Showing 2 results for Integration
M.r. Emami Shaker , A. Ghaffari, A. Maghsoodpour, A. Khodayari,
Volume 7, Issue 4 (12-2017)
Abstract
The Global Positioning System (GPS) and an Inertial Navigation System (INS) are two basic navigation systems. Due to their complementary characters in many aspects, a GPS/INS integrated navigation system has been a hot research topic in the recent decade. The Micro Electrical Mechanical Sensors (MEMS) successfully solved the problems of price, size and weight with the traditional INS. Therefore they are commonly applied in GPS/INS integrated systems. The biggest problem of MEMS is the large sensor errors, which rapidly degrade the navigation performance in an exponential speed. Three levels of GPS/IMU integration structures, i.e. loose, tight and ultra tight GPS/IMU navigation, are proposed by researchers. The loose integration principles are given with detailed equations as well as the basic INS navigation principles. The Extended Kalman Filter (EKF) is introduced as the basic data fusion algorithm, which is also the core of the whole navigation system to be presented. The kinematic constraints of land vehicle navigation, i.e. velocity constraint and height constraint, are presented. A detailed implementation process of the GPS/IMU integration system is given. Based on the system model, we show the propagation of position standard errors with the tight integration structure under different scenarios. A real test with loose integration structure is carried out, and the EKF performances as well as the physical constraints are analyzed in detail.
Mr. Rahmatulah Karimipoor, Dr. Mansour Hakimelahi, Dr. Masoud Masih Tehrani,
Volume 16, Issue 2 (6-2026)
Abstract
In this study, the design and implementation of an intelligent path – finding robot capable of simultaneously following a predefined track and avoiding obstacles are presented. The main objective was to enhance the performance of mobile robots through the integration of data from infrared (IR) and ultrasonic sensors and by establishing precise coordination between control algorithms and hardware components. To achieve this, and Arduino Uno microcontroller was employed as the central processing unit, an L298N motor driver was used to regulate the speed and direction of the motors, and infrared sensor was utilized for line detection, and an ultrasonic sensor was incorporated for obstacle identification.
The system was first simulated in the Proteus software environment to verify the accuracy of the algorithms and the synchronization of components. Afterward, a physical prototype was constructed, and a series of practical experiments were conducted to evaluate its precision and efficiency. The results demonstrated that the designed robot could accurately follow the designated path and effectively adjust its trajectory when encountering obstacles, without significant deviation or delay. The discrepancy between the simulation and experimental data was found to be less than five percent, indicating strong coherence between the software and hardware. From and application standpoint, the developed robot can serve as a valuable tool in robotics education, internal transportation systems, and small – scale automation projects.
