Heading Reference System Research Articles

Multi-Sensor Orientation Tracking for a Façade-Cleaning Robot.

Glass-façade-cleaning robots are an emerging class of service robots. This kind of cleaning robot is designed to operate on vertical surfaces, for which tracking the position and orientation becomes more challenging. In this article, we have presented a glass-façade-cleaning robot, Mantis v2, who can shift from one window panel to another like any other in the market. Due to the complexity of the panel shifting, we proposed and evaluated different methods for estimating its orientation using different kinds of sensors working together on the Robot Operating System (ROS). For this application, we used an onboard Inertial Measurement Unit (IMU), wheel encoders, a beacon-based system, Time-of-Flight (ToF) range sensors, and an external vision sensor (camera) for angular position estimation of the Mantis v2 robot. The external camera is used to monitor the robot’s operation and to track the coordinates of two colored markers attached along the longitudinal axis of the robot to estimate its orientation angle. ToF lidar sensors are attached on both sides of the robot to detect the window frame. ToF sensors are used for calculating the distance to the window frame; differences between beam readings are used to calculate the orientation angle of the robot. Differential drive wheel encoder data are used to estimate the robot’s heading angle on a 2D façade surface. An integrated heading angle estimation is also provided by using simple fusion techniques, i.e., a complementary filter (CF) and 1D Kalman filter (KF) utilizing the IMU sensor’s raw data. The heading angle information provided by different sensory systems is then evaluated in static and dynamic tests against an off-the-shelf attitude and heading reference system (AHRS). It is observed that ToF sensors work effectively from 0 to 30 degrees, beacons have a delay up to five seconds, and the odometry error increases according to the navigation distance due to slippage and/or sliding on the glass. Among all tested orientation sensors and methods, the vision sensor scheme proved to be better, with an orientation angle error of less than 0.8 degrees for this application. The experimental results demonstrate the efficacy of our proposed techniques in this orientation tracking, which has never applied in this specific application of cleaning robots.

Design and test of laser-controlled paddy field levelling-beater.

The technology for implementing a precise soil beating after precise soil levelling was proposed to improve the flatness of the paddy field and shorten the cycle time between the soil beating and levelling; in addition, a laser-controlled paddy field levelling-beater was designed according to the working principle. And the grade and tilt of the levelling scraper and beating mechanism are automatically controlled according to the levelling-beater vertical height and the tractor roll angle, respectively. The designed levelling-beater is capable of precise levelling and beating paddy fields at the same time with an adjustable beating depth. A paddy field test of the levelling-beater was conducted to compare the performance under both manual and automatic control modes, with the roll angles of the tractor and the levelling-beater measured using two attitude and heading reference systems (AHRSs), and the change in grade of the levelling-beater was measured using a global navigation satellite system (GNSS). The test results demonstrate that the operational quality of the levelling-beater is more stable when operating in automatic control mode than when operating in manual control mode. More specifically, the elevation of the levelling-beater varied ±4 cm around the mean elevation and roll angle varied within the range of ±0.5° when operating in automatic control mode. However, when operating in manual control mode, the elevation and roll angle were greater than ±11 cm and ±2.5°, respectively. The test results also demonstrate that the laser-controlled paddy field levelling-beater significantly improves the paddy field flatness, and enables it to operate at a stable depth to realise an even levelling and beating layer. More specifically, the maximum variation of elevation was reduced from 26.4 cm before the levelling and beating operation to 11.5 cm after the operation. In addition, the standard deviation of the elevation was reduced from 4.13 cm to 2.18 cm after the operation. The total number of flatness sampling points with the absolute difference of the desired elevation less than or equal to 3cm was more than 86%. The effective beating depth was 14.2 cm, compared with the set beating depth of 15cm, and the standard deviation of the beating depth was 2.46 cm. Keywords: laser control, levelling, beating, paddy field, flatness DOI: 10.25165/j.ijabe.20201301.4989 Citation: Zhou H, Hu L, Luo X W, Tang L M, Du P, Mao T, et al. Design and test of laser-controlled paddy field levelling-beater. Int J Agric & Biol Eng, 2020; 13(1): 57–65.

Adaptive Attitude Estimation for Low-Cost MEMS IMU Using Ellipsoidal Method

In this article, the attitude estimation for low-cost MEMS inertial measurement units in a smartphone is proposed using adaptive ellipsoidal methods. Accelerometer and magnetometer measurements in an attitude heading reference system (AHRS) are ideally applicable when there is no acceleration and magnetic disturbance. However, when the AHRS is applied to the smartphone, acceleration is frequently occurred by hand movement, and it causes significant attitude errors. Besides, magnetic disturbance from the surrounding environment degrades the heading estimation. In order to improve the attitude accuracy, the acceleration from movement, in addition to the magnetic disturbance is considered in adaptive logic using measurement residuals in this article. The performance of the proposed algorithm is simulated by the computer and tested using the rate table and optical motion capture system compared with the conventional algorithms.

Performance Analysis of Direct GPS Spoofing Detection Method with AHRS/Accelerometer.

The global positioning system (GPS) is an essential technology that provides positioning capabilities and is used in various applications such as navigation, surveying, mapping, robot simultaneous localization and mapping (SLAM), location-based service (LBS), etc. However, the GPS is known to be vulnerable to intentional attacks such as spoofing because of its simple signal structure. In this study, a direct method is proposed for GPS spoofing detection, using Attitude and Heading Reference System (AHRS) accelerometer and analyzing the detection performance with corresponding probability density functions (PDFs). The difference in the acceleration between the GPS receiver and the accelerometer is used to detect spoofing. The magnitude of the acceleration error may be used as a decision variable. Additionally, using the magnitude of the north (or east) component of the acceleration error as another decision variable is proposed, which shows better performance in some conditions. The performance of the two decision variables is compared by calculating the probability of spoofing detection and the detectable minimum spoofing acceleration (DMSA), given a pre-defined false alarm probability and a pre-defined detection probability. It turns out that both decision variables need to be used together to obtain the best spoofing detection performance.

Implementation and performance evaluation of an inertial navigation system/global navigation satellite system real‐time kinematic Ntrip navigation system aided by a robot operating system‐based emulated odometer for high‐accuracy land vehicle navigation in urban environments

AbstractAdvanced land‐vehicle navigation commonly uses integrated systems to counteract global navigation satellite system (GNSS) solution degradation. This occurs mainly in urban environments due to blockage of the satellite signals. This paper presents a loosely coupled inertial navigation system/GNSS navigation system that combines an attitude and heading reference system (AHRS) device with a dual‐frequency dual‐antenna GNSS heading receiver. The integrated navigation system is aided by a low‐cost odometer which replaces external wheel speed sensors usually installed in autonomous vehicles. The proposed odometer extracts the anti‐lock braking system‐generated pulses of rear wheels from vehicle controller area network messages. Following, it converts them into software‐generated signal pulses which are sent to the AHRS device through the serial port. The system platform uses a mobile internet data link to get differential GNSS corrections in real‐time from a public Ntrip—networked transport of Radio Technical Commission for Maritime Services via internet protocol—broadcaster in order to allow the GNSS receiver to operate in differential global positioning system/real‐time kinematic (RTK) modes. Thus, the integrated navigation system provides centimeter‐level positioning accuracy at 100 Hz. Since the positioning accuracy is severely affected by numerous factors, this work proposes a replicated 24 full factorial design with the purpose of evaluating the in‐field obtained positioning performance under different factors combinations. The experimental design chosen allows to know under which conditions it is feasible to replace the available GNSS velocity by the proposed odometry solution, when they are used as navigation aids, and knowing that the proposed odometry has a low resolution. The analysis of 32‐runs factorial design results, using a significance level of .05, demonstrated that the proposed odometry can overcome GNSS/RTK velocity.

Stochastic evaluation of orientation parameters for an antenna complex using strapdown inertial system measurements

When solving the problem of high-precision spatial orientation of antennas of radio engineering complexes placed on high-rise masts of various designs, one of the main tasks is to increase the accuracy of their angular orientation in the conditions of inevitable disturbances of the mast base (due to wind disturbances, operation of various units, etc.) and interference of measurement of antenna orientation parameters by various measuring systems. Currently used methods based on measurements of satellite navigation systems and autonomous measurements of attitude and heading reference system do not provide the required accuracy for solving the problem of antenna orientation, located on a highly dynamic mobile base. This circumstance is due both to the weak noise immunity of algorithms of information processing and the inability of accounting for the dynamic properties of orientation parameters measured also in the presence of noise of high intensity, whose properties also usually are not taken into account. In this regard, the article considers a dynamic algorithm for estimating the stochastic parameters of the antenna orientation of the radio engineering complexes, invariant to the nature of the movement of the mast base and providing stability and the required accuracy of the evaluation under the most general assumptions about the nature of interference of the sensing elements attitude and heading reference system. As the observed state vector – the vector of antenna orientation parameters, the vector of Rodriguez–Hamilton parameters is used, and as the vector of observation of it – the vector of measurements of angular velocity sensors of a strapdown inertial system, which consists of three accelerometers and three angular velocity sensors located orthogonally in the center of mass of the antenna. Based on the nonlinear stochastic equations of dynamics of change of the vector of parameters of the current orientation attitude and heading reference system on the moving base and stochastic models of the measuring signals sensing elements attitude and heading reference system, a non-linear (generalized) Kalman filter, providing the desired solution of the problem of estimation of the parameters of the current orientation of the antenna on the perturbed basis, was built. The results of the numerical experiment allow us to conclude that it can be used to solve the problem of operational orientation of radio engineering complexes antennas placed on masts, using medium-and high-precision attitude and heading reference system without correction for a long time.

Enabling outdoor MR capabilities for head mounted displays: a case study

This research work covers generic approaches to determine the outdoor position and orientation of an augmented reality device due to the lack of outdoor capability of depth-sensor or environmental recognition-sensor-based devices currently available on the market. The determination of the orientation is primarily achieved with an attitude heading reference system (AHRS) for a rough estimation. Based on a connected/built-in video camera the accuracy at minor changes of the orientation is enhanced by applying registration to assess the differences in orientation between two video frames, compensating for gyroscope drift errors. The position determination is achieved using GPS with a rover and base station real time kinematic beacon system to achieve enhanced precision. Results show that due to sensor application AR hardware considered for indoor use can be retooled to properly work outdoors, at large distances and even inside running vehicles. Thus, future implementation of applications in various domains is facilitated.

MEMS-Based IMU Drift Minimization: Sage Husa Adaptive Robust Kalman Filtering

The Attitude Heading Reference System (AHRS) has been widely used to provide the position and orientation of a rigid body. A low cost MEMS based inertial sensor measurement unit (IMU) is a core device in AHRS. To improve the AHRS system performance, there is a need to develop (i) stochastic IMU error models and (ii) random noise minimization techniques. In this paper, we modify the Sage-Husa Adaptive Kalman Filter (SHAKF) to incorporate time-varying noise estimator and robustifier, termed as Modified Sage-Husa Adaptive Robust Kalman Filter (MSHARKF). In the proposed algorithm, a three segment approach is used to evaluate the adaptive scale factor followed by the learning statistics. The scale factor is iteratively updated in the MSHARKF equations. In addition, angle random walk (ARW) and bias instability (BI) errors are represented by state-space models. The proposed algorithm is applied to restrain the drift error and random noise in the MEMS IMUs signals. The performance of this algorithm is analyzed using Allan variance (AV) analysis for static signals whereas the Root Mean Square Error (RMSE) values are evaluated for dynamic signals. Experimental results demonstrate the effectiveness of MSHARKF in reducing the drift and random noise in static and dynamic conditions as compared with other existing algorithms. Finally, we present sufficient conditions for convergence proof of the MSHARKF algorithm.

Virtual Reality-based Control of Robotic Endoscope in Laparoscopic Surgery

Robotic laparoscopic surgery has provided various benefits, but during the surgery, the surgeons are experiencing uncomfortable positioning issue which leads to neck pain and back pain. So, for an enhanced & efficient ergonomic robot-assisted surgery, a novel virtual reality (VR)-based endoscope control system (ECS) is proposed in this research. The overall system in this study is composed of a da Vinci research kit (dVRK), 4-degree-of-freedom ECS, three-dimensional endoscope, and VR headset with a built-in attitude and heading reference system (AHRS) module. Also, the proposed VR headset could replace a stereo viewer in the dVRK which could reduce the size of surgical robot system. Furthermore, the three dimensional endoscope could be controlled by a built-in AHRS module in the VR headset. The proposed system has been verified with four novice volunteers. They showed rapid learning through the peg-transfer task. Additionally, a collision avoidance strategy for VR-based ECS control was developed and verified by performing computer-based simulations. The ergonomic VR-based ECS proposed in this research could greatly reduce surgeon’s pains in both neck and back which potentially reduce surgeon’s workload during the surgery.

Method and Implementation of a Bioinspired Polarization-Based Attitude and Heading Reference System by Integration of Polarization Compass and Inertial Sensors

How to self-determine the attitude and heading of users is a challenging issue in unknown, global navigation satellite system (GNSS) denied, or magnetic disturbance environments. In this article, a bioinspired polarization-based attitude and heading reference system (PAHRS) aided by inertial sensors is presented. As compared with the existing studies, a polarization compass, which consists of three photoelectric-based polarization sensors is designed to cope with the three-dimensional polarization heading determination and distinguish issue. Hence, the real heading orientation can be determined without GNSS correction. To address the triaxis attitude and heading determination issue of PAHRS, the system measurement model tightly coupled the attitude and heading accumulate errors of inertial navigation system. Finally, the experimental platform is built and the results illustrate that the PAHRS is able to achieve satisfactory performance without the aid by GNSS and magnetic compass system.

SwimBIT: A Novel Approach to Stroke Analysis During Swim Training Based on Attitude and Heading Reference System (AHRS).

In a world where technology is assuming a pervasive role, sports sciences are also increasingly exploiting the possibilities opened by advanced sensors and intelligent algorithms. This paper focuses on the development of a convenient, practical, and low-cost system, SwimBIT, which is intended to help swimmers and coaches in performance evaluation, improvement, and injury reduction. Real-world data were collected from 13 triathletes (age 20.8 ± 3.5 years, height 173.7 ± 5.3 cm, and weight 63.5 ± 6.3 kg) with different skill levels in performing the four competitive styles of swimming in order to develop a representative database and allow assessment of the system’s performance in swimming conditions. The hardware collects a set of signals from swimmers based on an attitude and heading reference system (AHRS), and a machine learning workflow for data analysis is used to extract a selection of indicators that allows analysis of a swimmer’s performance. Based on the AHRS data, three novel indicators are proposed: trunk elevation, body balance, and body rotation. Experimental evaluation has shown promising results, with a 100% accuracy in swim lap segmentation, a precision of 100% in the recognition of backstroke, and a precision of 89.60% in the three remaining swimming techniques (butterfly, breaststroke, and front crawl). The performance indicators proposed here provide valuable information for both swimmers and coaches in their quest for enhancing performance and preventing injuries.

GNSS/LiDAR-Based Navigation of an Aerial Robot in Sparse Forests

Autonomous navigation of unmanned vehicles in forests is a challenging task. In such environments, due to the canopies of the trees, information from Global Navigation Satellite Systems (GNSS) can be degraded or even unavailable. Also, because of the large number of obstacles, a previous detailed map of the environment is not practical. In this paper, we solve the complete navigation problem of an aerial robot in a sparse forest, where there is enough space for the flight and the GNSS signals can be sporadically detected. For localization, we propose a state estimator that merges information from GNSS, Attitude and Heading Reference Systems (AHRS), and odometry based on Light Detection and Ranging (LiDAR) sensors. In our LiDAR-based odometry solution, the trunks of the trees are used in a feature-based scan matching algorithm to estimate the relative movement of the vehicle. Our method employs a robust adaptive fusion algorithm based on the unscented Kalman filter. For motion control, we adopt a strategy that integrates a vector field, used to impose the main direction of the movement for the robot, with an optimal probabilistic planner, which is responsible for obstacle avoidance. Experiments with a quadrotor equipped with a planar LiDAR in an actual forest environment is used to illustrate the effectiveness of our approach.

Aerodynamic angle estimation: comparison between numerical results and operative environment data

Several architectures exist to measure aerodynamic angles based on physical sensors. As far as unmanned aerial vehicle (UAV) is concerned, traditional systems hardly comply with reliability and redundancy requirements due to size and weight limitations. A patented virtual sensor, based on artificial neural network (ANN) techniques, named smart-air data, attitude and heading reference system (Smart-ADAHRS) has been investigated as a good estimator for aerodynamic angles in simulated environment. This paper focuses on flight testing procedures in operative environment and data processing for the Smart-ADAHRS validation with real data. As many factors interfere during the generation of the ANN training set, an accurate choice and integration of the flight test instrumentation (FTI) system components becomes crucial. A comprehensive description has been included about the FTI equipment and its influence on the neural network performance. Differences between numerical simulation and operative environment data are detailed as final aim of this work. At the end, feasible solutions are suggested to solve the typical gap between virtual and real scenario, both in terms of data analysis and neural network architecture.

Sampled-data $$H_\infty$$ Fuzzy Observer Design Under Time-varying Sampling Rates and its Applications to the Attitude and Heading Reference System

In this paper, we design a sampled-data \(H_\infty\) fuzzy observer for nonlinear systems under variable sampling periods. To do so, a linear matrix inequality-based design condition is derived to stabilize the error vector consisting of the difference of the state vectors of the observer and the system. When deriving the condition, we propose a time-dependent fuzzy Lyapunov–Krasovskii functional with discrete time membership functions to obtain numerically less conservative condition. Also, since the proposed method allows the sampling time to be variable, it can be used in low-cost hardwares that do not meet the strict sampling period. Finally, in the hardware experiment, the proposed method is applied to design the attitude and heading reference system that estimates the Euler angles from inertial sensor data, and the results demonstrate the effectiveness of the proposed method.

An Embedded System Design to Build Real-Time 2D Maps for Unknown Indoor Environments

In this paper, a remotely controllable, differentially driven wheeled mobile robot was developed in order to build 2D maps of unknown indoor environments; this system would eliminate the need to pre-explore such environments. Main aim of the study is to develop a system with high accuracy by using minimum number of sensors and a processor with low cost especially for comparatively small indoor areas. The distance traveled was calculated using the wheel odometry method. Obstacles surrounding the robot, the distance traveled, and the robot’s orientation were obtained using an ultrasonic distance sensor, optical encoder, and a 3D orientation sensor (also known as an Attitude and Heading Reference System –AHRS), respectively. In addition, the characteristics of the system hardware components were empirically explored, and the errors resulting from the sensors were evaluated. The non-linearity percentage error arising from the encoder was defined and then compensated for. The hysteresis behavior of the ultrasonic distance sensors was also empirically tested. All of the tasks were conducted by using a low-cost FPGA (Field Programmable Gate Arrays) board. This study used the graphical development platform, National Instruments (NI) LabVIEW, and it’s FPGA Module which is used for programming of embedded systems instead of the text-based HDLs (Hardware Description Languages). This distinguishes the proposed system from similar prior studies.

Compensation for errors in determining the angle in the flight-navigation complex of the aircraft in case of failure of the satellite navigation system

Currently, navigation equipment for long-range aircraft, where the flight is characterized by a long duration and the absence of frequent and abrupt maneuvers, continues to be actively improved. Therefore, the main purpose of the work is to analyze the compensation of the error in determining the angle in the flight-navigation complex of the aircraft in case of failure of the satellite navigation system. In order to achieve the goal, inertial navigation methods using an attitude and heading reference system were used. It was determined that when comparing the evolution graphs of the heading and errors in determining the eastern component of the ground speed, it is possible to establish the dependence that at the moments of maneuvers associated with a significant change in the direction of this speed, the phase and amplitude of the error fluctuations are also changed. It was established that today the operational technology is equipped with blocks of an inertial navigation system. It helps to improve the accuracy of the definition of navigation information.

RTK with the Assistance of an IMU-Based Pedestrian Navigation Algorithm for Smartphones.

Real-time kinematic (RTK) technique is widely used in modern society because of its high accuracy and real-time positioning. The appearance of Android P and the application of BCM47755 chipset make it possible to use single-frequency RTK and dual-frequency RTK on smartphones. The Xiaomi Mi 8 is the first dual-frequency Global Navigation Satellite System (GNSS) smartphone equipped with BCM47755 chipset. However, the performance of RTK in urban areas is much poorer compared with its performance under the open sky because the satellite signals can be blocked by the buildings and trees. RTK can’t provide the positioning results in some specific areas such as the urban canyons and the crossings under an overpass. This paper combines RTK with an IMU-based pedestrian navigation algorithm. We utilize attitude and heading reference system (AHRS) algorithm and zero velocity update (ZUPT) algorithm based on micro electro mechanical systems (MEMS) inertial measurement unit (IMU) in smartphones to assist RTK for the sake of improving positioning performance in urban areas. Some tests are carried out to verify the performance of RTK on the Xiaomi Mi 8 and we respectively assess the performances of RTK with and without the assistance of an IMU-based pedestrian navigation algorithm in urban areas. Results on actual tests show RTK with the assistance of an IMU-based pedestrian navigation algorithm is more robust and adaptable to complex environments than that without it.

Invariant observer design of attitude and heading reference system

An attitude and heading reference system (AHRS) is a nonlinear state estimator unit for computing orientation in 3D space. This paper designs an AHRS using three approaches: an invariant observer, an invariant extended Kalman filter (IEKF), and a conventional extended Kalman filter (EKF). The three designs are validated in experiment versus a ground truth, demonstrating the practical interest of the invariant observer methodology and the advantage of the IEKF over the EKF under model uncertainty.

정지 상태 추정 알고리즘과 간접 칼만 필터를 이용한 AHRS 개발

The AHRS(Attitude and Heading Reference System) estimates the attitude (roll, pitch, and yaw angles) by using sensor fusion technique from an IMU(Inertial Measurement Unit) which comprises 3-axis acceleration and angular velocity information. Compared to maritime vessels or aircraft, the ground vehicle’s attitude changes rapidly, which requires an exquisite attitude estimation algorithm. This paper suggests a sensor fusion AHRS algorithm which utilizes still-state estimation algorithm along with indirect Kalman filter for attitude estimation. Also, we applied this algorithm on AHRS hardware to evaluate its performance. We implemented three kinds of experiments to verify the suggested algorithm: lateral acceleration rejection test, 3-axis robot simulator test, and vehicle field test.

Visualization interface for posture monitoring of wheel loaders based on inertial sensors

Wheel loaders need an interactive interface to collect and display the posture information when they operate in a harsh environment. This paper proposes a visualization interface for posture monitoring of wheel loaders. It is driven by the data from the inertial measurement system based on attitude heading reference system, including a real-time three-dimensional animation display system created using OpenGL. The proposed system could be effectively used for monitoring the body posture of wheel loaders under the influence of the external environment. Experimental results are presented to show the good accuracy and real-time performance of the proposed monitoring system. Moreover, the proposed system could enhance vehicle safety, remote operability, and human–vehicle interaction.