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Range-Based Navigation Algorithms for Marine Applications
Published in Chao Gao, Guorong Zhao, Hassen Fourati, Cooperative Localization and Navigation, 2019
David Moreno-Salinas, Naveen Crasta, António M. Pascoal, Joaquín Aranda
We now discuss the results of field tests performed using two Medusa-class AMVs (see Figure 18.13).* Each vehicle has two side thrusters, which can be independently controlled to impart longitudinal and rotational motions about the {zℐ}-axis and two vertical thrusters for depth control. In addition, the vehicles are equipped with attitude and heading reference systems (AHRS) that provide measurement of body orientation and body fixed-angular velocity for control purposes. Each vehicle carries an acoustic Blueprint Seatrac data modem and ranging unit* that is used for communications and range measurements. During the tests, we operated two Medusa vehicles: one of them was used as a target operating at a constant depth underwater, while the other was used as a tracker operating at the surface (equipped with GPS), while interrogating the target. Starting from an unknown initial position, the target executed a lawnmowing motion with a constant body-speed and performed dead reckoning navigation using a DVL and the AHRS. In the tests, for the sake of simplicity, the tracker had access not only to the range to the target but also to the velocity vector of the latter (communicated via the acoustic communications channel) every 1.5 [s]. However, we remark that we can relax this requirement. The target parameters are summarized in Table 18.5.
Boxfish-Like Robot with an Artificial Lateral Line System
Published in Guangming Xie, Xingwen Zheng, Bionic Sensing with Artificial Lateral Line Systems for Fish-Like Underwater Robots, 2022
As shown in Figure 3.7, the control compartment contains an attitude and heading reference system (AHRS), several circuit boards, a credit card-sized micro-computer called NanoPi, and a pressure sensor which is named Pstatics and locates at the bottom of the compartment. AHRS consists of a triaxial accelerometer, a gyroscope and a magnetometer. It outputs yaw angle, pitch angle, roll angle, angular velocities, and accelerations of the robotic fish with a sampling rate of 50 Hz. Multiple 32-bit micro controllers on the above-mentioned circuit boards serve the functions of AHRS data acquisition and steering engines control. The pressure sensor Pstatics (MS5803-14BA, TE Connectivity Ltd.) is used for measuring the static pressure when the robotic fish is beneath the water. The static PVs can be used to calculate the depth variations of the robotic fish. NanoPi is adopted as a main processor of the robotic fish. Based on a Linux system installed on NanoPi, the robotic fish can be operated autonomously. The pressure acquisition system compartment contains a circuit board which serves the functions of collecting data of the ALLS via inter-integrated circuit (I2C) bus and then transferring the data to NanoPi. Finally, the data were transmitted to host computer through wireless serial communication modules, with a rate of 50 Hz.
Robotics and Sensors: Environmental Applications
Published in John G. Webster, Halit Eren, Measurement, Instrumentation, and Sensors Handbook, 2017
To monitor the position of the body and pelvis, some form of sensor must be implemented. An AHRS consists of three gyroscopes, three accelerometers, and three magnetometers. These systems are currently used in intelligent gait humanoids. An on-board processor calculates the rate of change with respect to time outputting an absolute position in hexadecimal. The magnetometer is used as a horizontal reference to compute the rotational angle whereas the gravity is used for the roll and pitch.
An improved LSE-EKF optimisation algorithm for UAV UWB positioning in complex indoor environments
Published in Journal of Control and Decision, 2022
As a hot technology for indoor multi-rotor UAV positioning, UWB has the advantages of good temporal and spatial resolution, strong penetration and high positioning accuracy compared with traditional positioning techniques (Chittoor et al., 2021; Wilson et al., 2022), but UWB technology itself has certain limitations, such as the existence of multi-path effects in confined spaces (Lin & He, 2019) and interference of sensor data by Gaussian white noise. Zhang et al. (2018) proposed a UWB-based attitude and heading reference system (AHRS), which can automatically eliminate the yaw angle error of the UAV during takeoff by multiple orientation measurements, and it was experimentally verified that the system can have improved the positioning accuracy of the UAV, and the errors of the UAV attitude and heading were greatly reduced compared with the manually calibrated AHRS system. Sheng (2018) from Xiangtan University proposed a UAV indoor positioning system based on a combined UWB and pseudo-satellite system, which fuses UWB signals and analogue GNSS satellite signals to solve the problem of poor indoor UAV GPS signals.
Is machine learning and automatic classification of swimming data what unlocks the power of inertial measurement units in swimming?
Published in Journal of Sports Sciences, 2021
Matthew T.O. Worsey, Rebecca Pahl, Hugo G. Espinosa, Jonathan B. Shepherd, David V. Thiel
With a single IMUs attached to the torso, much of the limb movement must be inferred. With a single IMUs attached to the wrist, much of the torso and the opposing limb movements must be inferred. The challenge therefore is to determine accurately the body movements from sensor movement. The sensor data is more complex to interpret as acceleration is the double differential of displacement/position, the gyroscope provides angular rotation, and the magnetometer provides information about direction. The acceleration measures include both the static earth component and the dynamic movement components, which cannot readily be distinguished numerically. Clearly, this requires more information than a relatively simple transform from linear acceleration to relative position (Stamm et al., 2013; Worsey et al., 2018). The Attitude and Heading Reference System (AHRS) (Madgwick et al., 2011) is a commonly used data fusion algorithm based on the 9 IMU measurements (3 linear acceleration, 3 angular rotation and 3 axis magnetometer) to determine absolute orientation and movement in space. There are unpublished reports that the movement through the water can distort the magnetometer readings used in AHRS calculations, however.
Design and simulation of sensor fusion using symbolic engines
Published in Mathematical and Computer Modelling of Dynamical Systems, 2019
AHRSs [9] are used to maintain an accurate estimate of objects 3D orientation (i.e. roll, pitch and heading) as the objects move in 3D space [4]. AHRS commonly use IMU sensor set that includes accelerometer, gyroscope and magnetometer. In dynamic scenarios, speed may be also used to assist AHRS. AHRS has a wide range of applications such as navigation, machine control, haptics and augmented reality [10], gimbal systems [11] and unmanned aerial vehicles (UAV) stabilization. Several approaches exist for AHRS design.