Hybrid Single Board Computer System for Unmanned Ground Vehicle

The single-board computer (SBC) is a compact, powerful and cost-effective system on chip (SOC) that can perform all complex tasks that may be executed by the desktop computer. Further, with the development of the SBCs having a powerful processor and high RAM support–they are already deployed in many applications such as in agriculture, home security, automation, drones, unmanned ground vehicle (UGV) and like-wise. However, each SBC has a unique capability and therefore some SBC may perform better than others depending upon the application. Further, some SBC has high processing power but they are either costly or support fewer I/O pins. Similarly, some SBC are cost-effective, has a high number of I/O pins but they have quite less processing power for executing complex instructions and tasks. In addition, for some SBC there is no dedicated integrated development environment (IDE) available that can be used to program SBC according to the user-specific need. Due to these above-mentioned problems, the user has to either invest in the high processing power SBC (having high price) but compromise with less I/O pins or invest in the SBC (having low price) that has the high number of I/O pins but compromise with low processing power (although there are some SBCs available in the market that has high processing power along with the high number of I/O pins. But they are available at a quite high price so we are not considering these SBCs). In both cases, the user is not able to integrate intelligent high processing sensors and equip the system in which SBC is used with new features over time. To over this problem, we are introducing a smart and highly intelligent hybrid implementation of Raspberry pi model 3b+ with Arduino UNO board for controlling UGV. During testing, we have found that our developed hybrid SBC system for UGV work quite impressively. The CPU utilization and CPU temperature readings of the hybrid system are noted while performing various complex tasks on the UGV. Further during the evaluation process, it is observed that UGV consumed a maximum of 80% of system CPU utilization to perform all the allocated tasks efficiently. The rest 20% of the CPU usage as well as some of the GPIO pins of Raspberry pi are reserved and can be used to implement various complex applications in the UGV based on the user requirement.