Position Algorithm Research Articles

A Portable RFID Localization Approach for Mobile Robots

Localizing RFID-tagged objects by a mobile robot plays an important role in many Internet of Things (IoT) applications. Existing RFID localization systems are infeasible, since they either demand bulky RFID infrastructures or cannot achieve sufficient localization accuracy. In this article, a portable localization (POLO) system is developed for a mobile robot to locate RFID-tagged objects. POLO consists of an RFID reader, a tag array, and a lightweight receiver. The reader is used for interrogating the RFID tag on an object. The tag array is designed to reflect the RFID signal from an object into multipath signals. The receiver captures such signals and estimates their multipath channel coefficients by a tag-array-assisted channel estimation (TCE) mechanism. Such channel coefficients are further exploited to determine the object’s direction by a spatial smoothing direction estimation (SSDE) algorithm. To resist the impact of multipath reflections from surroundings, more spatial information is exploited by placing the tag elements densely and collecting the channel coefficients during the robot’s movement. Based on the object’s direction, POLO guides the robot to approach the object. When the object is in proximity, its 3-D location is finally determined by a near-range positioning (NRP) algorithm. Moreover, POLO is designed to be compatible with commercial RFID systems. POLO is prototyped and evaluated via extensive experiments. Results show that the average angular error is within 1 degree when the object is in the far range (2–6 m), and the average location error is within 6 cm while the object is in the near range (~1 m).

Crucian carp position and pose recognition based on an improved photo-model method

This research aimed to solve the problem of fish position and pose recognition in the process of robot grasping fish. Crucian carp is the subject of this research and studies the position and pose recognition algorithm of crucian carp by photo-model-based stereo-vision. This research aims at the problem that the fitness algorithm has a poor effect in identifying photo models with more black and white information, this research proposes an improved fitness algorithm. The new fitness algorithm uses the improved HSV model and adds the Saturation and Value to the evaluation value of the original fitness algorithm. Then Real-Time Multi-step Genetic Algorithm (RM-GA) is used to verify the improvement effect of the fitness algorithm. We use the improved algorithm for experiments. The findings indicated that the peak fitness is significantly improved, with an average increase of about 95%. At the same time, the position and posture recognition error of crucian carp is reduced by more than 50%, and the real-time performance of the algorithm is improved.

Multivehicle 3D Cooperative Positioning Algorithm Based on Information Geometric Probability Fusion of GNSS/Wireless Station Navigation

With the rapid development of large urban agglomerations and the increasing complexity of urban roads, the high-precision positioning of vehicles has become the cornerstone for the application of vehicle core technologies such as automatic driving. The real-time positioning accuracy of satellite navigation is easily affected by urban canyons, and its stability is poor; thus, how to use the information of the internet of vehicles to achieve satellite navigation fusion has become a difficult problem of multivehicle cooperative positioning. Aiming at this problem, this paper proposes a multivehicle 3D cooperative positioning algorithm based on information geometric probability fusion of GNSS/wireless station navigation (MVCP-GW), which creatively converts various navigation source information into an information geometric probability model, unifies navigation information time–frequency parameters, and reduces the impact of sudden error. Combined with the Kullback–Leibler algorithm (KLA) fusion method, it breaks off the shackles of the probabilistic two-dimensional model and achieves multivehicle three-dimensional cooperative positioning. Compared with the existing cooperative positioning algorithms in the performance of accuracy stability, applicability, obstruction scenarios, and physical verification, the simulation results and physical verification show that the MVCP-GW algorithm can effectively improve real-time vehicle positioning and the stability of vehicle positioning, as well as resist the impact of obstructed environments.

A Novel UAV Visual Positioning Algorithm Based on A-YOLOX

The application of UAVs is becoming increasingly extensive. However, high-precision autonomous landing is still a major industry difficulty. The current algorithm is not well-adapted to light changes, scale transformations, complex backgrounds, etc. To address the above difficulties, a deep learning method was here introduced into target detection and an attention mechanism was incorporated into YOLOX; thus, a UAV positioning algorithm called attention-based YOLOX (A-YOLOX) is proposed. Firstly, a novel visual positioning pattern was designed to facilitate the algorithm’s use for detection and localization; then, a UAV visual positioning database (UAV-VPD) was built through actual data collection and data augmentation and the A-YOLOX model detector developed; finally, corresponding high- and low-altitude visual positioning algorithms were designed for high- and low-altitude positioning logics. The experimental results in the actual environment showed that the AP50 of the proposed algorithm could reach 95.5%, the detection speed was 53.7 frames per second, and the actual landing error was within 5 cm, which meets the practical application requirements for automatic UAV landing.

Research on Indoor 3D Positioning Model Using Improved Triangular Centroid Position Algorithm Based on UWB

The indoor positioning technology of an ultra-wideband (UWB) can play an excellent supplementary role in satellite navigation and has broad application prospects. However, if strong interference exists, the measurement data based on UWB will fluctuate abnormally, which seriously affects the accuracy of positioning. In view of the above problems, based on the combination of mathematical modeling, this paper starts from the subject data, and proposes a positioning method suitable for non-interference/interference conditions and an abnormal data identification method to improve the positioning accuracy. The specific scheme includes data preprocessing model establishment and solution, positioning model establishment and solution, migration application of positioning model, interfering data identification model establishment and solution, and movement track positioning model establishment and solution.

A hydraulic actuator for joint robots with higher torque to weight ratio

AbstractJoint actuators with a compact size and high power density are necessary for robots with rotating joints, making the hydraulic actuator (HyA) an ideal actuator candidate. This paper presents a joint HyA with the following characteristics: compact installation size of 70 mm × 92.5 mm × 145 mm, low weight of 1.93 kg, high output torque of 742.2 Nm/531.09 Nm in two directions under 210 bar, high torque to weight ratio of 265.5 Nm/kg, low internal leakage of about 9 mL/min, zero external leakage, low starting pressure of 0.26 MPa/0.39 MPa in two directions, and a large rotation angle of 135 degrees. Compared with HyAs that have been applied in robot joints, the HyA proposed in this paper can greatly reduce the joint weight, reduce the joint size, and ensure the control performance of the joint movement.In addition, a dynamic model of the HyA is established. Based on this, some dynamic design suggestions are given. Furthermore, a simple position and torque control algorithm for the HyA is proposed. Finally, some experiments are carried out to verify the performance of the HyA.

3D indoor positioning with spatial modulation for visible light communications

In this paper, a novel three-dimensional (3D) indoor visible light positioning (VLP) algorithm is proposed based on the spatial modulation (SM) and its error performance assessed as compared to the conventional received signal strength (RSS)-based 3D VLP systems. As contrasted to the traditional VLP system, the proposed SM-based 3D VLP system first estimates the optical channel gain between the transmitting light-emitting diodes (LEDs) and the two photo detectors (PDs) attached to the user by a pilot-based channel estimation technique. Then, unknown 3D positions of the receiver are determined by the trilateration algorithm with distances computed from the estimates of the channel gains. Consequently, the 3D VLP system achieves an interference-free transmission with increased spectral efficiency and without the need for a demultiplexing process at the receiving end. The algorithm’s performance is evaluated regarding positioning error by applying the SM over four LEDs and the number of pilots selected as a function of the environmental signal-to-noise ratios (SNRs). The computer simulation results show that the positioning errors are obtained in an order of magnitude smaller than RSS-based techniques in an indoor industrial environment. This is mainly because the distances involved in determining the 3D positions can be determined more precisely by the pilot-aided channel estimation method without creating any data rate problem in transmission due to the higher spectral efficiency of the SM.

A Multimagnetometer Array and Inner IMU-Based Capsule Endoscope Positioning System

The wireless capsule endoscope (robot) has become more extensively used due to its comprehensive detection and patient-friendly experience. However, to provide better diagnostic information to medical staff, there is an urgent need for high-accuracy position information of capsule endoscopes during their working inside the human body. In this article, a capsule endoscopy positioning system using a magnetic sensor array is designed. It has two advantages. 1) Most of the existing magnetic positioning method needs to initialize the magnetic moment accurately, which is difficult to meet in practical applications. To solve this issue, this article proposes a method to determine the magnetic moment direction based on an inertial measurement unit. The proposed method can accurately estimate the direction of the magnetic moment even when the roll angle is singular. 2) This article proposes a nonlinear least-squares algorithm for capsule magnetic positioning based on the three-axis magnetometer observation. The algorithm is more robust than the Levenberg–Marquardt (LM) method that is widely used in capsule endoscopy positioning. Furthermore, its computation speed is over 100 times faster than the LM method, which successfully meets the real-time requirements. In this research, a three-axis mechanical platform and a six-axis robot arm are used to build a capsule magnetic positioning evaluation system. Preliminary results show the accuracy (RMS) of the proposed capsule endoscope positioning algorithm was better than 6 mm.

Research on the Fiducial Fibre Coding and Identification Algorithm of the LAMOST Fibre Positioning System

To obtain the world coordinates of a measured object in camera measurement, a certain number of image control points should be established around the detected object as fiducial points for data processing. The precision of a fiducial point directly determines the measurement accuracy of the measured object in the camera measurement. Fiducial points in camera measurement are widely used in various fields of measurement, such as aerial surveys, close-range surveys, and 3D reconstruction. In addition, a fiducial point generally adopts an encoding structure to reduce the search cost in the detection. Camera measurement is used in the LAMOST fiber positioning system, which also requires fiducial points to ensure the accuracy of fiber positioning. Different from a general fiducial point, the whole system needs to operate under dark conditions during LAMOST observation, and optical fiber positioning is carried out by the “backlighting method”, which measures the optical fiber spot under a dark background. In addition, fiber positioning needs to reach an accuracy of 40 μm at a distance of 20 m, which is difficult to achieve with the fiducial points used in general vision measurements. To meet the requirements of LAMOST optical fiber positioning with high accuracy, this paper presents a fiducial fiber and its recognition algorithm for optical fiber positioning of optical fiber spectroscopic telescopes. The fiducial fiber can be applied to a wide-field optical fiber spectroscopic telescope measurement system in a high-precision long-distance measurement environment. The experimental results show that fiducial fiber can ensure the accuracy of closed-loop positioning for LAMOST fiber units.

Low-Complexity Visible Light Positioning and Rotation Estimation Based on Eigenvalue Decomposition

Most existing visible light positioning (VLP) systems assume vertically placed receivers, or estimate the receiver rotations through gyroscopes. Although several recent VLP systems can deal with the arbitrary tilting of the receiver without the additional sensors, the positioning frequency, algorithm efficiency and stability are still to be improved. In this work, we propose a novel positioning and rotation estimation algorithm based on the eigenvalue decomposition of the covariance matrix of light direction vectors. Four or more light-emitting diodes (LEDs) serve as the anchors and three or more tilted photodiodes are equipped on the mobile receiver. The new algorithm's computational complexity only grows linearly with the number of LEDs, and it can distinguish and bypass the local optima by verifying if the objective function reaches its theoretical maximum. We use simulations to reveal the error patterns of the algorithm and build a low-cost prototype to verify its real performance. The measured average positioning error is 1.83 cm and the orientation estimation error is 0.04 rad. The proposed scheme can be used for positioning and navigation that require fast response, low power consumption, low cost, high accuracy and small device size.

Multi-Target Position and Velocity Estimation Algorithm Based on Time Delay and Doppler Shift in Passive MIMO Radar

Multi-Target Position and Velocity Estimation Algorithm Based on Time Delay and Doppler Shift in Passive MIMO Radar

Smart Home Control System Using VLC and Bluetooth Enabled AC Light Bulb for 3D Indoor Localization with Centimeter-Level Precision.

Smart home systems (SHSs) are a modern lifestyle trend, changing daily lives in the most intuitive way. To connect and operate various smart devices under one system, an accurate, convenient, and secure control method is of utmost significance. Nowadays, most smart home control systems are based on radio-frequency (RF) technologies such as Bluetooth, Wi-Fi, and Zigbee. They tend to suffer from poor location accuracy in high-density environments due to the interference and noise in RF signals as well as their penetration through walls, leading to security vulnerabilities and low-precision control. To address these issues, this paper presents a smart home control system based on visible light communication (VLC), with enhanced security and accurate localization for precise and convenient control. The system includes an AC lightbulb, a smartphone running the position and control applications, and a cloud server with location-based access and a database of smart home devices. The design of the AC lightbulb integrates VLC and Bluetooth connectivity in a standard form factor for easy installation and plug-n-play capability. A smartphone camera-based 3D indoor positioning and orientation algorithm that allows precise control by pointing the smartphone the device is also presented. We demonstrate the feasibility of this system through prototype implementation and experimental verification.

A Continuous PDR and GNSS Fusing Algorithm for Smartphone Positioning

Pedestrian dead reckoning (PDR), used in state-of-the-art smartphones, calculates pedestrian positions by using built-in inertial sensors. However, the complex and changeable usage modes of smartphones have been obstructing the development of PDR in the field of gait detection. Since the measurement of the sensor is affected by noise, position errors will emerge, needing to be corrected periodically via external measurements. To this end, an optimization-based PDR (OBPDR) method for smartphones is proposed in this study. First, an improved finite state machine (IFSM) gait detection method is designed, which can improve the gait recognition rate and stability compared with the traditional peak detection method. Second, the step detection algorithm proposed in this paper is combined with a heading estimation to obtain the PDR dynamic model. Finally, the measurements of GNSS are fused to the PDR model, based on an adaptive extended Kalman filter (AEKF) algorithm, which can enhance the adaptability of the system to the environment in order to reduce the cumulative errors of PDR. Experiments are carried out to evaluate the performance of the proposed method. The results indicate that compared with the gait detection method, based on peak detection, and the integrated positioning method, based on an extended Kalman filter, the proposed method boasts favorable robustness and a high gait recognition rate, the recognition accuracy being kept between 97.5% and 98.5%; the average position error decreased by more than 67.25%.

A Robust Algorithm for Multi-GNSS Precise Positioning and Performance Analysis in Urban Environments

In this paper, we propose a partial ambiguity method of global navigation satellite system (GNSS) reliable positioning based on a robust estimation model to address the problems of the low reliability and availability of GNSS positioning in urban complex environments. First, the high-precision observations selected on the basis of the signal-to-noise ratio (SNR) were used to solve ambiguities. Then, the fixed ambiguities were used as constraints to solve the ambiguities of low-quality observations. The robust estimation method was used to reduce the impact of outliers for the ambiguity solutions. The robust estimation was also used to solve the position parameters to reduce the influence of the residual errors and uncorrected ambiguities for GNSS high-accuracy positioning. Static and dynamic data were used to evaluate the proposed algorithm. These experiments show that the proposed algorithm with the robust estimation can reduce the fixed time of ambiguity initialization, compared with the conventional algorithm without the robust estimation. The positioning accuracy and solution rate are similar regardless of whether the robust estimation is used in the GNSS unblocked environment. In blocked environments, the solution rate improves to more than 99%, and the three-dimensional (3D) position accuracy improves by more than 70% when the robust estimation is used. When the observation number of simulated small gross error accounts for 40.91% of total observations, the centimeter-level positioning accuracy can still be obtained via several robust estimation models. In the urban blocked environment, the IGG (Institute of Geodesy and Geophysics) III scheme has a better performance than other robust schemes discussed in this paper with regard to the positioning performance and computational efficiency.

Research on Improved Algorithm of Mobile Robot Vision SLAM Based on Depth Camera

<p>Aiming at the problems of low accuracy, poor real-time performance and large cumulative error in traditional depth camera visual synchronization positioning and map construction algorithms, this paper proposes an improved visual SLAM algorithm for mobile robots based on depth camera. According to the realization process of robot vision, a feature point extraction algorithm based on feature extraction and image segmentation watershed algorithm is proposed in the front-end process, which improves the real-time performance of the algorithm. Then the RE-RANSAC algorithm is used to eliminate the mismatched feature points to improve the matching accuracy, and then the accumulated error is eliminated through closed loop detection, and finally the process of robot mapping is completed. After simulation experiments, the feasibility of the improved algorithm is proved, and the robot’s mapping and trajectory estimation are completed.</p> <p> </p>

Deep learning solution for medical image localization and orientation detection.

Magnetic Resonance (MR) imaging plays an important role in medical diagnosis and biomedical research. Due to the high in-slice resolution and low through-slice resolution nature of MR imaging, the usefulness of the reconstruction highly depends on the positioning of the slice group. Traditional clinical workflow relies on time-consuming manual adjustment that cannot be easily reproduced. Automation of this task can therefore bring important benefits in terms of accuracy, speed and reproducibility. Current auto-slice-positioning methods rely on automatically detected landmarks to derive the positioning, and previous studies suggest that a large, redundant set of landmarks are required to achieve robust results. However, a costly data curation procedure is needed to generate training labels for those landmarks, and the results can still be highly sensitive to landmark detection errors. More importantly, a set of anatomical landmark locations are not naturally produced during the standard clinical workflow, which makes online learning impossible. To address these limitations, we propose a novel framework for auto-slice-positioning that focuses on localizing the canonical planes within a 3D volume. The proposed framework consists of two major steps. A multi-resolution region proposal network is first used to extract a volume-of-interest, after which a V-net-like segmentation network is applied to segment the orientation planes. Importantly, our algorithm also includes a Performance Measurement Index as an indication of the algorithm's confidence. We evaluate the proposed framework on both knee and shoulder MR scans. Our method outperforms state-of-the-art automatic positioning algorithms in terms of accuracy and robustness.

A Hybrid Modified Ant Colony Optimization - Particle Swarm Optimization Algorithm for Optimal Node Positioning and Routing in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have been widely deployed in hostile locations for environmental monitoring. Sensor placement and energy management are the two main factors that should be focused due to certain limitations in WSNs. The nodes in a sensor network might not stay charged when energy draining takes place; therefore, increasing the operational lifespan of the network is the primary purpose of energy management. Recently, major research interest in WSN has been focused with the essential aspect of localization. Several types of research have also taken place on the challenges of node localization of wireless sensor networks with the inclusion of range-free and range-based localization algorithms. In this work, the optimal positions of Sensor Nodes (SNs) are determined by proposing a novel Hybrid M-ACO – PSO (HMAP) algorithm. In the HMAP method, the improved PSO utilizes learning strategies for estimating the relay nodes' optimal positions. The M-ACO assures the data conveyance. A route discovers when it relates to the ideal route irrespective of the possibility of a system that includes the nodes with various transmission ranges, and the network lifetime improves. The proposed strategy is executed based on the energy, throughput, delivery ratio, overhead, and delay of the information packets.

Cooperative Location Method for Leader-Follower UAV Formation Based on Follower UAV's Moving Vector.

The traditional leader-follower Unmanned Aerial Vehicle (UAV) formation cooperative positioning (CP) algorithm, based on relative ranging, requires at least four leader UAV positions to be known accurately, using relative distance with leader UAVs to achieve the unknown position follower UAV’s high-precision positioning. When the number of the known position leader UAVs is limited, the traditional CP algorithm is not applicable. Aiming at the minimum cooperative unit, which consists of a known position leader UAV and an unknown position follower UAV, this paper proposes a CP method based on the follower UAV’s moving vector. Considering the follower UAV can only acquire the single distance with the leader UAV at each distance-sampling period, it is difficult to determine the follower UAV’s spatial location. The follower UAV’s moving vector is used to construct position observation of the follower UAV’s inertial navigation system (INS). High-precision positioning is achieved by combining the follower UAV’s moving vector. In the process of CP, the leader UAV obtains a high-precision position by an INS/Global Positioning System (GPS) loosely integrated navigation system and transmits its position information to the follower UAV. Based on accurate modeling of the follower UAV’s INS, the position, velocity and heading observation equation of the follower UAV’s INS are constructed. The improved extended Kalman filtering is designed to estimate the state vector to improve the follower UAV’s positioning accuracy. In addition, considering that the datalink system based on radio signals may be interfered with by the external environment, it is difficult for the follower UAV to obtain relative distance information from the leader UAV in real time. In this paper, the availability of the relative distance information is judged by a two-state Markov chain. Finally, a real flight test is conducted to validate the performance of the proposed algorithm.

An improved LSE-EKF optimisation algorithm for UAV UWB positioning in complex indoor environments

With the increasing application of UAVs, UAV positioning technology for indoor complex environment has become a hot research issue in the industry. The traditional UWB positioning technology is affected by problems such as multipath effect and non-line-of-sight propagation, and its application in complex indoor environments has problems such as poor positioning accuracy and strong noise interference. We propose an improved LSE-EKF optimisation algorithm for UWB positioning in indoor complex environments, which optimises the initial measurement data through a BP neural network correction model, then optimises the coordinate error using least squares estimation to find the best pre-located coordinates, finally eliminates the interference noise in the pre-located coordinate signal through an EKF algorithm. It has been verified by experiments that the evaluation index can be improved by more than 9% compared with EKF algorithm data, especially under non-line-of-sight (NLOS) conditions, which enhances the possibility of industrial application of indoor UAV.

Grey Wolf Cooperative Positioning Algorithm for UWB Network

Grey Wolf Cooperative Positioning Algorithm for UWB Network