Introduction
The advancement of robotics technology has grownin recent years, offering substantial potential across various industrial sectors. One prominent application area is logistics, where robotic systems can play a vital role in automating material handling processes to improve operational efficiency and reduce human labor. Despite this progress, challenges remain in achieving improved accuracy, speed, and load-handling capabilities. Among the available robotic solutions, the line follower robot stands out as a simple yet effective approach for automating transportation tasks. Designed follower robots have been widely implemented in industrial settings due to their low cost, ease of deployment, and relatively simple control systems.
The selection of a line follower robot in this study is driven by several considerations. First, the technology offers simplicity and efficiency, making it well-suited for small-to medium-scale logistics operations. Second, it is cost-effective and composed of affordable components, which supports its use in resource-constrained environments. Third, it offers flexibility; route modifications can be achieved by reprogramming or physically altering the track. Lastly, the architecture is scalable, allowing for future upgrades in terms of payload or sensor integration. Several previous studies have explored the development of line follower robots. Ridarmin et al. (2019) proposed a prototype utilizing an Arduino Uno and TCRT5000 sensors for tracking a dark line, demonstrating basic autonomous navigation. Susilo (2018) introduced a prototype for automatic object delivery that incorporated a load cell sensor to determine the object’s weight and delivery destination, showcasing an early attempt at functional integration for logistics applications.
While these studies laid the foundational work, challenges remain in increasing navigation accuracy, improving payload handling, and optimizing system integration for practical use cases. This study aims to address these challenges by designing and developing an autonomous line follower robot capable of transporting lightweight objects (up to 100 grams) along a fixed path. The proposed system integrates real-time navigation and load transport using an Arduino UNO microcontroller, BFD-1000 infrared sensors (as a more accurate alternative to TCRT5000), and an L298N motor driver for efficient motor control.
The novelty of this work lies in its optimized design for power-efficient movement, enhanced sensor precision, and application in small-scale logistics environments to an area that remains under explored research. This approach is intended to contribute to the development of accessible and low-cost automation solutions for small and medium-sized enterprises (SMEs). The development of smart mobile robots based on line follower technology has been extensively studied and applied across various fields, particularly in logistics and healthcare industries. This technology enables robots to follow predetermined paths using infrared sensors that detect color contrasts between the line and the background surface. Mahendra et al. (2019) and Hossain et al. (2021) demonstrated that line-following navigation systems offer high reliability in structured indoor environments and are relatively low-cost to implement. In the context of object transportation automation, this approach has proven effective for tasks involving the delivery of goods or lightweight materials from one location to another without direct human involvement.
Beyond navigation technology, another critical aspect of such robotic systems is the ability to carry or push objects. Studies by Rathore et al. (2019) and Kale et al. (2020) discuss the design of actuators and robotic mechanisms to lift or push objects automatically. The integration of additional sensors, such as ultrasonic modules, has also been explored to enhance obstacle detection and navigation safety. Recent innovations even incorporate Internet of Things (IoT) connectivity, as discussed in Hossain (2021), enabling real-time monitoring and control of the robot. Therefore, a line follower-based robotic system equipped with object-handling capabilities presents a promising solution for efficient and adaptive internal transport automation.
Experimental Setup
In this study, we design the system usingan Arduino UNO microcontroller, which functions as the processor for both incoming and outgoing data. The components are integrated into a single structural frame, including motorized wheels that serve as the base support for the BFD-1000-linesensor, which is responsible for detecting the navigation path. The frame of the line follower robot is constructed from acrylic material, with the robotic arm positioned at the topmost section to facilitate efficient object pickup and placement. The following sections present the system block diagram and the workflow diagram of the object transfer robot based on line follower navigation.
The L298N driver is used to control both the rotational speed and direction of DC motors. It receives power from a 5V input, which can be supplied either through the 5V output of the microcontroller or from a step-down voltage regulator. The driver receives control signals from the microcontroller to determine whether the motor should move forward, turn, or stop. Additionally, the microcontroller sends speed control signals based on the programmed instructions, allowing the motor to operate at the desired speed when moving forward or turning.
The BFD-1000 sensor is used as the path detection component for the line follower robot. A total of five BFD-1000-linesensors are employed and calibrated using
potentiometers. The calibration process is carried out to determine the appropriate infrared light intensity received by the photodiode sensor, enabling it to differentiate between high and low logic levels. This calibration is optimized for a sensor height of approximately 0.8 cm above the reflective surface.
Robotic Arm Design
The robotic arm is designed to assist in the picking and placing of objects. It utilizes four servo motors that function as the gripper and actuators for movement. The servo motors are directly connected to the microcontroller without the use of an external driver. The microcontroller sends control signals to the servo motors, instructing them on the direction and angle of rotation, thereby enabling the robotic arm to grasp and place objects as required.The robotic arm is assumed to consist of nrevolute joints (rotary joints), each driven by a servo motor. The robot operates in a 2D or 3D environment. Each joint contributes an angular rotation denoted by θi, and each arm segment has a length Li.The base frame is fixed. For a planar 2D robotic arm, the end-effector position(x,y) is calculated using:
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