Robust Position Research Articles

Tightly Coupled LIDAR/IMU/UWB Fusion via Resilient Factor Graph for Quadruped Robot Positioning

Continuous accurate positioning in global navigation satellite system (GNSS)-denied environments is essential for robot navigation. Significant advances have been made with light detection and ranging (LiDAR)-inertial measurement unit (IMU) techniques, especially in challenging environments with varying lighting and other complexities. However, the LiDAR/IMU method relies on a recursive positioning principle, resulting in the gradual accumulation and dispersion of errors over time. To address these challenges, this study proposes a tightly coupled LiDAR/IMU/UWB fusion approach that integrates an ultra-wideband (UWB) positioning technique. First, a lightweight point cloud segmentation and constraint algorithm is designed to minimize elevation errors and reduce computational demands. Second, a multi-decision non-line-of-sight (NLOS) recognition module using information entropy is employed to mitigate NLOS errors. Finally, a tightly coupled framework via a resilient mechanism is proposed to achieve reliable position estimation for quadruped robots. Experimental results demonstrate that our system provides robust positioning results even in LiDAR-limited and NLOS conditions, maintaining low time costs.

A Novel Robust Position Integration Optimization-Based Alignment Method for In-Flight Coarse Alignment.

In-flight alignment is a critical milestone for inertial navigation system/global navigation satellite system (INS/GNSS) applications in unmanned aerial vehicles (UAVs). The traditional position integration formula for in-flight coarse alignment requires the GNSS velocity data to be valid throughout the alignment period, which greatly limits the engineering applicability of the method. In this paper, a new robust position integration optimization-based alignment (OBA) method for in-flight coarse alignment is presented to solve the problem of in-flight alignment under a prolonged ineffective GNSS. In this methodology, to achieve a higher alignment accuracy in case the GNSS is not effective throughout the alignment period, the integration of GNSS velocity into the local-level navigation frame is replaced by the GNSS position in the Earth-centered, Earth-fixed frame, which avoids the need for complete GNSS velocity data. The simulation and flight test results show that the new robust position integration method proposed in this paper achieves higher stability and robustness than the conventional position integration OBA method and can achieve an alignment accuracy of 0.2° even when the GNSS is partially time-invalidated. Thus, this greatly extends the application of the OBA method for in-flight alignment.

Real-time localization with probabilistic maps and unscented kalman filtering: a dynamic sensor fusion approach

This paper presents a robust position estimation technique for autonomous vehicles navigating urban and highway environments. It employs a probabilistic framework, integrating Monte Carlo simulation and Bayesian filtering via particle filters to represent position estimates beyond Gaussian distributions. To improve accuracy, Unscented Kalman Filtering (UKF) is used to fuse radar and lidar data, facilitating the detection of static, pole-like objects. These objects are matched with landmarks from a detailed reference map generated through 3D lidar scans. The proposed method addresses both lateral and longitudinal localisation challenges, ensuring precise vehicle positioning. Comprehensive simulation tests validate the system's effectiveness, demonstrating reliable performance across various driving conditions.

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.

Improving Indoor WiFi Localization by Using Machine Learning Techniques

Accurate and robust positioning has become increasingly essential for emerging applications and services. While GPS (global positioning system) is widely used for outdoor environments, indoor positioning remains a challenging task. This paper presents a novel architecture for indoor positioning, leveraging machine learning techniques and a divide-and-conquer strategy to achieve low error estimates. The proposed method achieves an MAE (mean absolute error) of approximately 1 m for latitude and longitude. Our approach provides a precise and practical solution for indoor positioning. Additionally, some insights on the best machine learning techniques for these tasks are also envisaged.

Robust position regulation of a 2-DOF experimental helicopter using higher order super twisting sliding mode control

This study presents a third-order super twisting sliding mode control algorithm for robust position regulation of a two-degree-of-freedom (2-DOF) experimental helicopter. The proposed approach explores high-order sliding mode control, offering advantages such as finite-time convergence and continuous control signals. The controller is implemented and validated on the Quanser Aero 2 platform, an inherently unstable and nonlinear system that presents significant challenges for control design including cross-coupling effects, model uncertainties, and external disturbances. Extensive simulations and experimental results are presented to validate the efficacy and robustness of the proposed control strategy. They highlight the controller's ability to achieve precise position control while significantly mitigating the chattering phenomenon, a common drawback associated with traditional sliding mode control. This study contributes to the field of nonlinear control systems by demonstrating the potential of high-order sliding mode control in managing nonlinear, coupled dynamic systems. The successful implementation on a real-world platform underscores the practical applicability of the proposed method in aerospace and robotics applications.

Fractional‐order fixed‐time sliding mode control of robotic manipulators with robust exact differentiator

AbstractThis paper introduces a robust position tracking control structure of exoskeleton robots (ERs). An open environment and body flexibility are the main challenges in ER control system design. First, a fractional‐order nonsingular terminal sliding mode (FONTSM) controller is proposed for a robotic manipulator with uncertainty, where lumped unknown dynamics are computed by time‐delay estimation (TDE). Secondly, a robust exact differentiator (RED) is employed as the output feedback observer for the velocity sensorless robotic system. Thirdly, the stability of the FONTSM control system is demonstrated. It is proved that the system error can move to the specified value along the sliding mode surface at a fixed time and can reach the sliding mode surface after a finite time. Finally, simulations on a 2‐DOF robotic manipulator show the effectiveness of the fixed‐time control strategies. When a combined trigonometric function disturbance is applied, the joint angle error is reduced to 1% by the model‐free controller based on output feedback within approximately 1 s.

Attitude determination for multirotor aerial vehicles using a prescribed‐time super‐twisting algorithm

AbstractThis paper is concerned with the prescribed‐time robust attitude determination (AD) of multirotor aerial vehicles (MAVs) using vector measurements from the local magnetic field and local gravity. To address this problem, we first introduce a novel modified super‐twisting algorithm endowed with the prescribed‐time convergence property. The state of the proposed algorithm is governed by an unbounded time‐varying gain up to the prescribed settling time (PST) and by a function after that. Therefore, after the PST, the new algorithm coincides with the conventional super‐twisting, thus showing robust stability at the origin. This prescribed‐time super‐twisting algorithm (PTSTA) is then applied to the formulation of a three‐stage gyro‐free attitude determination method for MAVs. In the first stage, the classical QUEST algorithm is used to compute a Wahba‐optimal attitude estimate from the vector measurements. In the second stage, the PTSTA is employed in the formulation of a robust state estimator that provides estimates of the attitude Gibbs vector and its rate. Finally, in the third stage, these state estimates as well as the attitude kinematic equation are immediately used to compute the MAV angular velocity. The proposed robust prescribed‐time gyro‐free AD method is evaluated numerically, showing invariance with respect to disturbance and model uncertainty.

Predefined-time sliding mode position and attitude coupling control for in-cabin robots on space stations

As space science advances, the significance of space robotics research escalates, particularly in control methodologies. Addressing the work efficiency requirements during the mission of the in-cabin robot, it is necessary to develop a fast and robust position and attitude coupling controller for in-cabin robots. This paper presents a predefined-time sliding mode position and attitude coupling control method, employing the SE(3) frame description. The first, the dynamic model of the in-cabin robot is developed within the SE(3) framework. Then, the predefined-time position and attitude coupling sliding mode controller is designed with a sliding surface based on the dynamic error model, and a hyperbolic tangent function is used to suppress system chattering. In addition, the stability of the proposed control method is rigorously confirmed using the Lyapunov theorem, ensuring that the convergence time is accurately predefined. Finally, simulation results are provided to demonstrate the effectiveness of this novel control strategy. A comparative analysis with fixed-time sliding mode control highlights its superior performance.

Strategic Resilience and Financial Analysis of Merck & Co., Inc.: Navigating Challenges and Opportunities in the Pharmaceutical Industry

This study provides a comprehensive analysis of Merck & Co., Inc. (MRK), a prominent player in the global pharmaceutical industry, known for its significant contributions to healthcare through the development of innovative drugs and vaccines. Merck's core focus areas include pharmaceuticals, vaccines, animal health, and healthcare services, with its flagship oncology drug Keytruda being a major revenue contributor. The company faces challenges such as regulatory changes in drug pricing and competition from biosimilars and generics. To mitigate these challenges, Merck has initiated restructuring plans, including the 2024 Restructuring Plan, to optimize operational efficiency and achieve significant cost savings. The paper also presents a comparison of Merck's financial performance with industry peers - Johnson & Johnson, AbbVie and Bristol-Myers Squibb - and evaluates liquidity, solvency and profitability metrics. Despite certain challenges, Merck's robust market positioning, strategic acquisitions, and active involvement in the development of COVID-19 vaccines and therapeutics underscore its potential for sustainable growth. Overall, the study concludes that Merck's strategic investments, coupled with its strong financial position and proactive restructuring efforts, make it an attractive investment prospect, particularly in its core therapeutic areas of oncology and immunology. This paper suggests that future research could delve deeper into the long-term impact of R&D investments, the intricacies of restructuring plans, and evolving market trends to provide a more nuanced understanding of Merck's strategic planning.

Robust neuro-adaptive command-filtered back-stepping fault-tolerant control of satellite using composite learning

This study proposes an adaptive neural command-filtered backstepping control system for precise and fast attitude control of a satellite considering different uncertain dynamics. Mission success crucially depends on robust attitude control resilient to model uncertainties, unmodeled dynamics, external disturbances, and actuator faults. The proposed approach synergistically combines a neural network for handling model uncertainties, unmodeled dynamics, and actuator faults, with a disturbance observer for compensating external disturbances and neural network estimation errors. The command-filtered backstepping technique avoids the explosion of complexity inherent to traditional backstepping, while integrating integral action into the design results in the elimination of the steady-state tracking error. Besides, a composite learning method optimizes the update laws for the neural network and disturbance observer weights, enhancing control performance. Despite the presence of uncertainties, the closed-loop system stability is guaranteed by the Lyapunov stability theorem. Simulation results demonstrate the proposed controller’s ability to handle severe actuator faults, unmodeled dynamics, and measurement noise without requiring explicit fault detection and isolation schemes.

Development and validation of FinTox: A new screening tool to assess cancer-related financial toxicity.

This study aimed to develop and validate FinTox, a concise tool for screening and managing financial toxicity in oncology settings. Development involved qualitative interviews with healthcare providers and patients, and feedback from a 7-member expert panel resulting in a 5-item measure that evaluates financial strain, psychological responses, and care modifications. Psychometric evaluations examined factor structure, internal consistency, test-retest reliability, and concurrent and convergent validity. Associations between FinTox scores and sociodemographic/medical factors were also analyzed using univariate and multivariable regression models. Twelve healthcare providers and 20 patients were interviewed, and 268 patients (69.8% female, 47.4% non-Hispanic White) completed surveys including FinTox, the Comprehensive Score for Financial Toxicity (COST), health-related quality of life (HRQOL) measures, and sociodemographic questions. FinTox demonstrated excellent internal consistency (Cronbach's alpha = 0.90) and test-retest reliability (ICC = 0.95). Significant correlations with the COST (r = -0.62, p < 0.001) and HRQOL measures corroborated content and convergent validity. Diagnostic accuracy was evidenced by a sensitivity of 72.3%, specificity of 85.2%, positive predictive value of 83.2%, and negative predictive value of 70.3%. Higher FinTox scores were also associated with receiving care at a safety-net hospital, Black race, household income <600% of the federal poverty level, and Stage 4 cancer. FinTox's robust psychometric properties and diagnostic accuracy position it as a reliable tool for detecting financial toxicity. Future research should evaluate its responsiveness to changes over time and integration into clinical workflows.

Robust Positioning of Unmanned Vehicles with the Application of Satellite Measurements and Digital Path Model Data

Robust Positioning of Unmanned Vehicles with the Application of Satellite Measurements and Digital Path Model Data

Robust Attitude Estimation for Low-Dynamic Vehicles Based on MEMS-IMU and External Acceleration Compensation.

Attitude determination based on a micro-electro-mechanical system inertial measurement unit (MEMS-IMU) has attracted extensive attention. The non-gravitational components of the MEMS-IMU have a significant effect on the accuracy of attitude estimation. To improve the attitude estimation of low-dynamic vehicles under uneven soil conditions or vibrations, a robust Kalman filter (RKF) was developed and tested in this paper, where the noise covariance was adaptively changed to compensate for the external acceleration of the vehicle. The state model for MEMS-IMU attitude estimation was initially constructed using a simplified direction cosine matrix. Subsequently, the variance of unmodeled external acceleration was estimated online based on filtering innovations of different window lengths, where the acceleration disturbance was addressed by tradeoffs in time-delay and prescribed computation cost. The effectiveness of the RKF was validated through experiments using a three-axis turntable, an automatic vehicle, and a tractor tillage test. The turntable experiment demonstrated that the angle result of the RKF was 0.051° in terms of root mean square error (RMSE), showing improvements of 65.5% and 29.2% over a conventional KF and MTi-300, respectively. The dynamic attitude estimation of the automatic vehicle showed that the RKF achieves smoother pitch angles than the KF when the vehicle passes over speed bumps at different speeds; the RMSE of pitch was reduced from 0.875° to 0.460° and presented a similar attitude trend to the MTi-300. The tractor tillage test indicated that the RMSE of plough pitch was improved from 0.493° with the KF to 0.259° with the RKF, an enhancement of approximately 47.5%, illustrating the superiority of the RKF in suppressing the external acceleration disturbances of IMU-based attitude estimation.

Robust Attitude and Positioning Alignment Methods for SINS/DVL Integration Based on Sliding Window Improvements

Robust Attitude and Positioning Alignment Methods for SINS/DVL Integration Based on Sliding Window Improvements

Design of a logistics warehouse robot positioning and recognition model based on improved EKF and calibration algorithm

Automatic guided vehicles for logistics warehousing are a key link in the construction of intelligent logistics. To improve the positioning accuracy of warehouse robots, we designed an advanced extended Kalman filter method integrating multiple synchronous positioning techniques and map construction methods, and completed the calibration and detection of pallets based on color image information. The results revealed that the proposed multi-innovation enhanced model achieved minimum relative rotation and absolute trajectory errors of 0.13 and 0.09, outperforming existing models. It showcased excellent mapping fidelity and integrity (above 0.9) across various datasets, with a high loop detection success rate (0.91) enhancing map precision. The tray fusion detection algorithm's AUC (area under the curve) reached 0.92, reflecting a balanced accuracy-recall tradeoff. This research offers robust positioning and mapping capabilities in logistics warehousing environments, effectively identifying errors and ensuring pallet accuracy. The detection error and accuracy of this method are better than the other three models, with the lowest average absolute error of 0.32 and the lowest root-mean-square error of 0.27, and the overall error in the detection of pallets is small. The findings provide strong theoretical backing and technical support for advancing intelligent logistics warehousing technology. Precise positioning and identification capabilities enable logistics and warehousing robots to accurately and quickly complete tasks such as access, handling and sorting of goods, greatly improving the efficiency of warehousing operations, promoting the digital transformation and intelligent development of the logistics and warehousing industry, and improving the competitiveness of the industry and the level of service.

Tightly coupled visual-inertial fusion with image enhancement for robust positioning

Abstract Traditional vision-based inertial odometry (VIO) suffers from significant visual degradation, which substantially impacts state estimation in challenging lighting environments. Thermal imaging cameras capture images based on the thermal radiation of objects, rendering them impervious to lighting variations. However, integrating thermal infrared data into conventional visual odometry poses challenges due to its low texture, poor contrast, and high noise levels. In this paper, we propose a tightly coupled approach that seamlessly integrates information from visible light cameras, thermal imaging cameras, and inertial measurement units (IMUs). First, we employ adaptive bilateral filtering and Sobel gradient enhancement to smooth infrared images, thereby reducing noise and enhancing edge contrast. Second, we leverage the Sage-Husa adaptive filter in conjunction with iterative Kalman filtering (IEKF) to effectively mitigate the impact of non-Gaussian noise on the system. Finally, we conduct comprehensive evaluations of the proposed system using both open datasets and real-world experiments across four distinct scenarios: normal lighting, low-light conditions, low-light conditions with camera shake, and challenging lighting environments. Comparative analysis reveals that our method outperforms IEKF, yielding a reduction in localization error measured by root mean square error (RMSE) by 58.69%, 57.24%, 60.23%, and 30.87% in these respective scenarios.

Sensing and Control Integration for Thrust Vectoring in Heavy UAVs: Real-World Implementation and Performance Analysis

Unmanned Aerial Vehicles (UAVs) have garnered significant attention among researchers due to their versatility in diverse missions and resilience in challenging conditions. However, electric UAVs often suffer from limited flight autonomy, necessitating the exploration of alternative power sources such as thermal engines. On the other hand, managing thermal engines introduces complexities and internal uncertainties into the system. In this paper, an Adaptive Robust attitude controller (ARAC) is proposed to address these challenges by drawing inspiration from helicopter solutions while minimizing mechanical intricacies. Specifically, the designed algorithm employs Thrust Vector Control (TVC) for an industrial heavy Multi-Ducted Fan (MDF), known for its superior static stability compared to conventional ducted fans. Subsequently, an integrated flap vanes system is positioned at the exhaust of the ducts for precise attitude control, effectively removing unwanted yaw moments associated with traditional propellers. This research builds on prior authors’ works to establish a proper mathematical and aerodynamic model. Also, using former simulation results to conduct real flight experiments aimed at enhancing TVC functionality. The findings highlight the effectiveness of this approach for heavy UAV applications. It is worth noting that the practical value of this research lies in its potential to significantly extend flight autonomy supplied by thermal engines and improve the resilience of UAVs in challenging real-world missions. This is particularly achievable provided that the design of flap vanes aligns closely with the dimensions of the duct system, offering a promising solution to a critical engineering challenge in the field of UAV technology.

Dynamic Modeling and Control of a 4-Wheel Narrow Tilting Vehicle

The automotive industries currently face challenges such as emission limits, traffic congestion, and limited parking, which have prompted shifts in consumer preferences and modern passenger vehicle requirements towards compact vehicles. However, given the inherent limited width of compact vehicles, the potential risk of vehicle rollover is greater than that of regular vehicles. This paper addresses the safety concerns associated with vehicle rollover, focusing on narrow tilting vehicles (NTVs). Quantifying stability involves numerical indicators such as the lateral load transfer ratio (LTR). Additionally, a unique approach is taken by applying ZMP (zero moment point), commonly used in the robotics field, as an indicator of vehicle stability. Effective roll control requires a detailed analysis of the vehicle’s characteristic model and the derivation of lateral and roll dynamics. The paper presents the detailed roll dynamics of an NTV with a MacPherson strut-type suspension. A stability-enhancing method is proposed using a cascade structure based on the internal robust position controller and outer roll stability controller, addressing challenges posed by disturbances. Experimental verification using Simscape Multibody and CarSim validates the dynamic model and controller’s effectiveness, ensuring the reliability of the proposed tilting control for NTVs in practical scenarios.

Robust Indoor Positioning of Automated Guided Vehicles in Internet of Things Networks With Deep Convolution Neural Network Considering Adversarial Attacks

Robust Indoor Positioning of Automated Guided Vehicles in Internet of Things Networks With Deep Convolution Neural Network Considering Adversarial Attacks