contact robot manipulation tasks that require the robot to make and break contacts with objects and surfaces. The piecewise continuous interaction dynamics of such tasks make it difficult to construct and use a single dynamics model or control strategy. Also, the nonlinear dynamics during contact changes can damage the robot or the domain objects. Our framework enables the robot to incrementally improve its prediction of contact changes in such tasks, efficiently learn models for the piecewise continuous interaction dynamics, and to provide smooth and accurate trajectory tracking based on a task-space variable impedance controller. We experimentally compare the performance of our framework against that of representative control methods to establish that the adaptive control, prediction, and incremental learning capabilities of our framework are essential to achieve the desired smooth control of changing-contact robot manipulation tasks.

Neural-network-based trajectory error compensation for industrial robots with milling force disturbance

ABSTRACT Industrial robots are increasingly used in advanced manufacturing fields such as aerospace due to their high efficiency and low cost. In robotic machining applications, deviation of the tool centre point trajectory from the desired path due to load disturbances acting on the end-effector of an industrial robot can result in poor dimensional accuracy and surface quality of products. Therefore, improving robot trajectory accuracy under external load disturbances is extremely important. This study proposes an error compensation methodology using neural networks optimized by a hybrid marine predators-grid search algorithm to compensate for robot trajectory error. Two neural networks are developed: one for predicting load disturbances and the other for predicting trajectory errors in robotic machining. The milling experiment results show that the compensated robot trajectory errors in x, y, and z directions are reduced by 65%, 76%, and 77% respectively, which proves the effectiveness of this method in improving the robotic milling accuracy.

Accurate trajectory tracking control for AUV under state constraints with a rapid stability dimensionality‐augmented state observer

AbstractA rapid stability dimensionality‐augmented state observer (RSDASO) based event‐driven control strategy is presented for the autonomous underwater vehicle (AUV) trajectory tracking issue, addressing state constraints, model uncertainty, limited communication resources and unknown external time‐varying disturbances. The first step is to develop a fast stability extended state observer to estimate the lumped disturbances and unmeasurable states of the system and ensure the estimation error converges in fixed time. Secondly, a fixed‐time AUV trajectory tracking control method is proposed to ensure that the tracking error of the system converges within a fixed time, based on the mentioned observer. Simultaneously, to reduce the communication resource usage by the system, an event‐triggered mechanism (ETM) is included in the control law. Lastly, simulation experiments verify the effectiveness of the process.

Adaptive optimal concurrent control for detumbling space non-cooperative target via multipoint repeated contact

Space debris generally exhibits complex tumbling motions, and its direct capture may cause damage to space manipulator and base spacecraft. Current detumbling strategies typically require continuous contact collisions with the target and do not take into account actuator limitations. Thus, this paper presents an adaptive optimal concurrent control of space free-floating multi-fingered robot (SFMR) for multipoint repeated contact detumbling of non-cooperative targets. Firstly, the multipoint repeated intermittent contact model between the SFMR and the target is established. Further, an optimal admittance control that considers manipulator actuator limits and target motion bounds is formulated to generate a compliant detumbling trajectory. Through transforming the state and input inequality constraints into extended dynamical subsystems and saturation functions, respectively, the optimal control problem (OCP) is transformed into a readily solvable equality constraint problem. Moreover, an enhanced nonsingular terminal sliding mode (ENTSM) control with radial basis function neural network (RBFNN) compensation is presented in the presence of lumped uncertainties and external disturbances, which achieves rapid finite-time convergence and low-chattering. The simulation results show that the proposed method can reduce the velocity of the space target effectively without causing base spacecraft interference while achieving accurate trajectory tracking.

An integrated framework for accurate trajectory prediction based on deep learning

An integrated framework for accurate trajectory prediction based on deep learning

Enhanced Trajectory Forecasting for Hypersonic Glide Vehicle via Physics-Embedded Neural ODE

Forecasting hypersonic glide vehicle (HGV) trajectories accurately is crucial for defense, but traditional methods face challenges due to the scarce real-world data and the intricate dynamics of these vehicles. Data-driven approaches based on deep learning, while having emerged in recent years, often exhibit limitations in predictive accuracy and long-term forecasting. Whereas, physics-informed neural networks (PINNs) offer a solution by incorporating physical laws, but they treat these laws as constraints rather than fully integrating them into the learning process. This paper presents PhysNODE, a novel physics-embedded neural ODE model for the precise forecasting of HGV trajectories, which directly integrates the equations of HGV motion into a neural ODE. PhysNODE leverages a neural network to estimate the hidden aerodynamic parameters within these equations. These parameters are then combined with observable physical quantities to form a derivative function, which is fed into an ODE solver to predict the future trajectory. Comprehensive experiments using simulated datasets of HGV trajectories demonstrate that PhysNODE outperforms the state-of-the-art data-driven and physics-informed methods, particularly when training data is limited. The results highlight the benefit of embedding the physics of the HGV motion into the neural ODE for improved accuracy and stability in trajectory predicting.

Portable ultrasound-guided keyhole evacuation of intracerebral hemorrhage: a detailed case report highlighting technical nuances.

Intracerebral hemorrhage (ICH) is a common neurosurgical emergency that is associated with high morbidity and mortality. Minimally invasive or endoscopic hematoma evacuation has emerged in recent years as a viable alternative to conventional large craniotomies. However, accurate trajectory planning and placement of the tubular retractor remains a challenge. We describe a novel technique for handheld portable ultrasound-guided minimally invasive endoscopic evacuation of supratentorial hematomas. A 64-year-old male diagnosed right hematoma (48.5 mL) at the basal ganglia was treated with emergent ultrasound-guided endoscopic transtubular evacuation through a small craniotomy. Ultrasound-guidance facilitated optimal placement of the tubular retractor into the long axis of the hematoma, and allowed for near-total evacuation, reducing iatrogenic tissue damage by mitigating the need for wanding or repositioning of the retractor. The emergence of a new generation of small portable phased array ultrasound probes with improved resolution and clarity has broadened ultrasound's clinical applications.

An optimized MOPSA algorithm for highly accurate deterministic lateral displacement microfluidic sorting chip design

Abstract Deterministic lateral displacement (DLD) sorting is a passive sorting technology. Due to the advantages of small volume and high accuracy, the DLD devices have been used in many applications. The critical diameter (Dc) value of the DLD device depends on the geometric structure of the internal post array. However, the existing empirical formula cannot match all types of post-arrays. Achieving the desired Dc value typically involves multiple iterative processes, leading to increased labor and time costs. In this paper, we propose an optimized trajectory prediction algorithm based on the original MOPSA, which considers the impact of fluidic pressure on particle trajectories. The method is to stretch the 2D physical field space into 3D, the particle expands from a 2D circle into a 3D ball on the pressure analysis. The net pressure on the particle is calculated, and the displacement of the particle is predicted by combining Newton’s second law and the displacement formula. A DLD device with Dc=10.6 μm was designed using this optimized algorithm and fabricated. It is verified by separation of 10 μm and 11 μm polystyrene particles. The test results were consistent with the simulation results. Compared with the empirical formula and the original MOPSA, this optimized algorithm can improve the prediction accuracy of particle trajectories in DLD devices.

Output feedback active disturbance rejection control of an electro-hydraulic servo system based on command filter

The output feedback active disturbance rejection control of a valve-controlled cylinder electro-hydraulic servo system is investigated in this paper. First, a comprehensive nonlinear mathematical model that encompasses both matched and mismatched disturbances is formulated. Due to the fact that only position information can be measured, a linear Extended State Observer (ESO) is introduced to estimate unknown states and matched disturbances, while a dedicated disturbance observer is constructed to estimate mismatched disturbances. Different from the traditional observer results, the design of the disturbance observer used in this study is carried out under the constraint of output feedback. Furthermore, an output feedback nonlinear controller is proposed leveraging the aforementioned observers to achieve accurate trajectory tracking. To mitigate the inherent differential explosion problem of the traditional backstepping framework, a finite-time stable command filter is incorporated. Simultaneously, considering transient filtering errors, a set of error compensation signals are designed to counter their negative impact effectively. Theoretical analysis affirms that the proposed control strategy ensures the boundedness of all signals within the closed-loop system. Additionally, under the specific condition of only time-invariant disturbances in the system, the conclusion of asymptotic stability is established. Finally, the algorithm’s efficacy is validated through comparative experiments.

Comparison of curved and straight tip radiofrequency cannula deflection in a ballistic model

BackgroundPercutaneous pain and spine procedures play an important diagnostic and therapeutic role in the treatment of various pain diagnoses. Accurate placement of needles or cannulae during these procedures is paramount to the success of these procedures. ObjectiveThe purpose of this study is to examine and quantify the amount of deflection of radiofrequency cannulae based on curved tip versus no curved tip, using a ballistic gel tissue simulant. Materials and methodsSix different types of cannulae commonly used for spinal and peripheral nerve ablations were selected, including 18, 20, and 22 gauge curved and straight radiofrequency cannulae. Ballistic gel samples were made in molds of 40 mm and 80 mm. Each cannula was mounted in a drill press to ensure accurate trajectory. ResultsCurved RFA cannula had increased deflection when compared to straight cannula for 18-, 20-, and 22-gauge cannulae at a depth of 40 mm. Curved RFA cannula had increased deflection when compared to straight cannula for 20- and 22-gauge cannulae at a depth of 80 mm. Overall, the mean deflection for a curved cannula increased 1.9x for 20-gauge cannulae and 2.5x for 22-gauge cannulae when compared to a straight cannula. ConclusionsFor interventionalists, understanding the effects of needle or cannula shape is crucial for accurate placement. When a procedure requires additional steerability, additional deflection up to 2.5x obtained by placing a bend in the needle or cannula tip should be considered.