Journal of Mechanical Robotics

Optimization of Single and Multi-robot Work Cell Layout: Extended Literature

2026-02-16T05:42:28+00:00

In contemporary manufacturing systems, high productivity, operational efficiency, and flexible automation are all dependent on the design of the work cell layout. This study offers a thorough examination and in-depth analysis of optimization techniques used for work cell layout challenges involving one or more robots. Although heuristic and rule-based solutions have been fundamental to layout design, they frequently fail to meet the growing complexity of modern robotic work environments. Researchers have improved the quality of layout decisions in recent decades by incorporating sophisticated mathematical models, such as combinatorial optimization and mixed-integer programming. Additionally, meta-heuristic and hybrid algorithms like genetic algorithms, particle swarm optimization, ant colony optimization, and simulated annealing have gained popularity for resolving high-dimensional and non-linear work cell layout problems as a result of Industry 4.0 and complex manufacturing tasks. Additional difficulties for multi-robot systems, like task assignment, path planning, collision avoidance, inter-robot communication, and dynamic reconfiguration, necessitate integrated optimization algorithms that take operational sequencing and spatial organization into account. The approaches are summarized, research gaps are noted, and prospects for work cell layout optimization in increasingly autonomous and collaborative robotic systems are suggested in this extensive literature review. The goal of robot work cell layout optimization is to arrange robots, equipment, and accessories in a way that minimizes cycle time, workspace size, and energy consumption while guaranteeing effective, collision-free operations. Robot work cell layouts are designed configurations of industrial robots and auxiliary machinery (such as conveyors, machines, and tools) that are maximized for productivity, security, and minimal robot movement. Reducing cycle times, guaranteeing safety, and optimizing workplace usability are important design factors. In this work, an attempt has made to optimize the cycle time of the work cell by reducing the traveling path of the robot arm using metaheuristics method. This extended literature review synthesizes these methodologies, identifies research gaps, and proposes future directions for optimizing work cell layouts in increasingly autonomous and collaborative robotic environments.

Intelligent Bio-mimetic Raking System: Design of a Load-responsive, Bluetooth-enabled Waste Collection Robot

2026-02-11T06:03:52+00:00

This study presents the design and implementation of a bio-mimetic robotic arm system for autonomous lawn maintenance, inspired by conventional rake mechanics. The rake head, configured with tines at a 90° angle to the shaft, ensures efficient engagement with debris. Using ANSYS 3D modelling, the system underwent rigorous dimensional optimisation and stress analysis before fabrication. The robotic arm comprises five integrated subsystems: rake head, servomotor, tension cable, lawn tractor interface, and control electronics. The rake head is mounted via an adjustable-angle connector secured with M13 bolts, while the arm attachment is anchored to the tractor’s reinforced frontal frame using M10 bolts. A servomotor, bonded with vibration-resistant adhesive, drives angular displacement through a 1.5 mm tension cable, enabling precise lifting and lowering of the rake head. Torque calculations, based on gravitational force and radial distance, validate the mechanical dynamics of the system, with a design torque of 78.4 kg-cm. Control is achieved via an Arduino Nano-based system, integrating Bluetooth communication, single-channel relays, and a mobile application interface for wireless operation. Mobility is provided by a custom lawn tractor chassis powered by dual wiper shaft motors and four size 8 tires, ensuring terrain adaptability. The tractor also houses the control electronics and a detachable waste compartment for debris collection. This modular, programmable architecture supports real-time responsiveness and autonomous functionality, offering a scalable solution for residential and commercial lawn care. The system’s integration of mechanical precision, wireless control, and mobility establishes a robust platform for intelligent environmental maintenance.

Artificial Intelligence in 3D Printing: Paving the Way for Smarter Manufacturing

2026-03-10T05:32:10+00:00

Additive manufacturing (AM), commonly known as 3D printing, has become a transformative technology across various industries due to its ability to produce highly complex geometries with reduced material waste and lower production costs. Despite its advantages, optimizing AM processes remains a significant challenge. Several factors, including material characteristics, machine parameters, and environmental conditions, directly influence the quality and reliability of printed components. Managing these variables effectively is essential to ensure consistent performance and reduce defects. Artificial intelligence (AI) has emerged as a powerful tool to address these challenges. This study explores the integration of AI techniques such as machine learning (ML), deep learning (DL), and computer vision to enhance AM process optimization. It reviews recent advancements in AI-driven material selection, defect detection, predictive modeling, and real-time process monitoring, with a focused case study on metal additive manufacturing. These intelligent approaches enable improved accuracy, reduced production costs, enhanced efficiency, and better product quality. However, the adoption of AI in AM is not without limitations. Challenges such as high computational requirements, data dependency, and integration complexity must be addressed. The paper concludes by highlighting future research directions and emphasizing AI’s critical role in advancing smart, autonomous, and sustainable manufacturing systems.

Robot Vision System: How the Robot Vision System Works in Industries

2026-02-16T05:23:35+00:00

Robot vision systems have recently become a backbone for Industry 4.0 because they enable intelligent perception and autonomous decision-making for robots within an industry. Because of current advancements in camera resolution capabilities and computing power from computer vision techniques, robot vision systems have enabled industry robots with accurate image information perception capabilities. This paper does an exhaustive review of the conceptual functioning of robot vision systems used in industry and includes details on industry robot vision system design, image sensor technology, image preprocessing, feature extraction processing, and computer vision-based decision-making processes. The paper also does a review of current use of robot vision systems for key industry applications, including autonomous inspection processes for industry components, precision assembly processes for industry components, object recognition systems for industry components, industry component defect recognition systems, and real-time industry monitoring systems. It gives information on current benefits of robot vision system use for industry applications, including accuracy, speed, consistency, and productivity enhancements, along with current technology-related hurdles, including lighting effects and computing complexities.

Low-power IoT Networks for Autonomous Farm Robotics and Sensor Fusion: A Comprehensive Review

2026-02-06T08:15:55+00:00

The integration of low-power internet of things (IoT) networks, autonomous farm robotics, and multimodal sensor fusion is rapidly transforming modern agriculture into a data-driven, intelligent, and sustainable ecosystem. Low-power wide-area networks (LPWANs) such as LoRaWAN, NB-IoT, and Sigfox play a critical role by enabling large-scale, long-range, and energy-efficient communication among distributed sensor nodes deployed for soil, crop, environmental, and livestock monitoring, even in remote rural regions. At the same time, autonomous agricultural robots, including unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and multi-arm robotic manipulators, are increasingly used to automate labor-intensive tasks such as precision seeding, targeted spraying, mechanical weeding, harvesting, crop scouting, and yield estimation, thereby improving productivity and reducing resource wastage. Multimodal sensor fusion techniques that combine data from ground-based sensors, RGB and multispectral vision systems, LiDAR, hyperspectral imaging, and localized weather stations enable real-time situational awareness, robust perception, and accurate decision-making under dynamic field conditions. This review synthesizes findings from more than fifty peer-reviewed studies published between 2020 and 2025, focusing on system architectures, communication protocols, edge- and cloud-based data processing, AI-driven analytics, and intelligent agricultural robotics. Key challenges such as energy optimization, intermittent rural connectivity, latency constraints, interoperability across heterogeneous devices, multi-robot coordination, and scalability in large-scale deployments are critically examined. Furthermore, emerging research directions, including 6G-enabled robotic swarms, digital twin-based farm modeling, nano-IoT sensing, soft robotics, and intelligent edge computing, are explored as promising pathways for next-generation smart farming systems. Overall, this work presents a unified conceptual framework to guide future research and practical deployment of low-power IoT-enabled autonomous agriculture.