Advancements in Embedded System Design and Robotic Applications

Inhaltsverzeichnis

1. Frontmatter

2. Embedded Systems for Power and Energy Optimization

   1. Frontmatter

   2. Simulation and Analysis of a Voltage-Mode Controlled Buck Converter for Energy Harvesting Application

      Anirudh G. Savanur, Sujata Kotabagi, Nikhil N. Kakatkar, R. M. Vikas, Sumeet S. Inamdar

      Abstract

      This project explores the design and performance of a voltage mode controlled buck converter for Energy Harvesting-Power Management ICs (EH-PMIC). By employing voltage-mode control, the buck converter regulates output voltage through duty cycle adjustments based on the deviation from a reference voltage. The study presents a thorough examination of the theoretical principles, design considerations, and practical implementation of this control method. Simulations and experimental results validate the converter’s performance, with an input voltage (V_in) of 2.5 V, output voltage (V_out) of 1.5 V, and load current (I_load) of 50 mA. The design is implemented in UMC 180 nm technology. The findings highlight the voltage-mode controlled buck converter’s effectiveness in achieving precise voltage regulation and high efficiency, making it suitable for diverse power management applications.

   3. State-Space Modeling and Observer-Based Fault Detection in Buck DC-DC Converters

      Satyawan Jagtap, Dagadu More

      Abstract

      This paper presents a model-based approach for performance analysis and fault detection in non-isolated buck converters, focusing on the impact of soft faults, such as gradual component degradation, and hard faults, including short-circuit or open-circuit failures, on the converter’s dynamic behavior. Pre-fault and post-fault state-space models are developed to extract fault signatures, which serve as key indicators for fault detection and identification. A state observer is designed to generate residuals, which are then utilized in a diagnostic algorithm to accurately identify faults. The research includes detailed modeling of state-space dynamics, analysis of capacitor equivalent series resistance (ESR) effects, and simulation of fault scenarios to diagnose inductor and capacitor soft faults as well as hard faults in the switch.

   4. AI-Driven Optimization of a Cascaded H-Bridge 11-Level Converter Using Reinforcement Learning

      B. Priya, M. Kanimozhi, M. Rajasubasri, V. Aakash, M. Suresh Kumar

      Abstract

      In real time, the performance of a cascaded H-bridge 11-level converter is optimized using reinforcement learning (RL) in this paper. Traditional methods generally fail to maintain power quality and adapt to changing load conditions, leading to increased total harmonic distortion (THD) and inefficiency. The RL agent continuously learns from the system's statesuch as output voltage and current and adjusts the switching of H-bridge modules dynamically to reduce switching losses, minimize THD, and maintain balanced direct current DC-link voltages, with Deep Q-Network (DQN) algorithms. The simulation results show that RL-based controllers significantly improve efficiency to 98%, reduce THD 2.45%, and enhance fault tolerance compared to conventional approaches, demonstrating the effectiveness of artificial intelligence (AI)-driven control when optimizing multilevel converters in complex, real-time environments in MATLAB/Simulink. This research work is aligned with Sustainable Development Goal (SDG) 7. The improved fault tolerance and reduced THD support reliable and sustainable energy systems also contribute to cleaner and more efficient energy distribution.

   5. Q Learning and Deep Deterministic Policy Gradient Method for Energy Optimization in HVAC System

      Manu Sharma, Reeba Qureshi, Deepika Kumar, Shivam Kotalia, Vaijayanthi Sambath Kumar

      Abstract

      This paper presents a novel methodology to optimize energy in commercial buildings. It explores two different techniques in reinforcement learning, Q-Learning and DDPG. A custom environment has been made using OpenAI GYM for testing purposes. Through this environment, an agent was trained to balance thermal comfort and energy consumption in simulated HVAC systems. The graph generated to showcase Q-Learning’s results showcased many fluctuations, thereby not giving a reliable output. DDPG (Deep Deterministic Policy Gradient), on the other hand, shows an increase in the reward value over time; the result graph always shows an upward trend. Two major metrics: reward and energy consumption are used to measure performance. DDPG provided better results as compared to Q-Learning, showing that reinforcement learning has the potential to improve the efficiency of HVAC systems, therefore offering energy optimization. The insights gained through this research can be used to develop intelligent systems with enhanced comfort and sustainability in future. By leveraging advanced RL techniques, stakeholders in the built environment sector can optimize HVAC system operations, reduce energy consumption, and minimize environmental impact, thereby advancing the paradigm of smart and eco-friendly building management practices.

   6. Soft Computing-Based Optimal Solar Tracking and MPPT

      Ankur Thakuria, Kankan Jyoti Kalita, Junpaa Barman, Kismita Saharia, Mridusmita Sharma

      Abstract

      Conventional solar tracking systems usually adjust the position of the solar panel based on position of the sun in the sky excluding other factors which may affect the power production. This system implements a new method for tracking solar position utilizing machine learning algorithms to estimate the best tilt angle with respect to extra climatic parameters including temperature, relative humidity, and GHI. The developed system also has a built-in feature of evaluating and actually applying different kinds of Machine Learning models such as the Feedforward Neural Network (FNN) and Random Forest that can improve the tilting angle forecast and further optimize the output power. Furthermore, it integrates a Maximum Power Point Tracking (MPPT) algorithm, which helps in dynamically varying the duty ratio of a boost converter to help in stabilizing and optimizing the power delivery. The performance of the proposed system is substantiated via accurate data analysis and power estimation techniques along with vast enhancements over more conventional solutions with respect to power and efficiency.

3. Robotics and Intelligent Automation

   1. Frontmatter

   2. Optimized Path Planning for Indoor Environments with Ant Colony and Curve Smoothing Algorithm

      Sudeep Sharan, Duc Thuan Nguyen, Anh Tong Ngoc Minh, Juan José Domínguez-Jiménez, Peter Nauth

      Abstract

      In this technological-world of high speed, where every operation needs to be performed instantaneously and more efficiently, scientists and engineers have started focus on the meta-heuristic methods, which are more strong and adaptable, and their use to solve the autonomous problems of the mobile robots to navigate on any environment, the Ant Colony Optimization (ACO) algorithm is one of such solution assists in solving the problem of robot path planning (PP). Thus, in order to migrate its shortcomings and enhance the robustness and quick execution, this paper proposes a new way of using the Ant Colony (AC) concept, which ensures to solve the limitations of traditional ACO, such as computational execution time. The modified algorithm built on the condition-based rules, named the Condition-Based Ant Colony Concept (CB-ACC). The algorithm was tested on three different maps to examine the output efficiency and the computational execution time, as well as the path distance. The results show that the proposed algorithm is completely efficient in small-scale environments and remarkably better in large-scale environments. Moreover, we update the CB-ACC to improve the path distance. We then simulate the algorithm in the same environments and discover its efficiency in achieving an optimal smooth path using the Bezier curve. Simulation results show the efficiency of CB-ACC, as well as how effective it is for static-complex environments. Moreover, this paper presents the efficiency of the algorithm on different types of maps.

   3. Automata-Driven Fire Rescue Bot: Leveraging NFA and TM for Efficient Pathfinding

      Sharanya Vanraj Thambi, G. Ishvarya, Kavya Sree Kammari, Niharika Panda

      Abstract

      Fire rescue operation scenarios are a hazardous environment that requires quick responses as well as accurate judgment. This paper presents the automata-driven fire rescue bot, an intelligent system that leverages non-deterministic finite automata (NFA) and Turing machines (TM) to optimize pathfinding and decision-making. The JFLAP tool has been used to simulate NFA and TM mechanisms. Pygame has been used to implement a working model of the environment and movements of the bot by using the condition-based mechanism of NFA. The study demonstrates how foundational concepts from the theory of computation can be applied to design a practical, scalable, and intelligent fire rescue solution.

   4. Comparative Analysis of Finite Automata and Pushdown Automata for an Elevator System

      Advik Narendran, Anantha Hothri, Yashaswini Manyam, Srinidhi Sundaram, Niharika Panda

      Abstract

      Finite Automata (FA) and Pushdown Automata (PDA) is the computational model used in developing systems of different complexities. FA works well with static states and simple transitions which can be applied to elevator controls with simple operations such as moving floors and controlling door operations. However, being memoryless, it would not be able to satisfy dynamic requests, prioritize actions to be performed, or go back to floors that were skipped. PDA solves this problem by using a stack to store and process floor requests hierarchically, thereby making the system capable of dealing with nested and complex situations. This paper compares these two models based on parameters such as memory usage, design complexity, real-world applicability, and computational efficiency. FA is found to be perfectly suited for simple state-driven operations with low computational overhead while PDA excels at managing advanced functionalities at the cost of increased complexity. This result gives insight into trade-offs between simplicity and capability for the selection of a proper computational model for an elevator system based on its operation demands.

4. Embedded System Design and Validation

   1. Frontmatter

   2. Multilevel Crop Image Segmentation Using Two-Dimensional Histogram on Raspberry Pi

      Arun Kumar, A. Kumar, Amit Vishwakarma

      Abstract

      This paper presents the multilevel crop image segmentation using a non-local mean two-dimensional (2D) histogram approach. Initially, the 2D histogram is formed with a greyscale and a non-local mean. Moreover, the produced 2D is prone to the slime mould technique when applied with 2D Renyi’s entropy for multilevel segmentation. The performance of the proposed technique is evaluated in terms of fidelity parameters and compared to firefly and beta differential algorithms to evaluate the efficacy. The proposed technique achieved an improvement of 7.37%, 5.97%, 4.52%, and 4.66% of the average peak signal-to-noise ratio with the firefly algorithm and beta differential algorithms at threshold levels 2, 5, 8, and 16, respectively. Correspondingly, the improvement in average root mean square error is 6.25%, 14.70%, 12.96%, and 7.82% at the same threshold levels. Further, the proposed technique is verified with the Raspberry Pi hardware platform. The proposed technique has great potential to be used in the realm of agriculture for image processing applications.

   3. Sustainable UAV-Assisted Data Collection in Wireless Sensor Networks Using Renewable Energy and Wireless Charging Platforms

      Jhalak Dutta, Smita Das, Abhijit Sinha, Anwesha Goswami, Sneha Lahiri, Uddipto Jana

      Abstract

      Unmanned aerial vehicles (UAVs) are increasingly being utilized in wireless sensor networks (WSNs) to facilitate data collection from distributed sensor nodes. The proposed model addresses the challenges of limited flight endurance and energy inefficiency by integrating renewable energy sources, specifically solar power, along with wireless power transfer (WPT) for recharging. By optimizing the UAV’s energy consumption and managing recharging through solar energy and WPT stations, the model ensures sustained data collection operations. Simulated environments with varying wind conditions, sensor densities, and altitudes were used to evaluate the UAV’s performance. The results demonstrate the model’s ability to enhance operational endurance, minimize energy consumption, and ensure efficient data collection, making it a promising approach for long-term WSN applications and other energy-intensive scenarios.

   4. ISTA 6A Drop Test Simulation Model Validation System Design

      Ahmet Batuhan Günaltay, Hasan Çalık, Ahmet Salih Adalı, Aytaç Gören

      Abstract

      This paper presents the design, implementation, and validation of an embedded system for validation of drop test simulations in accordance with the International Safe Transit Association 6-Amazon.com (ISTA 6A) standard, which is developed for the packaging of various products. The objective of this work is to reduce testing costs and accelerate the process by enhancing the accuracy of simulation models for drop tests. The embedded system includes real-time measurements using strain gauges (SG), a 9 Degree of Freedom (DoF) Inertial Measurement Unit (IMU), and data logging capabilities. Validation was performed using polymethyl methacrylate (PMMA), with the results showing a correlation between simulations and real-world tests within an acceptable tolerance for the real-life drop tests. The findings suggest that the system can effectively improve the accuracy of simulation models, particularly for complex composite materials for drop test simulations.

   5. Modernizing and Validating Safety Product

      Neha Agarwal, Monika Mangla, Richa Sharma, Sharvari Patil, Priyanca Gonsalves

      Abstract

      The need of enhanced safety and security in our technologically advanced society is growing, so the development of highly sophisticated facial recognition system has become not only essential but also a matter of utmost importance. The idea behind this paper was to put in place measures that would include facial recognition among other security systems. Its objective is to improve accuracy, efficiency, and overall security standards within different industries. The paper’s primary purpose is transforming security protocols in different industries. It will provide holistic security systems that not only react to potential risks, but also proactively detect them and then solve them. The main aim is to create inconspicuous ways of noticing any breakages in the system of safety like ensuring appropriate use of fire extinguishers, timely theoretical sources of smoke or fire and undertaking severe measures of detecting intruders into restricted areas. For more information about the paper the real-time safety equipment detection system can be incorporated which would notify users immediately if they fail to comply. Additionally, the integration incorporates a smoke/fire detector which helps for early warning The administration dashboard functions as an all in-one hubit provides comprehensive control over everything, allows real-time alerts and notifications for effective monitoring.

   6. Development of Low-Cost Electronic Controller Unit for Hybrid Electric Vehicles Using Communication Area Network Protocol

      S. Ganishvar, R. Harish, B. Harshitha, Mutyam Gayathri Priya, K. R. M. Vijaya Chandrakala

      Abstract

      The development of Electronic Control Units (ECUs) is one of the aspects that increases the efficiency and functioning of modern hybrid vehicles. The design and integration of microcontroller-based ECUs were added to the features of the Controller Area Network (CAN) protocol. Using CAN bus communications, it intends to build a strong and efficient platform for quick data transfer between different vehicle subsystems. The approach further refines the control of the operation, optimizing parameters like energy management and vehicle speed. Configuration of the microcontroller units (MCUs) to monitor and control critical functions of the hybrid vehicle such as powertrain, battery management, and regenerative braking, as well as communication through the CAN bus, has assured reliable and high-speed transmission of important data such as State of Charge (SOC), speed, and other key parameters. The system architecture integrates these MCUs with sensors and actuators, forming an interlinked network that is capable of dynamic decision-making to improve energy efficiency. This paper provides an example of how simple microcontroller-based ECUs can be employed in hybrid vehicles to streamline communication and control. The CAN bus integration enables scalable and flexible vehicle architectures for future advancement in hybrid and electric vehicle technologies. The development also provides inputs toward intelligent vehicle systems supporting future automobile trends for energy-efficient and sustainable transport solutions.