Robust Position Research Articles

Autonomous Full 3D Coverage Using an Aerial Vehicle, Performing Localization, Path Planning, and Navigation Towards Indoors Inventorying for the Logistics Domain

Over the last years, a rapid evolution of unmanned aerial vehicle (UAV) usage in various applications has been observed. Their use in indoor environments requires a precise perception of the surrounding area, immediate response to its changes, and, consequently, a robust position estimation. This paper provides an implementation of navigation algorithms for solving the problem of fast, reliable, and low-cost inventorying in the logistics industry. The drone localization is achieved with a particle filter algorithm that uses an array of distance sensors and an inertial measurement unit (IMU) sensor. Navigation is based on a proportional–integral–derivative (PID) position controller that ensures an obstacle-free path within the known 3D map. As for the full 3D coverage, an extraction of the targets and then their final succession towards optimal coverage is performed. Finally, a series of experiments are carried out to examine the robustness of the positioning system using different motion patterns and velocities. At the same time, various ways of traversing the environment are examined by using different configurations of the sensor that is used to perform the area coverage.

Robust attitude coordinated control for gravitational-wave detection spacecraft formation with large-scale communication delays

This paper concentrates on robust attitude control issues for spacecraft formation in gravitational-wave detection missions. Notably, with the large-scale communication delays brought by the million kilometers-scale inter-satellite distance, the exponential convergence of the closed-loop delayed system is firstly achieved via a new delay-dependent nominal attitude coordinated control scheme. Then, a finite-time disturbance observer is deployed to reconstruct the unknown lumped disturbance from internal uncertainties and the external environment. Meanwhile, an integral sliding manifold is embedded in the delay-dependent attitude controller for superior systems’ robustness. Finally, two simulation examples illustrate the feasibility and effectiveness of the proposed attitude control strategy for in-orbit gravitational-wave detection spacecraft systems.

DGVINS: Tightly Coupled Differential GNSS/Visual/Inertial for Robust Positioning Based on Optimization Approach

Abstract Due to the fragility of single-sensor positioning technology in complex scenarios, especially in complex urban areas, multi-sensor positioning technology is becoming increasingly popular. To further improve the robustness of the positioning system by fully utilizing the information from various sensors, this article proposes a differential-GNSS-visual-inertial navigation system (DGVINS) that tightly fuses differential global navigation satellite system (GNSS), vision and inertial information to provide accurate, robust and seamless position information for intelligent navigation applications. DGVINS effectively utilizes all sensor measurements within the factor graph optimization (FGO) framework. When using the carrier phase of GNSS, single-epoch ambiguity optimization is employed to prevent cycle slip detection and adapted to complex environments. We conducted experiments on public datasets with various features and compared the performance of simple differential-GNSS (DGNSS), DGNSS+Inertial, and the state-of-the-art GNSS-visual-inertial navigation systems (GVINS). We also compared the performance of different combinations of GNSS differential factors in various environments. Due to the superiority of differential GNSS and its appropriate integration with visual and inertial measurements, the experimental results demonstrate that DGVINS exhibits significant improvements in accuracy, stability, and continuity in both GNSS-challenged and vision-challenged environments.

FPGA-base object tracking: integrating deep learning and sensor fusion with Kalman filter

This research presents an integrated approach for object detection and tracking in autonomous perception systems, combining deep learning techniques for object detection with sensor fusion and field programmable gate array (FPGA-based) hardware implementation of the Kalman filter. This approach is suitable for applications like autonomous vehicles, robotics, and augmented reality. The study explores the seamless integration of pre-trained deep learning models, sensor data from a depth camera, real-sense D435, and FPGA-based Kalman filtering to achieve robust and accurate 3D position and 2D size estimation of tracked objects while maintaining low latency. The object detection and feature extraction are implemented on a central processing unit (CPU), and the Kalman filter sensor fusion with universal asynchronous receiver transmitter (UART) communication is implemented on a Basys 3 FPGA board that performs 8 times faster compared to the software approach. The experimental result provides the hardware resource utilization of about 29% of look-up tables, 6% of lookup table RAMs (LUTRAM), 15% of Flip-flops, 32% of Block-RAM, 38% of DSP blocks operating at 100 MHz, and 230400 baud rates for the UART. The whole FPGA design executes at 2.1 milliseconds, the Kalman filter executes at 240 microseconds, and the UART at 1.86 milliseconds.

Human–Robot Cooperation Control Strategy Design Based on Trajectory Deformation Algorithm and Dynamic Movement Primitives for Lower Limb Rehabilitation Robots

Compliant physical interactions, interactive learning, and robust position control are crucial to improving the effectiveness and safety of rehabilitation robots. This paper proposes a human–robot cooperation control strategy (HRCCS) for lower limb rehabilitation robots. The high-level trajectory planner of the HRCCS consists of a trajectory generator, a trajectory learner, a desired trajectory predictor, and a soft saturation function. The trajectory planner can predict and generate a smooth desired trajectory through physical human–robot interaction (pHRI) in a restricted joint space and can learn the desired trajectory using the locally weighted regression method. Moreover, a triple-step controller was designed to be the low-level position controller of the HRCCS to ensure that each joint tracks the desired trajectory. A nonlinear disturbance observer is used to observe and compensate for total disturbances. The radial basis function neural networks (RBFNN) approximation law and robust term are adopted to compensate for observation errors. The simulation results indicate that the HRCCS is robust and can achieve compliant pHRI and interactive trajectory learning. Therefore, the HRCCS has the potential to be used in rehabilitation robots and other fields involving pHRI.

A Multi-Level Robust Positioning Method for Three-Dimensional Ground Penetrating Radar (3D GPR) Road Underground Imaging in Dense Urban Areas

Three-Dimensional Ground Penetrating Radar (3D GPR) detects subsurface targets non-destructively, rapidly, and continuously. The complex environment around urban roads affects the positioning accuracy of 3D GPR. The positioning accuracy directly affects the data quality, as inaccurate positioning can lead to distortion and misalignment of 3D GPR data. This paper proposed a multi-level robust positioning method to improve the positioning accuracy of 3D GPR in dense urban areas in order to obtain more accurate underground data. In environments with good GNSS signals, fast and high-precision positioning can be achieved based on GNSS data using differential GNSS technology; in scenes with weak GNSS signals, high-precision positioning of subsurface data can be achieved by using GNSS and IMU as well as using GNSS/INS tightly coupled solution technology; in scenes with no GNSS signals, SLAM technology is used for positioning based on INS data and 3D point cloud data. In summary, this method ensures a positioning accuracy of 3D GPR better than 10 cm and high-quality 3D images of underground urban roads in any environment. This provides data support for urban road underground structure surveys and has broad application prospects in underground disease detection and prevention.

Ratio Analysis of Axis Bank

This report presents a thorough examination of AXIS Bank's financial performance utilizing ratio analysis as the primary tool. Ratio analysis offers a quantitative method to assess the bank's financial health and operational efficiency by comparing key financial metrics over time and against industry benchmarks. The analysis begins by evaluating AXIS Bank's current ratio, which measures the proportion of current assets to current liabilities. A higher current ratio indicates a greater ability to cover short-term obligations, suggesting a healthy liquidity position. Conversely, a lower ratio may raise concerns about liquidity risk. In the case of AXIS Bank, the current ratio analysis reveals a robust position, indicating a prudent allocation of resources towards current assets. Furthermore, the report explores the quick ratio, also known as the acid-test ratio, which provides a more stringent assessment of liquidity by excluding inventory from current assets. This ratio helps determine the bank's ability to meet short-term obligations without relying on inventory liquidation. Comparing the quick ratio with the current ratio provides additional insights into the composition of AXIS Bank's current assets and its liquidity management practices. Additionally, the analysis considers the fixed/worth ratio, which assesses the proportion of fixed assets financed by owner's equity. A higher fixed/worth ratio suggests greater reliance on equity financing for fixed asset investments, indicating a lower financial risk. Conversely, a lower ratio may signal higher leverage and potential financial vulnerability. Evaluating this ratio provides valuable insights into AXIS Bank's capital structure and risk management strategies. Furthermore, the report examines the cash flow statement to assess the bank's cash generation and utilization across operating, investing, and financing activities. Analyzing cash flow patterns helps identify trends in cash generation, liquidity management, and investment decisions. In the case of AXIS Bank, the analysis reveals a positive trend in cash generated from operating and financing activities, indicating healthy cash inflows. However, negative cash flow from investing activities warrants further investigation into the bank's investment strategies and capital expenditure decisions. Overall, this comprehensive analysis of AXIS Bank's financial ratios and cash flow dynamics provides valuable insights into the bank's financial position, liquidity management, and operational efficiency. The findings offer a basis for strategic decision-making and recommendations to optimize performance and ensure sustained growth and stability..

Multisource‐multitarget cooperative positioning using probability hypothesis density filter in internet of vehicles

AbstractAccurate positioning of intelligent connected vehicle (ICV) is a key element for the development of cooperative intelligent transportation system. In vehicular networks, lots of state‐related measurements, especially the mutual measurements between ICVs, are shared. It is an advisable strategy to fuse these measurements for a more robust positioning. In this context, an innovative framework, referred to as multisource‐multitarget cooperative positioning (MMCP) is presented. In MMCP, ICVs are local information source, that upload both the states of ICVs estimated by on‐board sensors and the relative vectors between surrounding objects and vehicles to a fusion centre. In the fusion centre, ICVs are selected as the global targets, and the relative vectors are converted into global measurements. Then, the MMCP is modelled into a multi‐target tracking problem with specific targets. This paper proposes a low complexity Gaussian mixture probability hypothesis density (GM‐PHD‐LC) filter to match and fuse the global measurements to further improve the estimation of ICVs. The evaluation results show that our GM‐PHD‐LC can provide 10 Hz positioning services in urban area, and significantly improve the positioning accuracy compared to the standalone global navigation satellite system.

Robust Attitude Tracking Control Based on Adaptive Dynamic Programming for Flexible Dumbbell-Shaped Spacecraft

Robust Attitude Tracking Control Based on Adaptive Dynamic Programming for Flexible Dumbbell-Shaped Spacecraft

Design and real-time implementation of a robust backstepping non-singular integral terminal sliding mode attitude control for a quadrotor aircraft

This paper concerns the problem of robust attitude control of a quadrotor aircraft subjected to modeling inaccuracies and wind disturbances. First, the model dynamics and state space representation of the quadrotor system are designed by considering the impact of disturbances and uncertainties. Then, a novel robust backstepping non-singular integral terminal sliding mode control (BNITSMC) is synthesized for the quadrotor attitude with the aim of enhancing the tracking accuracy. To strengthen the system’s robustness and guarantee the reachability of the sliding surface while reducing chattering, the supertwisting algorithm (ST) is used. Closed-loop stability and state convergence are guaranteed via the Lyapunov theory. Some experimental flight tests have been executed to validate the workability of the developed flight control. The obtained results prove that the recommended technique can provide improved performance in terms of stabilization and tracking precision.

An LMI-based robust state-feedback controller design for the position control of a knee rehabilitation exoskeleton robot: Comparative analysis

Rehabilitation exoskeleton robots play a crucial role in restoring functional lower limb movements for individuals with locomotor disorders. Numerous research studies have concentrated on adapting the control of these rehabilitation robotic systems. In this study, we investigate an affine state-feedback control law for robust position control of a knee exoskeleton robot, taking into account its nonlinear dynamic model that includes solid and viscous frictions. To ensure robust stabilization, we employ the Lyapunov approach and propose three methods to establish stability conditions using the Schur complement, the Young inequality, the matrix inversion lemma, and the S-procedure lemma. These conditions are formulated as Linear Matrix Inequalities (LMIs). Furthermore, we conduct a comprehensive comparison among these methods to determine the most efficient approach. At the end of this work, we present simulation results to validate the developed LMI conditions and demonstrate the effectiveness of the adopted control law in achieving robust position control of the knee exoskeleton robot.

Designing a fixed-time observer-based adaptive non-singular sliding mode controller for flexible spacecraft

This paper investigates the stabilization problem with a fixed-time approach for a flexible spacecraft subject to vibrations of flexible modes, unknown bounded disturbance, and inherent uncertainty. To estimate the modal variables of a flexible spacecraft which are often unmeasurable in practice, an observer with guaranteed fixed-time convergence is designed. Using the estimated modal variables, a fixed-time non-singular sliding mode controller is designed so that the desired attitude can be reached before a pre-specified time threshold regardless of the spacecraft's initial attitude. By incorporating the estimated modal variables in the control design, significant reduction in the steady-state error of the system response is achieved. The proposed control system is further enhanced with an adaptive law to increase robustness against unknown external disturbances and uncertainties. Stability analysis based on Lyapunov theory guarantees the convergence of observer estimation error and spacecraft attitude error to a pre-determined set before a fixed threshold. Simulation results validate the promising performance of the proposed control system, highlighting its effectiveness in achieving accurate and robust attitude control for flexible spacecraft.

Attitude maneuver planning and robust tracking control for flexible satellite

Abstract In this paper, we investigate the attitude manoeuver planning and tracking control of the flexible satellite equipped with a coilable mast. Due to its flexible beamlike structure, the coilable mast experiences bending and torsional modal vibrations in multi-direction. The complex nonlinear coupling and other external disturbances significantly impact the achievement of high-precision attitude control. To overcome these challenges, a robust attitude tracking controller is proposed for easy implementation by the Attitude Determination and Control System (ADCS). The controller consists of a disturbance compensator, feedforward controller and output feedback controller. The compensator, based on a Nonlinear Disturbance Observer (NDO), effectively compensates for the cluster disturbances caused by vibrations, environmental factors and parameter perturbations. The feedforward controller tracks the desired path in the nominal satellite model. Furthermore, the output feedback controller enables large-angle manoeuver control of the satellite and evaluates the suppression effect of the controlled output on the observation error of cluster disturbances used the ${L_2}$ -gain. Simulation results demonstrate that the proposed controller successfully achieves high-precision attitude tracking control during large-angle manoeuvering.

Robust attitude tracking control for a variable‐pitch quadrotor with uncertainties

SummaryVariable‐pitch quadrotors have demonstrated their capability to enhance control performance and overcome limitations compared to conventional fixed‐pitch quadrotors, while still facing challenges such as nonlinearity, couplings, uncertainties and so on. This article aims to address the robust attitude control problem of variable‐pitch quadrotors afflicted with parametric uncertainties and unpredictable external disturbances. Initially, a practical actuator allocation strategy for the motor‐propeller is proposed, followed by the development of a nonlinear robust attitude tracking controller capable of addressing all uncertainties. The effectiveness of the proposed controller in ensuring exponential stability of the closed‐loop system, thereby driving the attitude tracking error to a small range within a finite time, has been substantiated. To evaluate the control performance, a hardware‐in‐the‐loop testbed is constructed to conduct real‐time experiments. The experimental results from attitude control case, comparative case, and position control case demonstrate that the designed motor‐propeller allocation method and robust controller are effective in achieving a fast response and excellent control performance.

Factor graph-based PPP-RTK for accurate and robust positioning in urban environments

Factor graph-based PPP-RTK for accurate and robust positioning in urban environments

Preliminary results of scintillation monitoring at KLEF-Guntur low latitude station using GNSS software defined radio

Abstract Global Navigation Satellite Systems (GNSS) have become an integral part of modern life, supporting various applications, from precise positioning and navigation to timing and synchronization. However, GNSS signals are vulnerable to natural interferences including various atmospheric disturbances, with ionospheric scintillations being a significant challenge. Ionospheric scintillations, caused by irregularities in the Earth’s ionosphere, introduce rapid fluctuations in the amplitude and phase of GNSS signals. These fluctuations can severely degrade the accuracy and reliability of GNSS receivers, leading to positioning errors and navigation failures. Hence, it is crucial to develop effective mitigation strategies. One of the promising approaches to mitigate ionospheric scintillations is the utilization of Software Defined Radio (SDR) technology in GNSS receivers. SDR allows for real-time adaptation to changing signal conditions, enabling the receiver to detect scintillations and adjust its signal processing accordingly. This adaptability enhances the receiver’s stability against ionospheric disturbances, ensuring more robust and accurate positioning and navigation. In this paper, preliminary results of GNSS SDR (Make: iP-Solutions, Japan) installed at Koneru Lakshmaiah Education Foundation (KLEF), Vaddeswaram (Guntur) (16.44° N, 80.62° E) are presented. Amplitude scintillation index (S4) variations for different PRNs and subsequent positioning results are interpreted from April to September 2023. The results are compared and validated with those of the co-located Novatel GNSS receiver and NAVIC receiver. Most of the S4 variations correlate well with the S4 values from the Novatel and NAVIC receivers. S4 observations from the Septentrio receiver at Daytona Beach (Florida) are also presented. The results of SDR will be extended further for the development of scintillation mitigation algorithms. We plan to install an SDR and employ similar mitigation strategy at this location in the near future.

Enhanced Seamless Indoor–Outdoor Tracking Using Time Series of GNSS Positioning Errors

The seamless integration of indoor and outdoor positioning has gained considerable attention due to its practical implications in various fields. This paper presents an innovative approach aimed at detecting and delineating outdoor, indoor, and transition areas using a time series analysis of Global Navigation Satellite System (GNSS) error statistics. By leveraging this contextual understanding, the decision-making process between GNSS-based and Visual-Inertial Odometry (VIO) for trajectory estimation is refined, enabling a more robust and accurate positioning. The methodology involves three key steps: proposing the division of our context environment into a set of areas (indoor, outdoor, and transition), exploring two methodologies for the classification of space based on a time series of GNSS error statistics, and refining the trajectory estimation strategy based on contextual knowledge. Real data across diverse scenarios validate the approach, yielding trajectory estimations with accuracy consistently below 10 m.

Research on Robust Adaptive RTK Positioning of Low-Cost Smart Terminals.

The performance of low-cost smart terminals is limited by the performance of their low-cost Global Navigation Satellite System (GNSS) hardware and chips, as well as by the impact of complex urban environments, which affect the positioning accuracy and stability of GNSS services. To this end, this paper proposes a robust adaptive Kalman filter for different environments that can be applied after data preprocessing. Based on the Kalman filter algorithm, a robust estimation approach is introduced into real-time kinematic (RTK) positioning to make judgments on the abnormal observation values of low-cost smart terminals, which amplifies the variance and covariance of the outlier observation equation, and reduces the impact of outliers on positioning performance. The Institute of Geodesy and Geophysics III (IGG III) function is used for regulation purposes, where prior information is modified and refreshed using the equivalent weight matrix and adaptive factors, thus reducing the impact of system model errors on system state estimation results. In addition, a robust factor is defined to adjust positioning deviation weighting between the pre- and post-test robust estimates. The experimental results show that after robust RTK positioning in the static experiments, the overall improvement in positioning accuracies of the Xiaomi 8, Huawei P40, Huawei mate40, and low-cost M8 receiver reached 29.6%, 31.3%, 32.1%, and 30.7%, respectively. Similarly, after applying the proposed robust method in the dynamic experiments, the overall positioning accuracies of the Xiaomi 8, Huawei P40, Huawei mate40, and the low-cost M8 receiver improved by 28.3%, 32.9%, 35.4%, and 26.2%, respectively. The experimental results reveal that an excellent positioning effect of a smartphone is positively correlated with robust RTK positioning performance. However, it is worth noting that when the positioning accuracy reaches a high level, such as the positioning results achieved using low-cost receivers, the robustness performance shows a relatively decreasing trend. This finding suggests that under the condition of high positioning accuracy, the sensitivity of specific positioning equipment to interference sources may increase, resulting in a decline in the effect of robust RTK positioning.

Three‐dimensional path following control of underactuated autonomous underwater vehicles with nonzero roll dynamics: A novel line‐of‐sight‐guided approach

AbstractMost existing path following control approaches for underactuated Autonomous underwater vehicles (AUVs) assumed that the roll angle can be passively stabilized at zero, and simplified the problem to the 5‐degree‐of‐freedom model. However, in practice, the roll motion suffers from undesirable oscillations induced by environmental disturbances, unbalanced propeller torque, etc. To preserve the stability of the system under nonzero roll dynamics, this paper proposes a novel line‐of‐sight‐ (LOS) guided path following control approach. Firstly, the spatial path following error dynamics model is derived to comprehensively reveal the negative effect of roll dynamics. Secondly, an improved LOS guidance law is designed to compensate for the nonzero roll angle. A salient feature is that the proposed guidance law can be applied to the underwater vehicle no matter it is underactuated in the roll motion or not. Thirdly, by assigning the guidance angles to the desired attitude angles, a robust attitude tracking controller is developed, in which the adaptive estimation technique is employed to handle the lumped uncertainties. The tanh‐based saturation function is incorporated such that the resultant control signals are inherently bounded. The closed‐loop path following and attitude tracking errors are proved to be globally asymptotically stable at the origin. Comparative simulation results are provided to substantiate the effectiveness and superiority of the proposed method.

Robust Positioning Estimation for Underwater Acoustics Targets with Use of Multi-Particle Swarm Optimization

With the extensive application of sensor technology in scientific ocean research, ocean resource exploration, underwater engineering construction, and other fields, underwater target positioning technology has become an important support for the ocean field. This paper proposes a robust positioning algorithm that combines the disadvantages of distributed estimation and particle swarm optimization, which can solve the large localization error problem caused by uncertainties in underwater acoustic communication and sampling processes. Considering the presence of ranging anomalies and sampling packet loss in underwater acoustic measurements, a weighted coupling filling method is used to correct the outliers in an underwater acoustic ranging signal. Based on the mapping model from the element array to the underwater acoustic responder, an unconstrained optimization algorithm for one-time localization estimation of underwater acoustic targets was established. Based on the one-time localization estimation results of underwater acoustic targets, an improved multi-particle swarm optimization estimation based on interactive search is proposed, which improves the accuracy of underwater target localization. The numerical results show that the positioning accuracy of the proposed algorithm can be effectively enhanced in cases of distance measurement errors and azimuth measurement errors. Compared with the positioning error before optimization, the positioning error can be reduced after optimization. Additionally, the experiment was carried out in the underwater environment of Hangzhou Qiandao Lake, which verified the positioning performance of the proposed algorithm.