Position Estimation Research Articles

Rotor Position Estimation Method for Permanent Magnet Synchronous Motor Based on High-Order Extended Kalman Filter

To address the issue of decreased rotor position estimation accuracy in permanent magnet synchronous motors (PMSMs) caused by linearization rounding errors in the extended Kalman filter (EKF), this paper proposes a rotor position estimation method for PMSMs based on higher-order extended Kalman filtering. This method relies on the state-space equations of a PMSM in a stationary coordinate system and establishes a higher-order Taylor series expansion based on the least squares approach. It constructs a prediction and update model for the state variables using the higher-order Taylor series expansion and designs an algorithm for estimating the rotor position of PMSMs based on higher-order extended Kalman filtering. The simulation results indicate that, compared to the EKF, the proposed method reduces the root-mean-square error by 10%.

Estimation of the Person's Position Using Video Sequences and Radio Wave Strengths in a Space with Temporary Shielding

Estimation of the Person's Position Using Video Sequences and Radio Wave Strengths in a Space with Temporary Shielding

Wireless UV collaborative RSSI and the AOA hybrid localization method for UAV swarms

Wireless ultraviolet (UV) light can guarantee the inter-copter positioning accuracy of cluster-flying unmanned aerial vehicles (UAVs) in an electromagnetic confrontation environment. By combining wireless UV received signal strength indication (RSSI) localization and the angle of arrival (AOA) localization algorithm, a UV-hybrid localization method is proposed for UV communication collaboration between UAV swarms. The method collects the UV signal strength between the anchor UAV node and the unknown UAV node to obtain the inter-aircraft distance information, establishes a Gaussian hybrid noise model based on semi-definite relaxation, and uses the UV angle of the arrival estimation to solve the angle between the UAVs to achieve the maximum likelihood estimation of node positions in UAV swarms. A simulation comparison of the UV-hybrid localization algorithm, weighted least squares, and the Gaussian hybrid semi-definite planning localization algorithm is carried out. The results show that the performance of the UV-hybrid localization algorithm is close to the Cramer–Rao lower bound, and the average localization error is reduced by 32.9% and 15.6% compared to weighted least squares and Gaussian hybrid semi-definite planning algorithms; the algorithm of this paper achieves the node localization with fewer iterations, and it has a higher accuracy and efficiency of localization than the other algorithms.

Efficient data-driven joint-level calibration of cable-driven surgical robots

Accurate joint position estimation is crucial for the control of cable-driven laparoscopic surgical robots like the RAVEN-II. However, any slack and stretch in the cable can lead to errors in kinematic estimation, complicating precise control. This work proposes an efficient data-driven calibration method, requiring no additional sensors post-training. The calibration takes 8–21 min and maintains high accuracy during a 6-hour heavily loaded operating. The Deep Neural Network (DNN) model reduces errors by 76%, achieving accuracy of 0.104∘, 0.120∘, and 0.118 mm for joints 1, 2, and 3, respectively. Compared to end-to-end models, the DNN achieves better accuracy and faster convergence by correcting original inaccurate joint positions. Additionally, a linear regression model offers 160 times faster inference speed than the DNN, suitable for RAVEN’s 1000 Hz control loop, with slight compromises in accuracy. This approach should significantly enhance the accuracy of similar cable-driven robots.

Sensor attack online classification for UAVs using machine learning

Unmanned Aerial Vehicle (UAV) sensors play a vital role in maintaining flight safety and stability. However, the increasing frequency and complexity of sensor attacks have emerged as a critical threat to UAV systems. The current lack of robust multi-classification methods for detecting sensor attacks limits the effectiveness and completeness of existing defense strategies. This research addresses these challenges by leveraging machine learning (ML) techniques to classify various sensor attacks using heterogeneous sensor data and control parameters, thereby enhancing UAV system security. In this study, we design and implement multiple sensor attack scenarios targeting gyroscopes, accelerometers, barometers, and GPS. Comprehensive datasets are collected during UAV flight, integrating diverse sensor readings, flight states, and control parameters. By analyzing the characteristics of sensor attacks and their impact on position estimation and attitude control, we identify and extract key features. To optimize the classification model, we employ feature importance analysis, correlation analysis, and ablation experiments, significantly reducing data dimensionality and enhancing model training efficiency. The experimental results demonstrate the proposed ML-based multi-classification model’s superior performance, achieving a detection rate of 89.38%, significantly outperforming traditional single-attack detection methods in terms of generalization capability. Our approach efficiently handles complex multi-sensor attack scenarios. Moreover, deploying the optimized model on UAV firmware enables real-time monitoring and classification, achieving an online detection rate of 74% with a response time of approximately 0.495ms per detection. The model’s lightweight design, requiring only 48KB of storage, makes it ideal for resource-constrained UAV environments. These contributions highlight the potential of our approach to enhance real-time anomaly detection and improve UAV system resilience against diverse sensor attacks.

Development of a Light Source Position Estimation System for Accurate Drone Flight

Development of a Light Source Position Estimation System for Accurate Drone Flight

Advanced sensorless control of a 12S/19P YASA-AFFSSPM motor using extended state observer and adaptive sliding mode control

This paper focuses on enhancing the sensorless control performance of a 12slots/19 poles yokeless and segmented armature axial flux-switching sandwiched permanent-magnet motor by proposing a rotor position Extended State Observer based on a extended back-EMF model method. Additionally, an adaptive sliding mode speed loop compensation method is introduced to address the significant cogging torque of the motor. By injecting the observed cogging torque as compensation into the q-axis current harmonic, this method aims to improve the motor's vibration and disturbance rejection performance in sliding mode control while eliminating steady-state errors in rotor speed and position estimation. The effectiveness of these control algorithms is validated through simulations and experiments under various operating conditions, demonstrating their potential for improving the position signal-free tracking performance of the investigated motor. The results indicate that the proposed control strategies achieve a maximum speed estimation error of approximately 1 rpm during steady-state operation and a maximum position estimation error of about 1.5°, showcasing high accuracy and robustness against disturbances.

UGVs’ Navigation Supported by Multi-UAVs in a ROS Environment

It is challenging for unmanned ground vehicles (UGVs) to navigate in an unknown environment without equipping them with sensors, such as LiDAR, cameras, etc. As a result, it is necessary to employ an unmanned aerial vehicle (UAV) to provide the UGVs with map information, obstacle information and relative position estimations. The UGVs receive the messages from the UAV and then automatically plan a global route. When approaching obstacles, UGVs release collision position messages to the UAV. This UAV calculates the optimal path point based on the collision position and the time to collision of the UGVs. However, the UAV’s relative position estimation equipment has a limited range, such that it cannot provide all UGVs with the information in time. This means that UGVs receive information late or even lose communication with the UAV. To address the above issues, this paper advocates for the use of multiple UAVs and the creation of different communication strategies. A variety of tests are conducted in a simulated environment built in ROS to pair each UAV with different numbers of UGVs and explore their initial positions to find the most suitable configuration.

A Receiver Position Estimation Method Based on LSTM for Multi-Transmitter Single-Receiver Wireless Power Transfer Systems

The multi-transmitter single-receiver wireless power transfer (MTSR-WPT) system has good tolerance for coil misalignment because the magnetic fields generated by multiple transmitters can be shaped to adapt to position changes in the receiver coil. In order to achieve magnetic field shaping of the MTSR-WPT system and increase power transfer efficiency, accurately estimating the position of the receiver coil is a key issue that needs to be addressed. In this article, a receiver position estimation method based on long short-term memory (LSTM) is proposed, which utilizes a data-driven approach to establish a neural network model. By learning the relationship between the measured time-series voltage data of the transmitter coils and the position of the receiver coil, the proposed model can achieve accurate position estimation of the receiver. Compared with previous works, the proposed method does not require communication between the transmitter and receiver, which is conducive to simplifying the system structure and reducing costs. In addition, the proposed LSTM-based method requires less derivation of complex formulas and the internal mechanism analysis of the system. Finally, a MTSR-WPT prototype is built to verify the proposed method. The experimental results show that the proposed LSTM-based method can achieve high-accuracy position estimation of the receiver. When the receiver moves within a range of 160 mm × 160 mm, the average error between the estimated receiver coil position using the proposed method and the actual receiver coil position is less than 2.40 mm.

A fast alignment method for shipbore SINS with the aid of electro-optical detection system

Aiming at the challenge of the extensive initial alignment time required for small ship strapdown inertial navigation system (SINS) under swaying base on the sea, an alignment method aided by electro-optical detection system (EODS) is proposed. By employing the EODS to track a target on the shore with a known position, the relative distance and orientation to the ship are obtained as auxiliary information. A direct relation between the SINS misalignment angle and the error in target position estimation has been established. This method theoretically ensures a high observability degree for yaw misalignment. Simulation and semi-physical simulation indicate that the proposed method does not rely on an accurate initial attitude and is capable of rapid correction of a 5∘ yaw error. The use of two or more sets of EODSs and targets can further eliminate the necessity for precise positioning information. In semi-physical simulation, the yaw angle converges within 100 s, with the alignment accuracy reaching the upper limit determined by inertial measurement unit (IMU) precision. This method has potential engineering utility for scenarios such as rapid initialization of SINS on unmanned surface vehicles (USVs) in nearshore mooring conditions.

Analytical formulas for geometrical factor and sensitivity for long electrodes

AbstractIn the electrical resistivity method, electrodes are usually modelled as point current sources and point voltage measurements. If the burial depth of the electrode is significant compared with the spacing between electrodes, this point approximation may not be accurate. Common situations employing long electrodes include the use of metal‐cased boreholes as electrodes and small‐scale, high‐resolution environmental, engineering and archaeological surveys where electrode spacings may be very small. In this contribution, I present analytical expressions for the mutual resistance between long electrodes modelled as line current sources. Mutual resistances are then used to calculate geometrical factors. Additionally, I present an expression for the current density and use it to derive an analytical expression for the sensitivity of electrode arrays with long electrodes. The sensitivity is, in turn, used to calculate the mean depth and position which can be used as estimates of depth and position of investigation and as pseudosection plot points. Example calculations using the geometrical factor, sensitivity and mean depth are shown, and comparisons are made with simulations and lab‐scale experiments.

Category-Level Pose Estimation and Iterative Refinement for Monocular RGB-D Image

Category-level pose estimation is proposed to predict the 6D pose of objects under a specific category and has wide applications in fields such as robotics, virtual reality, and autonomous driving. With the development of VR/AR technology, pose estimation has gradually become a research hotspot in 3D scene understanding. However, most methods fail to fully utilize geometric and color information to solve intra-class shape variations, which leads to inaccurate prediction results. To solve the above problems, we propose a novel pose estimation and iterative refinement network, use an attention mechanism to fuse multi-modal information to obtain color features after a coordinate transformation, and design iterative modules to ensure the accuracy of object geometric features. Specifically, we use an encoder-decoder architecture to implicitly generate a coarse-grained initial pose and refine it through an iterative refinement module. In addition, due to the differences between rotation and position estimation, we design a multi-head pose decoder that utilizes the local geometry and global features. Finally, we design a transformer-based coordinate transformation attention module to extract pose-sensitive features from RGB images and supervise color information by correlating point cloud features in different coordinate systems. We train and test our network on the synthetic dataset CAMERA25 and the real dataset REAL275. Experimental results show that our method achieves state-of-the-art performance on multiple evaluation metrics.

Improved Visual SLAM Algorithm Based on Dynamic Scenes

This work presents a novel RGB-D dynamic simultaneous localization and mapping (SLAM) method that improves accuracy, stability, and efficiency of localization while relying on deep learning in a dynamic environment, in contrast to traditional static scene-based visual SLAM methods. Based on the classic framework of traditional visual SLAM, we propose a method that replaces the traditional feature extraction method with a convolutional neural network approach, aiming to enhance the accuracy of feature extraction and localization, as well as to improve the algorithm’s ability to capture and represent the characteristics of the entire scene. Subsequently, the semantic segmentation thread was utilized in a target detection network combined with geometric methods to identify potential dynamic areas in the image and generate masks for dynamic objects. Finally, the standard deviation of the depth information of potential dynamic points was calculated to identify true dynamic feature points, to guarantee that static feature points were used for position estimation. We performed experiments based on the public datasets to validate the feasibility of the proposed algorithm. The experimental results indicate that the improved SLAM algorithm, which boasts a reduction in absolute trajectory error (ATE) by approximately 97% compared to traditional static visual SLAM and about 20% compared to traditional dynamic visual SLAM, also exhibited a 68% decrease in computation time compared to well-known dynamic visual SLAM, thereby possessing absolute advantages in both positioning accuracy and operational efficiency.

Automatic travel of a mine hole robot adaptive to changes in hole diameters

Abstract In response to the complex and challenging task of long-distance inspection of small-diameter and variable-diameter mine holes, this paper presents a design for an adaptive small-sized mine hole robot. First, focusing on the environment of small-diameter mine holes, the paper analyzes the robot’s functions and overall structural framework. A two-wheeled wall-pressing robot with good mobility, arranged in a straight line, is designed. Furthermore, an adaptive variable-diameter method is devised, which involves constructing an adaptive variable-diameter model and proposing a control method based on position and force estimators, enabling the robot to perceive external forces. Lastly, to verify the feasibility of the structural design and adaptive variable-diameter method, performance tests and analyses are conducted on the robot’s mobility and adaptive variable-diameter capabilities. Experimental results demonstrate that the robot can move within small-diameter mine holes at any inclination angle, with a maximum horizontal crawling speed of 3.96 m/min. By employing the adaptive variable-diameter method, the robot can smoothly navigate convex platform obstacles and slope obstacles in mine holes with diameters ranging from 70 mm to 100 mm, achieving the function of adaptive variable-diameter within 2 s. Thus, it can meet the requirements of moving inside mine holes under complex conditions such as steep slopes and small and variable diameters.

Sensorless Position Control in High-Speed Domain of PMSM Based on Improved Adaptive Sliding Mode Observer

To improve the speed buffering and position tracking accuracy of medium–high-speed permanent magnet synchronous motor (PMSM), a sensorless control method based on an improved sliding mode observer is proposed. By the mathematical model of the built-in PMSM, an improved adaptive super-twisting sliding mode observer is constructed. Based on the LSTA-SMO with a linear term of observation error, a sliding mode coefficient can be adjusted in real time according to the change in rotational speed. In view of the high harmonic content of the output back electromotive force, the adaptive adjustment strategy for the back electromotive force is adopted. In addition, in order to improve the estimation accuracy and resistance ability of the observer, the rotor position error was taken as the disturbance term, and the third-order extended state observer (ESO) was constructed to estimate the rotational speed and rotor position through the motor mechanical motion equation. The proposed method is validated in Matlab and compared with the conventional linear super twisted observer. The simulation results show that the proposed method enables the observer to operate stably in a wide velocity domain and reduces the velocity estimation error to 6.7 rpm and the position estimation accuracy error to 0.0005 rad at high speeds, which improves the anti-interference capability.

Pseudorandom high‐frequency injection synchronous reluctance motor sensorless control with parameter variation consideration

AbstractThe sensorless control of synchronous reluctance motor (SynRM) in the zero and low‐speed domain using the traditional high‐frequency injection method has the problems of audible noise caused by the high‐frequency injection and high‐frequency loss caused by the change of working conditions, which limits the practical application of SynRM in the industrial field. To solve the problem of high‐frequency noise and loss, a pseudo‐random high‐frequency injection method considering parameter variation is proposed in this paper. Firstly, the signal injection form was adjusted to expand the power spectral density of the high‐frequency current, and the current energy spike was suppressed to reduce high‐frequency noise. Secondly, the current demodulation was combined with the flux map model to complete the injection voltage amplitude adjustment, so that the response current was kept constant under multiple operating conditions to reduce the high‐frequency loss. At the same time, the flux map model is applied to the observer to reduce the rotor position estimation error caused by the cross‐coupling effect. Under the condition of satisfying the dynamic and steady performance requirements of sensorless control, the high‐frequency loss and sharp noise caused by the high‐frequency injection method are effectively suppressed. Finally, experiments were carried out on a 1.5 kW SynRM drive platform to verify the feasibility and effectiveness of the sensorless control scheme in this paper.

Differential Positioning with Bluetooth Low Energy (BLE) Beacons for UAS Indoor Operations: Analysis and Results.

Localization of unmanned aircraft systems (UASs) in indoor scenarios and GNSS-denied environments is a difficult problem, particularly in dynamic scenarios where traditional on-board equipment (such as LiDAR, radar, sonar, camera) may fail. In the framework of autonomous UAS missions, precise feedback on real-time aircraft position is very important, and several technologies alternative to GNSS-based approaches for UAS positioning in indoor navigation have been recently explored. In this paper, we propose a low-cost IPS for UAVs, based on Bluetooth low energy (BLE) beacons, which exploits the RSSI (received signal strength indicator) for distance estimation and positioning. Distance information from measured RSSI values can be degraded by multipath, reflection, and fading that cause unpredictable variability of the RSSI and may lead to poor-quality measurements. To enhance the accuracy of the position estimation, this work applies a differential distance correction (DDC) technique, similar to differential GNSS (DGNSS) and real-time kinematic (RTK) positioning. The method uses differential information from a reference station positioned at known coordinates to correct the position of the rover station. A mathematical model was established to analyze the relation between the RSSI and the distance from Bluetooth devices (Eddystone BLE beacons) placed in the indoor operation field. The master reference station was a Raspberry Pi 4 model B, and the rover (unknown target) was an Arduino Nano 33 BLE microcontroller, which was mounted on-board a UAV. Position estimation was achieved by trilateration, and the extended Kalman filter (EKF) was applied, considering the nonlinear propriety of beacon signals to correct data from noise, drift, and bias errors. Experimental results and system performance analysis show the feasibility of this methodology, as well as the reduction of position uncertainty obtained by the DCC technique.

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.

An Algorithm for Affordable Vision-Based GNSS-Denied Strapdown Celestial Navigation

Celestial navigation is rarely seen in modern Uncrewed Aerial Vehicles (UAVs). The size and weight of a stabilized imaging system, and the lack of precision, tend to be at odds with the operational requirements of the aircraft. Nonetheless, celestial navigation is one of the few non-emissive modalities that enables global navigation over the ocean at night in Global Navigation Satellite System (GNSS) denied environments. This study demonstrates a modular, low cost, lightweight strapdown celestial navigation solution that is utilized in conjunction with Ardupilot running on a Cube Orange to produce position estimates to within 4 km. By performing an orbit through a full rotation of compass heading and averaging the position output, we demonstrate that the biases present in a strapdown imaging system can be nullified to drastically improve the position estimate. Furthermore, an iterative method is presented which enables the geometric alignment of the camera with the Attitude and Heading Reference System (AHRS) in-flight without an external position input. The algorithm is tested using real flight data captured from a fixed wing aircraft. The results from this study offer promise for the application of low cost celestial navigation as a redundant navigation modality in affordable, lightweight drones.

Experimental assessment of IMU data processing techniques for a backpack mobile laser scanning system

Abstract. Positioning techniques are fundamental in many automation tasks with several applications. In GNSS-denied environments like in dense forests, other alternatives are required, such as inertial and visual navigation. However, Inertial Measurement Units (IMUs) data, mainly those from microelectromechanical-system (MEMS), are noisy, which affects the orientation estimation. MEMS IMUs have been employed in mobile laser scanning systems due to their compact design and low-cost solutions for short-term navigation. In this paper, we have compared three IMU processing techniques freely available: MAH (Mahony et al., 2009), MAD (Madgwick et al., 2011) and DCM (Hyyti and Visala, 2015). These techniques implemented different approaches to estimate the attitude. They were experimentally assessed with data from a backpack mobile laser scanning system, which is composed of an OS0-128 Ouster LiDAR equipped with an internal IMU. We have used data from a 5-second trajectory segment aiming to evaluate the attitude and position estimation for a local path. The results showed that the DCM algorithm maintained a consistent velocity for 5 seconds, achieving a positional error of 1.4 m, 0.06 m, and 1.05 m along the X-, Y- and Z-axis, respectively. In contrast, MAD and MAH showed a position error over 20 m, 7 m and 3 m along the X-, Y- and Z-axis, respectively, which was affected by the velocity drift.