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Innovations in Mine Safety Engineering
Published in Debi Prasad Tripathy, Mine Safety Science and Engineering, 2019
Vehicle tracking systems use GPS for locating the vehicles combined with software that collects fleet data for a comprehensive picture of vehicle locations, as shown in Figure 10.17. The vehicle locations can be accessed on a digital map or on specialized software.
IoT-based child tracking using RFID and GPS
Published in International Journal of Computers and Applications, 2023
Nadia Ahmed, Sadik Kamel Gharghan, Ammar Hussein Mutlag
In another paper, Al-Fedaghi and Atiyah [42] proposed a vehicle tracking system using GPS and GSM, which utilized the Thinking Machine (TM) technique to improve tracking tools. The authors explained that the TM approach combines mechanical devices and software at an operational base to locate and monitor a vehicle’s position, timing, and mobility; however, the system was still complex. Zein et al. [43] introduced a mechatronic system for autonomous vehicles that can save and recall a path using GPS and a digital map. Additionally, the system can detect obstacles and bumps autonomously. They constructed a small-scale vehicle for experimental purposes that employed multiple sensors and an Arduino Mega microcontroller running C++ programs. To enable the vehicle to autonomously follow a particular path, it needed to travel along that path at least once so that the GPS could record and store it. According to the study’s results, the system had an error range of 0.5–1 meter.
Trajectory tracking control of tracked vehicles considering nonlinearities due to slipping while skid-steering
Published in Systems Science & Control Engineering, 2022
Ahmad Al-Jarrah, Mohammad Salah
In this control technique, the desired speeds (i.e. angular velocities) of tracks motors are adjusted to compensate for the slipping while skid-steering via a high-level controller. Figure 6 shows the vehicle tracking system for SCFLC and SCPIC, which they have the same structure except for the algorithm used to adjust the desired speeds of the tracks motors. The position and orientation errors are utilized to adjust the desired tracks motors angular speeds affected by the slipping while skid-steering. The adjusted desired angular speeds, and , are then utilized in the vehicle dynamics by a low-level controller to overcome the total longitudinal resisting force and the turning resisting moment exerted on the tracks by the ground, M and R, and to ensure that the tracked vehicle tracks a prescribed desired trajectory with a constant desired vehicle speed as shown in Figure 7. It should be noted that the vehicle inverse kinematics are utilized to generate the desired angular speeds, and , from the desired trajectory represented by and (refer to Equation (5)). Due to slipping, those speeds must be adjusted using a high-level controller as mentioned earlier.
Real-time motion trajectory based head-on crash probability estimation on two-lane undivided highway
Published in Journal of Transportation Safety & Security, 2020
Nazmul Haque, Md. Hadiuzzaman, Fahmida Rahman, Mohammad Rayeedul Kalam Siam
To determine the overtaking status, high-resolution traffic data is required. The traffic data can be obtained using Intelligent Transportation System (ITS)-based data collection system. For example, vehicle trajectory extraction using image processing can be used to obtain more detailed traffic data from the field (Oh, Oh, & Min, 2009; Saunier & Sayed, 2008). Using video sensors, several automated systems have been developed for traffic monitoring. For instance, Saunier and Sayed (2008) propose a framework for estimating crash probability for two vehicles at an intersection by a vision-based vehicle tracking system. In another study, Oh and Kim (2010) develop a methodology for estimating rear-end crash potential using individual vehicle trajectory data extracted by vision-based software.