Journals of Mechatronics Machine Design and Manufacturing

Robotic Technologies and Intelligent Systems in Autonomous Vehicles: A Comprehensive Review

2026-04-07T06:13:56+00:00

Autonomous vehicles (AVs) represent one of the most transformative developments in modern transportation systems. By integrating robotics, artificial intelligence, and advanced sensing technologies, autonomous vehicles are capable of perceiving their environment, making intelligent decisions, and executing driving tasks with minimal or no human intervention. This study presents a comprehensive review of robotic technologies used in autonomous vehicle systems, focusing on the fundamental components that enable perception, planning, decision-making, and control. The study analyses recent advancements in sensor technologies, intelligent algorithms, and vehicle communication systems that collectively contribute to the development of safe and reliable autonomous driving. The research highlights the importance of perception systems that allow autonomous vehicles to understand their surrounding environment. Modern autonomous vehicles utilize multiple sensors such as LiDAR, radar, cameras, ultrasonic sensors, and inertial measurement units (IMU) to collect real-time data about the road environment. Sensor fusion techniques combine data from these sensors to generate accurate environmental models and improve object detection, localization, and obstacle recognition. The integration of these sensing technologies significantly enhances situational awareness and enables vehicles to operate safely even in complex and dynamic traffic conditions. Another major focus of this paper is behaviour-aware motion planning and decision-making algorithms used in autonomous vehicles. Intelligent algorithms such as reinforcement learning, Markov decision processes, deep neural networks, and game theory enable autonomous vehicles to predict the behaviour of surrounding vehicles and pedestrians. These algorithms help vehicles determine optimal driving actions such as lane changing, speed adaptation, and obstacle avoidance while maintaining safety and efficiency. Additionally, vehicle control strategies including proportional-integral-derivative (PID) control and model predictive control (MPC) are discussed as key techniques for ensuring accurate trajectory tracking and vehicle stability.

Artificial Intelligence and Robotics: Evolution, Integration, Applications, and Ethical Implications in Modern Society

2026-03-12T04:07:06+00:00

Artificial intelligence (AI) and robotics are reshaping the technological and social landscape of the 21st century. AI equips machines with the ability to learn, reason, and adapt, while robotics provides the physical embodiment that allows intelligent systems to interact with the world. Together, they enable autonomous systems capable of performing complex tasks across diverse sectors, from healthcare and manufacturing to agriculture, transportation, and space exploration. This study traces the evolution of AI-powered robotics from early mechanical automation to contemporary intelligent systems, highlighting enabling technologies such as machine learning, computer vision, natural language processing, and sensor-based control. It compares artificial and human intelligence, raising philosophical questions about autonomy, consciousness, and responsibility. Beyond technical progress, the study synthesizes ethical, social, and economic implications, including workforce transformation, algorithmic bias, privacy concerns, and trust in intelligent systems. To address these challenges, the paper introduces the human-centered AI-robotics integration model (HCAIRM), a layered conceptual framework that embeds ethical governance, transparency, and human oversight directly into system architecture. This analysis highlights that AI-powered robotics can enhance productivity, precision, and quality of life, but only if guided by responsible governance and human-centered design principles. The trajectory of these technologies will depend not solely on technical innovation, but on how societies choose to integrate them responsibly into human ecosystems.

Parametric Analysis, Modeling, and Optimization of Vibration-Assisted Electrical Arc Machining of Advanced Engineering Materials

2026-01-03T06:13:24+00:00

Electrical Arc Machining (EAM) has emerged as a promising unconventional machining process capable of machining advanced materials with high Material Removal Rates (MRR). This research presents a comprehensive investigation of Vibration-Assisted Electrical Arc Machining (VEAM) applied to Aluminum-Boron Carbide (Al-B₄C) Metal Matrix Composite (MMC) and Ti-6Al-4V alloy. An innovative VEAM setup was developed, incorporating a mechanical motion arc-breaking mechanism with tool vibration capabilities. The study involved parametric analysis using Box-Behnken design of experiments, mathematical modeling using Response Surface Methodology (RSM) and Artificial Neural Networks (ANN), and optimization using hybrid ANN-evolutionary algorithms. For Al-B₄C MMC machining, the Sine Cosine Algorithm (SCA) demonstrated superior performance, achieving MRR improvements of 40% and Tool Wear Rate (TWR) improvements of 12%, respectively. During Ti-6Al-4V alloy machining, the Rao-3 algorithm provided optimal results with 7.4% improvement in MRR and 9.93% reduction in TWR. ANN models proved significantly more accurate than regression models, with correlation coefficients exceeding 0.98 and negligible mean square error. The developed VEAM process achieves MRR approximately 50 times higher than conventional electrical discharge machining, establishing it as a viable alternative for precision machining of advanced materials. This research contributes critical knowledge regarding vibration-assisted unconventional machining, artificial intelligence-based modeling, and evolutionary optimization of complex manufacturing processes.

IoT-based Coal Mine Monitoring Robot

2026-03-20T09:14:29+00:00

Because of unpredictable gas leaks, sudden tremors, moisture accumulation, and unstable underground conditions, coal mines continue to be among the most hazardous working environments. This project presents an ESP32-based environmental monitoring and safety alert system designed exclusively for underground coal mine safety. The system employs an ESP32 microcontroller integrated with MEMS-based sensors to continuously monitor critical environmental and structural parameters in real time. The proposed system focuses on detecting hazardous conditions within coal mines by tracking temperature, humidity, vibrations, ground inclination, and the presence of harmful gases such as carbon monoxide (CO) and methane. Local edge processing on the ESP32 enables rapid analysis of sensor data and immediate triggering of safety alerts when predefined thresholds are exceeded. To ensure timely communication even in remote mining locations with limited internet connectivity, the system delivers alerts through SMS notifications as well as a cloud- based dashboard accessible via web and mobile applications. Designed for deployment in harsh and infrastructure-limited environments, the system supports solar-powered operation and wireless sensor networking for scalable coverage across underground mine sections. Field testing demonstrated reliable hazard detection and prompt alert dissemination within seconds of unsafe condition detection. This cost-effective and scalable solution enhances worker safety by providing continuous underground monitoring and early warning capabilities, thereby improving operational safety and emergency preparedness in coal mining environments.

Holistic Optimization: Integrating Mechanical, Electrical, and Ergonomic Design for BLDC Motor-driven Mobility Strategies

2026-01-06T08:35:28+00:00

Long-term adaptability, efficient functioning and performance of BLDC motor-driven mobility systems rely on the mindful combination of their electric subsystem, mechanical architecture, ergonomic interface, and maintenance. The present study is a multi-domain analysis that integrates mechanical, electrical, and human-centered optimization, in order to help in the construction of advanced mobility devices. The proposed work begins with the definition of the system architecture and defining the initial design requirements, such as the specifications of the BLDC motors, gear ratios, structural geometry, and user interfaces. These parameters are discussed with a view to coming up with the best spatial settings as well as functional interdependencies within the system. To help in performance assessment and design optimization, a comprehensive 3D CAD representation was created and connected to multi-physics simulations that included mechanical behavior, electrical performance and the ergonomic interaction. With this type of virtual prototype, it was possible to identify an interference, dimensional-check processes, and accurate investigations of assemblies. It was also used to facilitate iterative enhancement, standardization of parts and initial evaluation of electrical performance and human-machine interface. Simultaneously, an optimization system was introduced to attain high reliability, improved operational efficiency and better user comfort. The design goals were set according to performance goals, efficiency goals and ergonomics, and the optimization strategy took into account mechanical design constraints, electrical power management constraints and interface ergonomics constraints. The combination of CAD modeling with performance simulations and ergonomic analysis allowed obtaining the explicit design requirements and operational recommendations to enhance the usability of the device and minimize the chances of mechanical or user-related errors. Altogether, the suggested holistic solution ensures the reliability of the devices, their better operational performance, cost-effective life cycle management, and user satisfaction. The multi-domain optimization, digital modeling and human-centred design proved to be a systematic and replicable approach that can be used in a broad spectrum of mobility technologies or BLDC motor-driven.