dc.description.abstract |
With the recent increases of mobile robot deployment that rely on robot operating system (ROS), new
challenges have emerged as a result of the hardware requirements imposed by ROS on the host
computer. Installing ROS on a mobile robot requires the target robot to be equipped with a full
personal computer (PC) with specific specifications. However, deploying ROS on such PC’s will
introduce new issues such as increased size, weight, cost, and power consumption.
This research presents the development and implementation of a fully integrated standalone tennis
balls collecting mobile robot using ROS. The operating system is deployed on a compact, low-cost,
low power consumption, light weight and embedded single board computer (Raspberry Pi 4). The
robot goal is to assist playground attendees by collecting scattered tennis balls. This is accomplished
by integrating and implementing a miniature series of algorithms that construct the robot tasks. These
algorithms are used to detect objects, classify them, plan optimal paths, and avoid obstacles. During
the implementation process, a significant challenge arose in the form of a high computational load on
the main processing unit (CPU). The vision detection algorithm is to blame for this. This was resolved
by using a lighter version of the algorithm, which reduced the computational load.
The proposed method was investigated in this work. The results show that a single board computer
(Raspberry Pi 4) can complete the required objectives and run the algorithms within acceptable
constraints. The vision algorithms performed as expected, detecting all of the objects in the robot
workspace. However, the Raspberry Pi requires a longer execution time than a standard PC to perform
vision tasks. The extra time is due to the Raspberry Pi's hardware resource limitations, as well as the
limitation on utilizing hardware acceleration abilities. Keep in mind that hardware acceleration
employs the graphical processing unit to address vision algorithms in order to shorten execution time.
Furthermore, the A* algorithm was used to help the robot find the shortest obstacle-free path. Other
algorithms are in charge of formulating the wheel's trajectory and control law. All of the robot
algorithms were coded to use fewer computational resources, resulting in less extra execution time.
As a result, the robot is able to complete the tasks in a reasonable amount of time. Finally, the
proposed low-cost solution was shown to be capable of running ROS-based mobile robot algorithms. |
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