Improve Tracking Performance Research Articles

Distributed time‐varying optimization control for multirobot systems with collision avoidance by hierarchical approach

AbstractIn this article, a distributed time‐varying optimization problem for multirobot systems with collision avoidance is studied. A hierarchical control framework is employed to simplify the complex problem into two subproblems in two layers. One is the network layer where a novel distributed time‐varying finite‐time optimal control protocol as a signal generator is proposed for each robot. The signal generator ignores the model constraint and uncertainty. The other is the real‐time layer, which mainly contains a sampling module and a robust nonlinear model predictive control (NMPC) module. The former samples the optimal signal from the signal generator as a series of sampling tracking points with proper distance to improve tracking performance, and the latter searches for the optimal control protocol for the robot system to track these sampling tracking points in sequence with the model constraint and uncertainty. Furthermore, considering the collision between each robot, a give‐way strategy is designed that the robot with a low priority has to stop to wait for the other with a high priority to move first. And a primary‐secondary judgment technology determines the priorities of robots adaptively according to the current state of the robots. If the way forward of the moving robot is blocked by the stopping one, an auxiliary point is introduced as a temporary transfer station to avoid collision further. Finally, the effectiveness of the proposed methods is verified by a simulation example.

Rendering the Directional TSDF for Tracking and Multi-Sensor Registration with Point-To-Plane Scale ICP

Dense real-time tracking and mapping from RGB-D images is an important tool for many robotic applications, such as navigation and manipulation. The recently presented Directional Truncated Signed Distance Function (DTSDF) is an augmentation of the regular TSDF that shows potential for more coherent maps and improved tracking performance. In this work, we present methods for rendering depth- and color images from the DTSDF, making it a true drop-in replacement for the regular TSDF in established trackers. We evaluate the algorithm on well-established datasets and observe that our method improves tracking performance and increases re-usability of mapped scenes. Furthermore, we add color integration which notably improves color-correctness at adjacent surfaces. Our novel formulation of combined ICP with frame-to-keyframe photometric error minimization further improves tracking results. Lastly, we introduce Sim(3) point-to-plane ICP for refining pose priors in a multi-sensor scenario with different scale factors.

Consensus based target tracking against deception jamming in distributed radar networks

AbstractIn this study, an anti‐deception jamming target tracking algorithm based on the data‐level fusion is proposed in a distributed radar network. First, the consensus covariance intersection (CCI) algorithm is proposed to improve tracking performance by introducing the consensus theory to the field of covariance intersection fusion. Second, unscented Kalman filter is utilised to implement the preprocessing of distributed filtering. Then, two distributed target discrimination schemes are developed for non‐collaborative and collaborative false targets respectively. For non‐collaborative false targets, according to the characteristics that estimated spatial locations of the false target from different radars are relatively scattered, a false target elimination method based on nearest neighbour algorithm is proposed. For collaborative false targets, after equipped with passive radars that are less vulnerable to interference, the consensus algorithm is introduced to convey the real target information to the unconnected active radars, thus achieving an effect of global anti‐deception jamming. Finally, the CCI algorithm is deployed for distributed fusion of physical target tracking, so as to improve the overall estimation performance.

Improved High Order Model-Free Adaptive Iterative Learning Control with Disturbance Compensation and Enhanced Convergence

In this paper, an improved high-order model-free adaptive iterative control (IHOMFAILC) method for a class of nonlinear discrete-time systems is proposed based on the compact format dynamic linearization method. This method adds the differential of tracking error in the criteria function to compensate for the effect of the random disturbance. Meanwhile, a high-order estimation algorithm is used to estimate the value of pseudo partial derivative (PPD), that is, the current value of PPD is updated by that of previous iterations. Thus the rapid convergence of the maximum tracking error is not limited by the initial value of PPD. The convergence of the maximum tracking error is deduced in detail. This method can track the desired output with enhanced convergence and improved tracking performance. Two examples are used to verify the convergence and effectiveness of the proposed method.

Photovoltaic global maximum power point tracking method based on fuzzy PI control

At present, algorithms for finding the maximum power point of photovoltaic systems generally have some limitations, especially in cases of uneven light intensity, traditional algorithms often fall into local optima, with limited application space, and their tracking speed and stability are also not very good. To address this issue and improve tracking performance, the combination control of global scanning and fuzzy PI control is proposed. This combination control can avoid falling into local optima and achieve stable output of maximum power. This article first proves theoretically that the proposed method can achieve maximum power output; Then, an experimental platform was built for testing. Build control and sampling circuits and use a solar simulator to irradiate the photovoltaic array unevenly. In this environment, test the control effects of combination control, conductivity increment method, and disturbance observation method respectively. Through testing, it was found that compared to conductivity increment method and disturbance observation method, the proposed method in this article has faster tracking speed, higher output power, and better stability. The overall performance of combination control is better than other algorithms and has a relatively broad application space.

Improvement of Linear Tracking Response of Two-Degree-of-Freedom Control of Discrete-Valued Driven Crawler

To enable autonomous driving, the linear tracking control of on–off driven crawler vehicles, which are often used in agriculture, must be optimized. This paper presents a linear tracking control method for a discrete-value-driven crawler. Control laws based on feedback control are proposed for tracking control of discrete-valued driven crawler vehicles. Herein, we design a two-degree-of-freedom control system with feedforward for a discrete-value-driven crawler to improve tracking performance while reducing computational cost. The proposed method converts a continuous-valued input, determined using a two-degree-of-freedom control law, into a discrete-valued input based on a quadratic formal evaluation function. The effectiveness of the proposed method was verified through numerical simulations. As a result, we confirmed that the proposed method improves the tracking performance while reducing the computational complexity. This study made it possible to design a two-degree-of-freedom control system in discrete-valued input systems.

Fault tolerance evaluation of model-free controllers with application to unmanned aerial vehicles

Although important improvements in the area of robust control of nonlinear systems have been presented in the literature, most of the developed controllers suffer from complexity and large dependency on accurate mathematical formulation of the models. Recently, model-free robust control techniques were introduced and have shown good performance when applied to multi-input–multi-output systems. The model-free approach is characterized by the nonuse of any prior knowledge about the underlying structure and/or associated parameters of the dynamical system. Therefore, the major criteria for assessing the effectiveness of these controllers are related to their ability to handle unknown inputs and disturbances, as well as achieving the desired tracking performance in presence of faults and malfunctions. This work considers the development of robust fault-tolerant controllers based on the model-free approach and their application to multirotor unmanned aerial vehicles’ (UAVs) systems. The different controllers based on intelligent proportional-derivative (iPD), intelligent backstepping (iBackstepping), and adaptive control are compared in terms of performance, ease of implementation and parameters tuning. The simulated results, tested on Matlab/Simulink on a full nonlinear model of a hexarotor UAV, validate the theoretical advantages of the adaptive approach with respect to multiple criteria such as improved tracking performance in case of existence of actuators faults when compared to the iPD and iBackstepping control methods at the cost of increased complexity.

Adaptive Kernel Kalman Filter

Sequential Bayesian filters in non-linear dynamic systems require the recursive estimation of the predictive and posterior distributions. This paper introduces a Bayesian filter called the adaptive kernel Kalman filter (AKKF). With this filter, the arbitrary predictive and posterior distributions of hidden states are approximated using the empirical kernel mean embeddings (KMEs) in reproducing kernel Hilbert spaces (RKHSs). In parallel with the KMEs, some particles, in the data space, are used to capture the properties of the dynamical system model. Specifically, particles are generated and updated in the data space, while the corresponding kernel weight mean vector and covariance matrix associated with the feature mappings of the particles are predicted and updated in the RKHSs based on the kernel Kalman rule (KKR). Simulation results are presented to confirm the improved performance of our approach with significantly reduced particle numbers, by comparing with the unscented Kalman filter (UKF), particle filter (PF) and Gaussian particle filter (GPF). For example, compared with the GPF, the proposed approach provides around 5% logarithmic mean square error (LMSE) tracking performance improvement in the bearing-only tracking (BOT) system when using 50 particles.

Adaptively Adjusted EKF-Based Magnet Tracking Method for Fast-Moving Object

Permanent magnet tracking (PMT) can be employed in robots and precision control due to its advantage of wireless, without optical occlusion, and simultaneous position and orientation tracking. However, existing PMT studies based on optimization algorithms are limited to stationary or low-speed motions, where the tracking accuracy rapidly decreases with the increasing magnet speed. This study presents an extended Kalman filter (EKF)-based PMT. The magnetic field distribution around the cylindrical permanent magnet is approximated with a magnetic dipole model, where a random walk model is utilized as the system motion model. The state update model of the EKF estimation is adaptively adjusted according to different magnet speeds, while the initial magnet pose is approximated by the statistical analysis of magnetometer array outputs. The proposed PMT method hence can track the object at a higher speed while ensuring an effective pose accuracy than the previous studies. The proposed method was tested in technical verification experiments and a practical application. In addition, the relationship between the magnet speed and the position accuracy is investigated. When the magnet speed reaches 40 mm/s, experimental results show that the average pose error and algorithm latency are (1.83 ± 0.25 mm, 1.296 ± 0.091°) and 0.485 ± 0.064 ms, respectively. In the robot parking experiment for charging, the pose errors are (2.85 ± 0.41mm, 1.302 ± 0.023°) when the robot speed reaches 40 mm/s. The proposed method can greatly improve tracking performance for fast-moving object is greatly improved by performing the proposed method, thereby expanding the application scenarios of PMT.

Tracking control on homogeneous spaces: the Equivariant Regulator (EqR)

Accurate tracking of planned trajectories in the presence of perturbations is an important problem in control and robotics. Symmetry is a fundamental mathematical feature of many dynamical systems and exploiting this property offers the potential of improved tracking performance. In this paper, we investigate the tracking problem for input-affine systems on homogeneous spaces: manifolds which admit symmetries with transitive group actions. We show that there is natural manner to lift any desired trajectory of such a system to a lifted trajectory on the symmetry group. This construction allows us to define a global tracking error and apply LQR design to obtain an approximately optimal control in a single coordinate chart. The resulting control is then applied to the original plant and shown to yield excellent tracking performance. We term the resulting design methodology the Equivariant Regulator (EqR). We provide an example input-affine system posed on a homogeneous space, derive the trajectory linearisation in error coordinates and demonstrate the effectiveness of EqR compared to standard approaches in simulation.

MANGO: low-cost open source needle guidance

ABSTRACT Validating experiments in intra-operative planning for percutaneous interventions requires to localise the needle with a tracking system. As commercial optical trackers are expensive, we present MANGO, a low-cost OpenSource solution for needle tracking. We first present the design of our system, which localises coloured orbs using a stereo-camera setup. Based on this setup, we investigate the static and dynamic error of the system, and experiment with signal filtering to improve tracking performance. With the MANGO, we are able to meet the positional accuracy margin of 5 mm usually required in percutaneous interventions. Orientational accuracy, measured by needle tip localisation accuracy as a proxy measure, exceeds this margin. We plan to investigate this further in future research. Releasing our code, data and 3D models, we aim to provide a low-cost and reproducible platform for needle tracking and validation of intra-operative planning in research environments.

Multidomain Joint Learning of Pedestrian Detection for Application to Quadrotors

Pedestrian detection and tracking are critical functions in the application of computer vision for autonomous driving in terms of accident avoidance and safety. Extending the application to drones expands the monitoring space from 2D to 3D but complicates the task. Images captured from various angles pose a great challenge for pedestrian detection, because image features from different angles tremendously vary and the detection performance of deep neural networks deteriorates. In this paper, this multiple-angle issue is treated as a multiple-domain problem, and a novel multidomain joint learning (MDJL) method is proposed to train a deep neural network using drone data from multiple domains. Domain-guided dropout, a critical mechanism in MDJL, is developed to self-organize domain-specific features according to neuron impact scores. After training and fine-tuning the network, the accuracy of the obtained model improved in all the domains. In addition, we also combined the MDJL with Markov decision-process trackers to create a multiobject tracking system for flying drones. Experiments are conducted on many benchmarks, and the proposed method is compared with several state-of-the-art methods. Experimental results show that the MDJL effectively tackles many scenarios and significantly improves tracking performance.

Cooperative Multi-Objective Control of Heterogeneous Vehicle Platoons on Highway with Varying Slopes

Stability, vehicle safety, energy saving, and passenger comfort are the major objectives of vehicle platooning control. These objectives are coupled, interrelated, and even conflicting, so integrated optimization of multiple objectives is quite challenging. Particularly for heterogeneous platoons, the difficulties are intensified for the differences in vehicle dynamics. In this paper, the concept of symmetry is utilized in the platooning control, that is, the design method of each vehicle’s controller is the same. For each controller, it is to solve the optimal solution of multi-objective collaborative optimization. The concept of asymmetry is meanwhile embodied in the parameter setting of each controller, for the vehicle heterogeneity. The contents of this study are as follows. First, a mathematical model is established, in which the differences in vehicle dynamic characteristics of heterogeneous platoon, road slope, and aerodynamics are all taken into account. Then, based on distributed nonlinear model predictive control (DNMPC) method, multi-objective control strategies are proposed for the leader and followers, cooperatively. Furthermore, a weight coefficient optimization method is presented, to further improve the platoon’s multi-objective synthesis performance. Finally, comparative experiments are carried out. Results demonstrate that, compared with the classic cruise control method of vehicle platoons, the proposed approach can reduce energy consumption by more than 5% and improve tracking performance on the premise of passenger comfort. Real-road experiments verify that the proposed control system can function effectively and satisfy the computational requirements in real applications.

Anatomy aware-based 2.5D bronchoscope tracking for image-guided bronchoscopic navigation

ABSTRACT Physicians use an endoscopic navigation system during bronchoscopy to decrease the risk of getting lost in complex tree-structure like bronchus. Most existing navigation systems based on the camera pose estimated from bronchoscope tracking and/or deep learning. However, bronchoscope tracking-based method exists tracking error, and the pre-training of the model needs massive data. This paper describes an improved bronchoscope tracking procedure by adopting image domain translation technique to improve tracking performance. Specifically, our scheme consists of three modules, an RGB-D image domain translation module, an anatomical structure classification module and a structure-aware bronchoscope tracking module. The RGB-D image domain translation module translates a real bronchoscope (RB) image to its corresponding virtual bronchoscope image and depth image. The anatomical dependency module classifies the current scene into two categories: structureless and rich structure. The bronchoscope tracking module uses a modified video-CT bronchoscope tracking approach to estimate camera pose. Experimental results showed that the proposed method achieved higher tracking accuracy than the current state-of-the-art bronchoscope tracking methods.

ATLAS ITk tracking and readout performance

The ATLAS Inner Detector will be replaced with a new all-silicon Inner Tracker (ITk) designed for the challenging environment of the High Luminosity LHC (HL-LHC). The ITk layout of the pixel detector was recently successfully optimized to improve tracking performance and object reconstruction. The data rates of the ITk pixel detector have been evaluated to establish that the detector readout system can read out data at the design L0 trigger frequency of 1 MHz.

Geometric Adaptive Robust Hierarchical Control for Quadrotors With Aerodynamic Damping and Complete Inertia Compensation

In this article, the geometric adaptive robust hierarchical control strategy is proposed for underactuated quadrotor, in which the negative effects of nonlinearities, uncertainties, and coupling are thoroughly considered and effectively attenuated. For handling nonlinearities, geometric control is adopted, i.e., the attitude error is directly defined on tangent space of rotation group, thus better attitude tracking performance can be achieved. To deal with uncertainties, a comprehensive dynamic model is established with compound damping terms representing the main aerodynamic effects and all inertia parameters including mass, inertia tensor, and center of gravity. Then, integrated direct/indirect adaptive robust control is developed with adaptive compensation of aerodynamic damping and complete inertia, which is beneficial to improve tracking performance. As for the coupling, hierarchical control architecture is utilized to relax the strong coupling between attitude and translational subsystems. Moreover, the stability of the overall closed-loop hierarchical system, together with transient performance and final tracking accuracy, has been rigorously analyzed without time-scale separation assumption. Finally, comparative experiments demonstrate the better performance of the proposed method in terms of tracking accuracy and robustness.

An Improved Hybrid Control Scheme of a Switched Reluctance Motor for Torque Ripple Reduction

In this paper, we presents an improved hybrid control scheme based on model predictive torque control using linear active disturbance rejection control (LADRC) for the torque drive system of a 12/14 bearingless switched reluctance motor. The proposed approach can considerably reduce the torque ripple and enhance the anti-disturbance ability. First, a modified piecewise torque sharing function (TSF) is applied to reduce the torque ripple in the commutation interval. Second, model predictive control is introduced to further reduce the torque ripple caused by the hysteresis control. The selection of the optimal weighting factor is avoided, as the current value corresponding to different positions of each phase is determined by the TSF; thus, the cost function can be simplified. Then, a speed controller is designed by an enhanced LADRC for improved tracking performance and anti-disturbance ability, whereby a proportional gain of observation error and a time-varying function are investigated in extended-state observer. Finally, experiments are carried out to verify the effectiveness of the hybrid control scheme by comparison with cosine TSF function and conventional LADRC.

Marine Extended Target Tracking for Scanning Radar Data Using Correlation Filter and Bayes Filter Jointly

As the radar resolution improves, the extended structure of the targets in radar echoes can make a significant contribution to improving tracking performance, hence specific trackers need to be designed for these targets. However, traditional radar target tracking methods are mainly based on the accumulation of the target’s motion information, and the target’s appearance information is ignored. In this paper, a novel tracking algorithm that exploits both the appearance and motion information of a target is proposed to track a single extended target in maritime surveillance scenarios by incorporating the Bayesian motion state filter and the correlation appearance filter. The proposed algorithm consists of three modules. Firstly, a Bayesian module is utilized to accumulate the motion information of the target. Secondly, a correlation module is performed to capture the appearance features of the target. Finally, a fusion module is proposed to integrate the results of the former two modules according to the Maximum A Posteriori Criterion. In addition, a feedback structure is proposed to transfer the fusion results back to the former two modules to improve their stability. Besides, a scale adaptive strategy is presented to improve the tracker’s ability to cope with targets with varying shapes. In the end, the effectiveness of the proposed method is verified by measured radar data. The experimental results demonstrate that the proposed method achieves superior performance compared with other traditional algorithms, which simply focus on the target’s motion information. Moreover, this method is robust under complicated scenarios, such as clutter interference, target shape changing, and low signal-to-noise ratio (SNR).

Nonsingular Integral Terminal Sliding Mode Control for Resonant Frequency Tracking of Electromagnetic Acoustic Transducers (EMATs) Based on Fixed-Time Strategy.

Resonant frequency tracking control of electromagnetic acoustic transducers (EMATs) remains a challenge in terms of drifting working frequency and reduced conversion efficiency caused by working environment changes. This paper presents a fixed-time nonsingular integral terminal sliding mode (FT-NITSM) control strategy for resonant frequency tracking of EMATs to realize precise and high robustness resonant frequency tracking performance. Specifically, a FT-NITSM control method with fast convergence feature is developed and a resonant frequency tracking controller for EMATs is further designed to improve the convergence speed and tracking accuracy. Fixed time stability of the proposed frequency tracking control system is proved through Lyapunov function analysis. Moreover, numerical simulations demonstrate that the FT-NITSM control strategy can ensure precise tracking of the system's operating frequency to its natural resonant frequency in less than 3 s with a tracking error of less than 0.01 × 104 Hz. With the maximum overshoot variation between -20 and 20 and error range in -5 and 5° at the steady state, the FT-NITSM control strategy can ensure the control system impedance angle θ being consistent and eventually bounded. This study provides a toolbox for the resonant frequency tracking control and performance improvement of EMATs.

Model predictive tracking of spacecraft deorbit trajectories using drag modulation

A novel method is proposed for spacecraft deorbit trajectory tracking in low-Earth orbit using model predictive control to command changes in aerodynamic drag. Compared to previous approaches in the literature, the proposed method offers more direct control over actuator usage and is easier to tune. Most importantly, the proposed method is able to leverage the most recent and accurate knowledge of atmospheric density to improve tracking performance in a simple and computationally efficient way. Proof-of-concept simulations of the control strategy applied to targeted reentry demonstrate these advantages.