Accurate Trajectory Research Articles

Trajectory error compensation method for grinding robots based on kinematic calibration and joint variable prediction

Trajectory accuracy, a crucial metric in assessing the dynamic performance of grinding robots, is influenced by the uncertain movement of the tool center point, directly impacting the surface quality of processed workpieces. This article introduces an innovative method for compensating trajectory errors. Initially, a strategy for error compensation is derived using differential kinematics theory. Subsequently, a robot kinematic calibration method utilizing ring particle swarm optimization (RPSO) is proposed to address static errors in the grinding robot. Simultaneously, a method for predicting robot joint variables based on a dual-channel feedforward neural network (DCFNN) is designed to mitigate dynamic errors. Finally, a simulation platform is developed to validate the proposed method. Simulation analysis using extensive data demonstrates an 89.3% improvement in absolute position accuracy and a 74.2% reduction in error fluctuation range, outperforming sparrow search algorithm (SSA), improved mayfly algorithm (IMA), multi-representation integrated predictive neural network (MRIPNN), etc. Algorithmic comparison reveals that kinematic calibration significantly reduces the average trajectory error, while joint variable prediction notably minimizes error fluctuation. Validation through trajectory straightness testing and a 3D printing propeller grinding experiment achieves a trajectory straightness of 0.2425 mm. Implementing this method enables achieving 86.1% surface machining allowance within tolerance, making it an optimal solution for grinding robots.

SISGAN: A Generative Adversarial Network Pedestrian Trajectory Prediction Model Combining Interaction Information and Scene Information

Accurate pedestrian trajectory prediction is crucial in many fields. This requires the full use and learning of pedestrians’ social interactions, movements, and environmental information. In view of the current research on pedestrian trajectory prediction, wherein most of the pedestrian interaction information is explored from the level of overall interaction, this paper proposes the SISGAN model, which designs a social interaction module from the perspective of the target pedestrian, and takes four kinds of interaction information as the influencing factors of pedestrian interaction, so as to describe the influence mechanism of pedestrian–pedestrian interaction. In addition, in terms of environmental information, the index density of pedestrian historical trajectory in space is taken into account in the extraction of environmental information, which increases the potential correlation between environmental information and pedestrians. Finally, we integrate social interaction information and environmental information and make the final trajectory prediction based on GAN. Experiments on ETH and UCY datasets demonstrate the effectiveness of the SISGAN model proposed in this paper.

Autonomous Underwater Vehicle Navigation Enhancement by Optimized Side-Scan Sonar Registration and Improved Post-Processing Model Based on Factor Graph Optimization

Autonomous Underwater Vehicles (AUVs) equipped with Side-Scan Sonar (SSS) play a critical role in seabed mapping, where precise navigation data are essential for mosaicking sonar images to delineate the seafloor’s topography and feature locations. However, the accuracy of AUV navigation, based on Strapdown Inertial Navigation System (SINS)/Doppler Velocity Log (DVL) systems, tends to degrade over long-term mapping, which compromises the quality of sonar image mosaics. This study addresses the challenge by introducing a post-processing navigation method for AUV SSS surveys, utilizing Factor Graph Optimization (FGO). Specifically, the method utilizes an improved Fourier-based image registration algorithm to generate more robust relative position measurements. Then, through the integration of these measurements with data from SINS, DVL, and surface Global Navigation Satellite System (GNSS) within the FGO framework, the approach notably enhances the accuracy of the complete trajectory for AUV missions. Finally, the proposed method has been validated through both the simulation and AUV marine experiments.

Trajectory mapping through channel state information by triangulation method and fine-tuning

Trajectory mapping techniques have widespread applications in diverse fields, including robotics, localization, smart environments, gaming, and tracking systems. However, existing free devices encounter challenges in representing trajectories, thereby limiting the effectiveness of applications such as robotics, localization, and tracking systems. The imprecise mappings generated by these methods lead to suboptimal performance and unreliable results. The proposed approach leverages WiFi sensing through channel state information (CSI), triangulation techniques, and a fine-tuning mechanism to enhance trajectory precision within indoor environment trajectory mapping. The proposed solution employs a domain adapter fine-tuning technique to enable location-independent tracking via CSI, minimizing errors. The use of CSI MIMO signals for trajectory mapping offers enhanced spatial resolution, robust multipath handling, and improved accuracy in tracking movement by leveraging multiple antenna channels and exploiting the rich information embedded in signal reflections and scattering, while triangulation aids in accurately determining the location of objects or targets. Furthermore, incorporating a fine-tuning mechanism refines the generated trajectories. The findings demonstrate substantial enhancements in mapping precision, with an accuracy of 95.5% in tracking 13 paths within the new domain. These results underscore the effectiveness of the proposed approach in overcoming the limitations of existing methods and achieving highly accurate trajectory mapping.

Adaptive backstepping and sliding mode control of a quadrotor

This article proposes a simple robust adaptive control architecture with minimum parameter estimation laws for trajectory tracking of a quadrotor subjected to parametric variations and environmental disturbances. This simple control architecture aims to achieve highly accurate, robust, and fast trajectory tracking of the quadrotor in a short time with low computational cost. Firstly, the quadrotor model is divided into altitude, attitude, and position subsystems for which appropriate control methods are designed without prior knowledge of the upper bound of external disturbances. A simple adaptive fractional-order sliding mode control (AFSMC) is designed to enhance the tracking of the altitude subsystem and estimate the upper bound of the disturbances. Then, a simple adaptive backstepping control (ABC) is developed for the horizontal position to generate the required roll and pitch orientations. The adaptation laws not only estimate the upper bound of the disturbances but also adjust the controller gains thereby enhancing the robustness of the ABC. A nonsingular fast terminal sliding mode control (NFTSMC) is incorporated with a finite-time disturbance observer (FDO) to accurately suppress the disturbances, and follow the target rotation angles within a short finite-time. Simulation results showed that the compounded control structure ensures accurate, fast, and robust tracking. The AFSMC can achieve the desired altitude with a settling time of 0.31 s and root mean square error (RMSE) of 0.0454 m. The ABC can attain the target horizontal position coordinates with a settling time of (0.47 s, 0.47 s) and RMSE of (0.0370 m, 0.0518 s). The NFTSMC-FDO can achieve the desired attitude angles with a settling time of (0.11 s, 0.13 s, 0.52 s) and RMSE of (0.0098 rad, 0.0092 rad, 0.0935 rad). Performance comparisons with existing control methods in terms of settling time and RMSE demonstrated that the proposed control architecture is superior.

Lithium-ion battery future degradation trajectory early description amid data-driven end-of-life point and knee point co-prediction

This study develops a novel lithium-ion battery future degradation trajectory early description method amid data-driven end-of-life (EOL) point and knee point (KP) co-prediction. Inspired by the concept of KP, the proposed method firstly employs two-stage curve fitting to offline locate KP with minimization total absolute error between the original trajectory and fitted secants as object. Secondly, from partial multi-step fast-charging curves lying in high state-of-charge (SOC) level with only approximately 0.09 SOC interval, our method mines three new aging-dependent features for EOL and knee cycle co-prediction via two optimized Gaussian process regression models. Afterwards, considering the highly linear characteristics of slow degradation stage, knee capacity is further calculated by the linear function fitted via early capacity degradation sequence. Finally, with early capacity degradation sequence, predicted KP and predicted EOL point, the developed method adopts piece-wise cubic Hermite interpolating polynomial to directly generate future degradation trajectory. The verification results using a 140-cell aging dataset demonstrate that the proposed method is powerful for future degradation trajectory early prediction with high accuracy and extremely low real-time computational cost, where most root-mean-square-error and mean-absolute-percentage-error of trajectory prediction results are below 0.03 Ah and 3%, respectively. Our work, for the first time, reveals the possibility of feature extraction from partial multi-step fast-charging curves, and also provides a low-complexity universal framework for accurate future degradation trajectory early prediction.

ADRC-Based cooperative control for unmanned towing operation by multiple DP tugboats: A comparative study of two cooperative controllers

This paper addresses the challenge of coordinated manipulation of an unactuated floating object by multiple tugboats to execute both positioning and towing operation within complex marine environments. We proposes a multi-layer cooperative control algorithm for the multi-tugboat system, including tugboat control level and cooperative control level. At the individual tugboat control level, the dynamic positioning system (DPS) has been enhanced to match the operational behavior of the tugboat, enabling it to handle cable tension and environmental disturbances during transit. The controller incorporates a simple yet effective linear active disturbance rejection control method (ADRC) to counter external disturbances and ensure accurate trajectory tracking without relying on preset models. Moving to the multi-tugboat collaboration level, two distinct control strategies are proposed for towing operations: formation-based (FB) and force-allocation-based (FAB). The FB strategy involves multiple tugboats forming a coordinated formation, with control focused on the formation center to indirectly manipulate the object. On the other hand, the FAB strategy utilizes model predictive control (MPC) for the object to allocate the necessary towing force for each cable, thereby determining the required actions for each tugboat based on the cable model and the needed towing force. Simulation results demonstrate that both control strategies can effectively maneuver the floating object with high accuracy to perform positioning and towing tasks. Nevertheless, due to the variations in their fundamental mechanisms, there are significant discrepancies in towline tension, tugboat utilization, and energy consumption.

Control Based on Nonlinear Estimators of Parametric Uncertainties Applied to an Agricultural Tractor Equipped with a Towed Implement System

This article presents a nonlinear control strategy designed to address parametric uncertainties in an agricultural tractor system coupled to a towed implement. The controller ensures accurate tracking of lateral and yaw velocities relative to desired reference trajectories, even under the presence of parametric variations and external disturbances. The reference trajectories are derived from an “ideal” tractor model, excluding the effects of the towed implement. A High-Order Sliding Mode (HOSM) estimator is employed to provide an estimation of disturbances, which are subsequently mitigated by the controller to maintain system stability and precision. The effectiveness of the proposed control strategy is validated through Matlab-Simulink simulations, which include a double-step steer maneuver. This maneuver tests the system’s ability to handle abrupt steering changes, providing insight into the controller’s robustness and its capacity to ensure accurate trajectory tracking in demanding conditions.

RS-SLAM: real time semantic slam with driverless car using LiDAR-camera-IMU sensing

Abstract Accurate and robust Simultaneous Localization and Mapping (SLAM) technology is a critical component of driverless cars, and semantic information plays a vital role in their analysis and understanding of the scene. In the actual scene, the moving object will produce the shadow phenomenon in the mapping process. The positioning accuracy and mapping effect will be affected. Therefore, we propose a semantic SLAM framework combining LiDAR, IMU, and camera, which includes a semantic fusion front-end odometry module and a closed-loop back-end optimization module based on semantic information. An improved image semantic segmentation algorithm based on Deeplabv3+ is designed to enhance the performance of the image semantic segmentation model by replacing the backbone network and introducing an attention mechanism to ensure the accuracy of point cloud segmentation. Dynamic objects are detected and eliminated by calculating the similarity score of semantic labels. A loop closure detection method based on semantic information is proposed to detect key semantic features and use threshold range detection and point cloud re-matching to establish the correct loop closure detection, and finally reduce the global cumulative error and improve the global trajectory accuracy using graph optimization to ultimately obtain the global motion trajectory and realize the construction of 3D semantic maps. We evaluated it on the KITTI dataset and collected a dataset for evaluation by ourselves, which includes four different sequences. The results show that the proposed framework has good positioning accuracy and mapping effect in large-scale urban road environments.

GAP FILLING ALGORITHM FOR MOTION CAPTURE DATA TO CREATE REALISTIC VEHICLE ANIMATION

The dynamic development of the entertainment market entails the need to develop new methods enabling the application of current scientific achievements. Motion capture is one of the cutting-edge technologies that plays a key role in movement and trajectory computer mapping. The use of optical systems allows one to obtain highly precise motion data that is often applied in computer animations. This study aimed to define the research methodology proposed to analyze the movement of remotely controlled cars utilizing developed gap filling algorithm, a part of post-processing, for creating realistic vehicle animation. On a specially prepared model, six various types of movements were recorded, such as: driving straight line forward, driving straight line backwards, driving on a curve to the left, driving on a curve to the right and driving around a roundabout on both sides. These movements were recorded using a VICON passive motion capture system. As a result, three-dimensional models of vehicles were created that were further post-processed, mainly by filling in the gaps in the trajectories. The case study highlighted problems such as missing points at the beginning and end of the recordings. Therefore, algorithm was developed to solve the above-mentioned problem and allowed for obtaining an accurate movement trajectory throughout the entire route. Realistic animations were created from the prepared data. The preliminary studies allowed one for the verification of the research method and implemented algorithm for obtaining animations reflecting accurate movements.

Stochastic LPV MPC-based path following control for bevel-tip flexible needle with probabilistic constraints

This paper addresses the path-tracking problem for flexible needle control systems using a stochastic linear parameter varying (LPV) and model predictive control (MPC) strategy. Flexible needles operating in dynamic environments with non-uniform tissue density often deviate from ideal assumptions, resulting in non-standard models. The bicycle kinematics model for flexible needle motion control is transformed into an LPV model, improving accuracy and enabling more efficient control. The proposed stochastic LPV MPC approach aims to mitigate uncertainties arising from modelling errors and dynamic environmental factors, ensuring accurate trajectory tracking for the flexible needle. The sample and removal method is utilized to reformulate the probabilistic-constrained optimization problem for implementation. The contributions of this work lie in the application of stochastic LPV MPC to address the trajectory tracking problem in the presence of uncertainties. The simulation results illustrate the superior robustness of the stochastic LPV MPC approach, as evidenced by significantly smaller tracking errors across various scenarios.

Trajectory tracking control of a mobile robot using fuzzy logic controller with optimal parameters

Abstract This work investigates the use of a fuzzy logic controller (FLC) for two-wheeled differential drive mobile robot trajectory tracking control. Due to the inherent complexity associated with tuning the membership functions of an FLC, this work employs a particle swarm optimization algorithm to optimize the parameters of these functions. In order to automate and reduce the number of rule bases, the genetic algorithm is also employed for this study. The effectiveness of the proposed approach is validated through MATLAB simulations involving diverse path tracking scenarios. The performance of the FLC is compared against established controllers, including minimum norm solution, closed-loop inverse kinematics, and Jacobian transpose-based controllers. The results demonstrate that the FLC offers accurate trajectory tracking with reduced root mean square error and controller effort. An experimental, hardware-based investigation is also performed for further verification of the proposed system. In addition, the simulation is conducted for various paths in the presence of noise in order to assess the proposed controller’s robustness. The proposed method is resilient against noise and disturbances, according to the simulation outcomes.

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.