Smart Mobility Robot: Employing Line Follower Navigation for Object Movement

 

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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A Generative Neural Network for Psychological Traits in Workplace Self-confidence: A Physics-inspired Approach

 

Introduction

The term “workplace self-confidence” describes an individual’s belief in their ability to carry out duties, make wise choices, and successfully handle obligations in a work environment. In addition to being a vital component of a person’s mental and emotional health, it also plays a significant role in determining their level of productivity, job performance, and general well-being. Psychological characteristics including self-awareness, self-efficacy (belief in one’s own abilities), emotional intelligence (capacity to regulate emotions and interpersonal interactions), and selfesteem are essential to professional confidence. These traits are dynamically influenced by both external environmental stimuli and internal cognitive states. High selfconfidence among employees increases their chances of taking on new tasks, taking part in decision-making, and proactively solving problems, all of which increase workplace productivity. 

Furthermore, by lowering anxiety, boosting resilience, and cultivating a positive view on career advancement, self-confidence promotes mental health. This is especially noticeable when workers feel overqualified and have creative self-confidence because they are more likely to act creatively and improve workplace performance. Research on the psychological effects of artificial intelligence (AI) reveals how incorporating AI into the workplace can affect workplace self-confidence by influencing personal traits like trust, anxiety, and selfefficacy. According to research, workers who have more self-efficacy and previous technological experience are more likely to trust AI, which increases their confidence in their ability to use AI tools efficiently. Like human trust, trust in AI is essential for enabling staff members to take an active role in task management and decision-making, which raises overall productivity. 

Dynamical systems within cognitive agent models are designed to simulate workplace environments, taking into account varying factors such as job demands, leadership styles, and social influences to predict and support psychological traits like self-efficacy and emotional regulation. These models have the potential to gradually boost workplace self-confidence by encouraging positive feedback through helpful AI-driven interactions. Additionally, ethical considerations in AI design are crucial for developing systems that improve user well-being by avoiding bias, regulating emotional states, and incorporating compassion, which promotes a more positive, self-assured, and productive work environment. Research on smart systems for cognitive computing demonstrates how AI can integrate fundamental cognitive functions like language processing and expert knowledge representation to resolve ambiguities in human-computer interactions. In order to improve cognitive processing and enable machines to assist complex decision-making with human-like reasoning and intuition, this collaborative intelligence model blends AI and human intelligence (HI). 

The study demonstrates the potential of neural networks in refining predictive models, highlighting their adaptability in diverse contexts. Building on this adaptability, the integration of ontology-based approaches, as discussed in, offers a novel pathway to enhancing psychotherapy interventions, showcasing the versatility of computational intelligence in varied domains. Similarly, the work in highlights how ensemble learning can effectively handle high-dimensional data, enabling precise classification and prognosis in complex scenarios. In a related vein, the use of handwriting analysis in personality assessment, as illustrated in [15], underscores the potential of machine learning in psychological profiling,
emphasizing its broad applicability across disciplines. Furthermore, the study in [16] exemplifies how neural networks can be leveraged for accurate forecasting in energy generation systems, demonstrating their efficacy in addressing practical challenges beyond traditional boundaries.

Neural network architecture

The neural network model, as depicted in Fig. 1, is structured to simulate the progression and stabilization of psychological traits by integrating dynamic cognitive states. This architecture consists of an input layer, a hidden layer utilizing physics-inspired transformations, and an output layer to generate adaptive temporal traits. The input layer processes three core cognitive statesself-esteem, self-efficacy, and self-concept-capturing their fluctuations due to environmental factors and internal feedback. These cognitive states act as the foundation for generating complex psychological traits. Moving through the hidden layer, the model incorporates a Maxwell-Boltzmann distribution to represent how these cognitive states fluctuate
initially, akin to the dispersion of particle speeds in physics. This distribution allows the model to simulate initial instability in cognitive states before they begin to settle. To further shape the output, a sigmoid function is applied within the hidden layer, introducing non-linear scaling that drives the cognitive states towards equilibrium. 

Motivation, Learned Helplessness, and Social Anxiety are the final long-term psychological traits produced by the output layer, which represent the consistent results of the underlying cognitive processes. The traits that are produced reflect the way that cognitive states stable and change over time, providing information about how both adaptive and maladaptive traits evolve in response to work environments. A visual flow from initial inputs reflecting cognitive states through their distribution and change inside the hidden layer to the appearance of psychological traits in the output layer may be seen in Fig. 1. The physics of stability and stabilization can be used to form complex psychological traits in a structured neural network, as this model provides. In this model, interpretability is achieved by leveraging the Maxwell-Boltzmann distribution, which provides a statistically interpretable representation of variability within cognitive states, such as self-esteem, self-efficacy, and selfconcept. 

This distribution operates within an equilibrium framework that visually illustrates how each cognitive state stabilizes over time. By framing cognitive fluctuations as distributions converging toward equilibrium, the model makes the dynamic stabilization process both transparent and accessible, enhancing interpretability.

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Hybrid Deep Learning for Climate Prediction with Temporal, Spatial, and Environmental Data

 

Introduction

Climate change is one of the most urgent and complex challenges faced by humanity today. Its widespread impact is felt across ecosystems, economies, and human societies, altering the natural balance that sustains life on Earth. Rising global temperatures are melting glaciers, increasing sea levels, and intensifying extreme weather events. Also the biodiversity loss and agricultural disruptions threaten the stability of ecosystems and food security. These changes demand a deeper understanding of the Earth’s climate dynamics to predict and mitigate their consequences effectively. Traditional climate models, based on statistical and numerical approaches, have laid the foundation for understanding climate patterns. However, they face significant challenges when applied to the large, diverse, and interconnected datasets generated by modern climate monitoring systems. These limitations highlight the need for advanced computational models that can comprehensively analyze climate data and provide actionable predictions.

Understanding climate data complexity

Climate data is inherently multidimensional, capturing the interactions between temporal patterns, spatial variations, and human-induced influences. Each dimension offers unique insights into the processes shaping the Earth’s climate system. Time series data, such as temperature, precipitation, and greenhouse gas concentration measurements, are essential for understanding long-term trends, detecting anomalies, and forecasting future states. These datasets reveal patterns of global warming, seasonal fluctuations, and extreme weather events. However, temporal data alone cannot provide a complete picture, as it lacks information about how these changes vary across different regions or ecosystems.

Spatial data, such as satellite imagery, complements temporal datasets by offering a detailed view of the Earth’s surface. High-resolution images capture phenomena such as deforestation, glacier retreat, urban expansion, and vegetation health. These datasets allow researchers to assess the direct impact of climate change on specific regions and ecosystems. Their sheer volume and complexity present challenges in extracting meaningful insights. Advanced machine learning techniques are required to process these high-dimensional datasets and detect subtle changes that are often overlooked by traditional approaches.

Socioeconomic and environmental indicators provide another critical layer of information by linking human activities to climate change. Indicators such as CO2 emissions, energy consumption, urban development, and deforestation rates highlight the anthropogenic drivers of climate dynamics. These indicators also reveal the socioeconomic consequences of climate change, such as resource scarcity, economic instability, and public health challenges. Despite their importance, integrating these indicators with temporal and spatial data into a unified framework remains a complex task. This work requiring innovative modeling approaches that account for interactions between diverse data types.

Significance of the research

This study holds significant potential for advancing climate prediction by integrating multiple data dimensions into an unified framework. Existing models are often limited in their ability to holistically analyze interconnected factors or provide interpretable outputs. By leveraging advanced deep learning methodologies, such as TCNs for time series data, CNNs for spatial data, and Explainable AI for interpretability, this research seeks to address these limitations comprehensively.

One key outcome of this research is improved prediction accuracy, which enables more precise forecasts of climate variables like temperature and precipitation. These predictions are vital for developing effective strategies to mitigate and adapt to climate change impacts. Enhanced transparency, achieved through Explainable AI techniques, ensures that model outputs are interpretable, fostering trust among policymakers, researchers, and the general public. Additionally, actionable insights derived from the integrated framework empower stakeholders to implement targeted interventions. Such as optimizing land use policies, mitigating deforestation, or improving urban planning to address climate risks.

This unified approach bridges gaps between diverse data modalities, enabling a deeper understanding of climate dynamics. By providing a scalable and interpretable solution, this research contributes to global efforts to combat climate change, supporting data-driven strategies for sustainable development and environmental preservation.

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