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Multisensor Precise Positioning for Automated and Connected Vehicles
Published in Hussein T. Mouftah, Melike Erol-Kantarci, Sameh Sorour, Connected and Autonomous Vehicles in Smart Cities, 2020
Mohamed Elsheikh, Aboelmagd Noureldin
An inertial navigation system (INS) is a navigation system that includes a navigation processor and an inertial measurement unit (IMU). The IMU consists typically of accelerometers to measure specific forces (acceleration) and gyroscopes to measure angular rotation rates. The INS is an example of dead-reckoning navigation systems. It is autonomous, which means it does not need an external reference and only needs the knowledge of the initial position, speed, and heading information. Moreover, it gives a high output rate of at least 50 Hz compared to the low GNSS data rate which is typically around 10 Hz [12]. Despite the significant advantage of being a standalone and weather- and environment-independent system, the INS has a short-term accuracy. In the long term, the INS solution drifts dramatically away from the correct navigation parameters due to the inherent sensor errors. The mathematical integration of the IMU measurements to obtain the velocity and position causes that any small bias in any of the sensors will quickly grow larger with time [13].
Trajectory Planning in Autonomous Vehicles using GPS and Digital Compass
Published in P. C. Thomas, Vishal John Mathai, Geevarghese Titus, Emerging Technologies for Sustainability, 2020
Fundamentally, the autonomous vehicles can be seen as mobile robots, which can look around, recognize the environment, understand the traffic scenario, if any, make intelligent decisions and act upon them, similar to the way in which humans drive the vehicles. So one needs the basic hands, legs and body to be found in the autonomous vehicles; mechanisms to understand what the eyes see and mechanisms to instruct the legs to move as desired and some memory to remember the happenings. The present condition of autonomous vehicles suggests the use of state-of-the art sensors and technologies like LiDARs, RADARs, Ultrasonic Sensors, cameras, SLAM, DATMO, etc. These intelligent vehicles will require accurate and robust positioning systems to fulfil the demands of a wide range of applications. The self-driving car is an example of an emerging technology where accuracy and robustness are critical requirements for safe guidance and stable control. Current vehicular positioning systems are dominated by Global Navigation Satellite Systems (GNSS) such as the Global Positioning System (GPS). A common approach to enhance overall accuracy is the integration with other sensors such as inertial measurement units (IMU). IMU are used to develop inertial navigation systems (INS).
Flexible and Stretchable Devices for Human-Machine Interfaces
Published in Muhammad Mustafa Hussain, Nazek El-Atab, Handbook of Flexible and Stretchable Electronics, 2019
Irmandy Wicaksono, Canan Dagdeviren
Extensive studies have been conducted to perform activity recognition and gait analysis with micro-electro-mechanical systems (MEMS) inertial sensors attached to multiple body segments (Tao et al. 2012). To sufficiently classify body motion in normal daily activities, inertial measurement units (IMUs) must be able to sense accelerations with amplitude from −12 to +12 g and frequency of up to 20 Hz (Bouten et al. 1997). An IMU typically comprises single or multiple accelerometers, a gyroscope, and a magnetometer. It collectively measures 3-dimensional linear acceleration, angular rate, and magnetic field, respectively. Body-worn inertial sensors enable the calculation of angles around the body joints based on their orientations relative to one another. These combined joint angles and other markers from the IMUs can be collected and processed in real-time to characterize gestures. Ultimately, users activity can be recognized or even predicted from this subsequent set of motions (Seel et al. 2014, Yang and Hsu 2010).
Quantifying cricket fast bowling volume, speed and perceived intensity zone using an Apple Watch and machine learning
Published in Journal of Sports Sciences, 2022
Joseph W. McGrath, Jonathon Neville, Tom Stewart, Hayley Clinning, Bernd Thomas, John Cronin
If BV and intensity can be recorded effortlessly with minimal equipment outlay, this may inform a player’s training decisions, reduce injury rates, and improve performance – particularly when recorded over a day, month, year, or multiple seasons. Researchers could also use this information to examine more precise relationships between bowling volume, intensity, and injury. A possible practical solution to measure bowling volume and intensity is to use an inertial measurement unit (IMU). An IMU usually consists of an accelerometer, gyroscope, and magnetometer. An accelerometer measures linear acceleration (measured in g-force), the gyroscope measures angular velocity (degrees per second), while the magnetometer measures the strength and direction of the local magnetic field. IMU’s have a low relative cost and are accessible to most of the world’s population through smart devices (i.e., smartphones and smartwatches) (McGrath et al., 2020). This is important as most of the cricketing population lives in developing nations.
Development and testing of a wearable wrist-to-forearm posture measurement system for hand-tool design evaluation
Published in International Journal of Occupational Safety and Ergonomics, 2021
Michail Karakikes, Dimitris Nathanael
Beyond its much lower cost, the proposed system presents a number of advantages in comparison to common motion-tracking solutions (electrogoniometers, optoelectronic systems, magnetic tracking systems). First, compared to electrogoniometers, this technology allows for simultaneous measurements in all planes of motion (P/S, F/E, R/UD), whereas a dual-axis electrogoniometer can only measure the bend of a wire in two axes (F/E, R/UD). In order to measure P/S, a torsiometer (measuring wire twist) is required as well. Moreover, electrogoniometers are somewhat bulky and intrusive, obstructing natural motion patterns [9]. Second, in comparison to optoelectronic systems, which are considered the benchmark in motion tracking, the proposed system is more mobile, less complicated to operate, has a significantly shorter set-up time and does not suffer from issues pertinent to cameras (e.g., occlusions, defocusing, lighting) [33]. Finally, magnetic tracking systems can be either cumbersome due to power requirements (when body-mounted) or only allow for a limited working range in space, near the magnetic transmitters [34,35], while IMUs are small and do not require external components for reference. The main drawback of IMU technology is the issue of drift, inherent in gyroscopes, which sometimes imposes recalibration of the sensors, disrupting the experiment and limiting the time of potentially continuous recordings.
Vision-based load sway monitoring to improve crane safety in blind lifts
Published in Journal of Structural Integrity and Maintenance, 2018
Yihai Fang, Jingdao Chen, Yong K. Cho, Kinam Kim, Sijie Zhang, Esau Perez
In the robotic tower crane system proposed by Lee et al. (2009), a laser sensor was employed to measure the elevation of the load. However, this system configuration of a laser sensor and reflection board cannot reliably measure the load elevation during excessive load sway. To advance upon Lee et al.’s approach and take the load sway into consideration, Fang and Cho (2015) proposed a system framework in which inertial measurement unit (IMU) sensors are introduced to measure and report the load sway. An IMU is an electronic device where the onboard sensors (e.g., accelerometers, gyroscopes, and magnetometers) measure velocity, orientation, and gravitational forces of the body it is attached to. When mounted to the load, the wireless IMU sensor measures the orientations of the load in all three axes (i.e., pitch, roll, and yaw). Assuming the hoist cable is rigid and with cable length known, the orientation measurements were further transformed to the relative position to obtain the coordinate of the load in the three-dimensional space. Fang et al. (2016a) further validated the performance of the IMU-based method in a series of field experiments with a telescopic boom crane. The results indicate using the IMU-based method in conjunction with the encoder sensors was able to accurately position the load during sway with an average error of 0.43 m. Nevertheless, it was observed that the accuracy and reliability of the IMU data could be compromised due to magnetic and electronic interferences. Although not common, these circumstances present in the vicinity to power lines or in metallic environments such as OPs.