A study on obstacle detection for IoT based automated guided vehicle (AGV)

In modern manufacturing industry context, Automated Guided Vehicles (AGVs) are mobile robots used in aiding the process of material handling from one point to a desired location in the workplace. Development on AGVs is a trending discussion among researchers due to its wide usage and the implementation of the Industrial 4.0. The performance of the AGVs is one of the spotlight issues in order to compete with the rising technologies and demanding workload in the industry. Current concern on obstacle detection in AGVs has become more subjective due to the uprising issue on the congested environment in the workplace. Therefore, an IoT system is developed to allow a flexible wireless communication among mobile robots in an indoor industrial environment. An AGV prototype is designed for obstacle detection and the performance is analyzed. In order to improve obstacle detection in the AGV prototype, Kalman Filtering (KF) algorithm is used in the signal filtering for the HC-SR04 Ultrasonic Sensor data acquisition. Furthermore, the communication verification is diagnosed for the Wi-Fi connection broadcast traffic and network latency. The study produced several significant results related to obstacle detection of AGV with IoT based technology. Experiment results show the vehicle movement reaction in dealing with obstacles. Next, the KF programming algorithm managed to diminish the measurement noise including the Main bang and flaw echo of the ultrasonic sensor. Moreover, the IoT system allows ease of accessibility and user-friendly GUI. Results proved that Wi-Fi is a reliable communication medium in obstacle detection and is comparable to ZigBee and Bluetooth connection. The communication delay for wireless connectivity on the AGV prototype surpassed the requirements of latency test, which is less than 20 ms. The research concludes that the IoT-based AGV prototype is competent and reliable in handling the obstacle detection in fixed route with indoor environment workplace