International Journal of Robotics and Autonomous Systems ISSN: 3107-7382 (Online)

Authors: Abhishek Bajpai, Aman Bhardwaj, Manish Sharma, Lakshya Kumar, Pooja Tripathi

Abstract: Robotics is one of the most interesting and fastest growing industry in recent times. The reason being the fact that it makes use of most of the branches of engineering namely – Mechanical Engineering, Electrical Engineering and Computer Science. Today, Robotics is one of the most rapidly growing field, as technological advancements continue; researching, designing and building new robots serves various practical purposes, whether commercial, domestic or military. Robotic Classifier is an automated robot that can classify an object based on its structural property. It can sense the hardness and softness of the object with the help of various sensors and can classify them. The basic reason of choosing ROBOTICS as a project is that we want to make a robot that can work automatically and classify the objects as hard or soft on the basis of their structural properties and collect the specified type of object.

Authors: Vikash Singh, Anshika Sharma, Vinit Kumar, Sukanya, Poornima Gupta

Abstract: The intention of this paper is to reduce human involvement in security and surveillance activities. This machine is developed for providing security excellence in favor of mankind so that more and more human life can be saved. The main function of this machine is to transmit real time video records to the base station where it can be visual. Base station is nothing but just a mobile which is used to give commands to the machine. This operation is achieved by a wireless camera located at the top of the machine. In addition to this it also have much more features based on sensors like live human detection, metallic body detection, and LED flasher. These features make this machine more compatible and acceptable to different categories such as military applications, industrial use and for social use also. Use of renewable energy is one of the foremost concern of modern era and in this machine solar energy is effectively used to provide energy to the whole system by charging up the chargeable battery. Due to high range of controlling this machine can be operated wherever the mobile signals are available as it uses DTMF technology which is based on mobile keypad tone.

Authors: Dr. Sneha R. Malhotra, Mr. Karthik Bansal

Abstract: Computer-Aided Manufacturing (CAM) and robotics have become key enablers of Lean Manufacturing (LM) in today's dynamic industrial environment. The convergence of CAM with robotic automation supports lean objectives by reducing waste, improving workflow efficiency, enhancing production flexibility, and enabling rapid customization. This paper explores the significance, integration techniques, and impact of CAM-robotics synergy within lean manufacturing settings. Challenges related to interoperability, standardization, and scalability are discussed, along with opportunities created by smart factory initiatives and Industry 4.0. Furthermore, a comparative study of traditional manufacturing and lean systems powered by CAM and robotics is presented. The paper concludes by underlining best practices for successful integration, offering insights for manufacturers aiming for lean excellence.

Keywords: CAM, Lean Manufacturing, Robotics, Smart Factory, Automation, Industry 4.0, Integration, Interoperability

Authors: Dr. Aakash Mehra, Prof. Neha Verma

Abstract: Machine learning (ML) techniques are increasingly being integrated into Computer-Aided Manufacturing (CAM) to enhance the predictive capability and automation of machining processes. Surface roughness is a critical metric in manufacturing, impacting product quality, performance, and cost. Traditional predictive models often fall short in capturing complex, nonlinear interactions among process parameters. This paper presents a comprehensive review and implementation strategy of machine learning algorithms—such as artificial neural networks (ANN), support vector machines (SVM), and decision trees—for predicting surface roughness in milling, turning, and grinding processes. The integration of ML within CAM environments enhances adaptability, real-time optimization, and closed-loop control, leading to superior surface finish and reduced production costs. Key challenges in data collection, model training, and real-time deployment are discussed along with recommendations for future developments in smart manufacturing systems 

Keywords: Surface Roughness, Machine Learning, CAM, Artificial Neural Networks, SVM, Smart Manufacturing, Process Optimization

Authors: Dr. Arvind Deshmukh, Ms. Meenal Rao

Abstract : Reinforcement learning (RL) has emerged as a powerful paradigm for enabling robots to learn complex tasks through interaction with their environment. In robotic assembly automation, where variability, precision, and adaptability are critical, RL provides a mechanism for machines to develop decision-making policies that optimize performance over time. This paper explores the implementation of reinforcement learning algorithms—such as Deep Q-Learning, Proximal Policy Optimization, and Actor-Critic methods—in industrial assembly lines. By simulating trial-and-error behavior, these methods enable robotic agents to perform complex assembly operations like insertion, alignment, and fastening with minimal human supervision. The study highlights the benefits, challenges, and future prospects of integrating RL into smart robotic systems for automated assembly.

Keywords: Reinforcement Learning, Robotic Assembly, Automation, Deep Q-Learning, Smart Manufacturing, Actor-Critic, PPO

Authors: S.Sanjay Karthick, T.R.Balaji, S.Puliyuran, M.EazhisaiVallabi

Abstract: Robot is a machine that is specially programmed by a user or computer. Robot is mainly used for carrying things with complex action and it can be guided by external control device. This article depicts that controlling of robot is based on hand gesture movement. The gesture movement is done with the help of 3-axis accelerometer (i.e.ADXL 335).It works based on tilting the accelerometer in various directions. Based on the accelerometer directions the robot is controlled. An accelerometer is placed on the hand that is used to transmit the signal that is configured with Arduino UNO and the controlling the motor is done with NI-MYRIO using LabVIEW. Based on the transmitting device value the robot movement is controlled in various directions like forward, backward, left, right and stop. The movement of indication is viewed in LabVIEW.

Authors: Vikash Singh, Anshika Sharma, Vinit Kumar, Sukanya, Poornima Gupta

Abstract: Use of renewable energy is one of the main concerns of the modern world. Solar energy is one of the greatest and effective sources of renewable energy. So using solar energy every kind of electrical or mechanical device or machine can be operated and here we are using the same concept to operate our spy robot. Spy Robot can be a multitasking embedded system which can be worked under several technologies to control its operations like RF signalling or DTMF technology. In this project solar power is used to provide power supply to the machine so that it can operate multiple tasks like live human detection, metal detection, etc. Each unit is consisting of its own typical circuitry which is to be controlled by the mother unit of the machine i.e., microcontroller. DTMF technology is been used for transmitting and receiving the command from base station to the operating station. The base station is nothing but just a mobile phone who’s each key indicate a different command for the operating station i.e., the robot. One of the main features of the machine is its capability to operate in the night which is achieved by night vision camera.

Authors: Dr. Ravi Bansal, Ananya Ghosh

Abstract : Sensor fusion has become a cornerstone in the advancement of mobile robotic systems, particularly in achieving real-time localization and mapping. Simultaneous Localization and Mapping (SLAM) has emerged as a critical task in autonomous robotics, relying on the integration of various sensors such as LiDAR, GPS, IMU, and vision systems. This paper explores modern techniques in sensor fusion that enable robust SLAM in dynamic and unstructured environments. The work delves into Kalman Filters, Particle Filters, and deep learning-based fusion approaches. A comparative analysis of these techniques is presented, highlighting their effectiveness in indoor and outdoor navigation. Emphasis is laid on accuracy, computational efficiency, and adaptability. Real-world applications in autonomous vehicles, drones, and service robots are discussed to showcase practical relevance. The study concludes with insights into the challenges, limitations, and future research directions of sensor fusion for SLAM.

Keywords: Sensor Fusion, SLAM, Mobile Robots, Localization, Mapping, Kalman Filter, Deep Learning.

Author: Dr. Neha Tiwari

Abstract:  The integration of collaborative robots, or cobots, into industrial automation has revolutionized modern manufacturing systems by enhancing productivity, flexibility, and above all, safety. Unlike traditional robots confined to isolated zones, cobots are designed to work side-by-side with humans. This paper explores the methodologies, architectures, and adaptive algorithms that enable safe and efficient human-robot collaboration (HRC) in industrial settings. Focusing on risk assessment frameworks, real-time monitoring systems, ergonomic considerations, and smart sensor integration, the study presents a comprehensive evaluation of the benefits and challenges of HRC. Case studies in automotive and electronics industries are analyzed to demonstrate practical implementations. The paper concludes with forward-looking perspectives on ethical challenges and the growing role of artificial intelligence in future HRC systems.

Keywords: Collaborative Robots, Industrial Automation, Human-Robot Collaboration, Safety Systems, Real-Time Monitoring, Smart Sensors, Artificial Intelligence.

Author : Prof. Arvind Nair

Abstract:  Autonomous navigation remains a cornerstone in the advancement of intelligent robotic systems. The complexity of real-world environments presents significant challenges for traditional navigation algorithms. This paper explores the application of Deep Reinforcement Learning (DRL) for path planning and navigation in autonomous systems. DRL combines the perception capabilities of deep learning with the decision-making prowess of reinforcement learning, enabling robots to learn optimal navigation strategies in dynamic and unknown environments. Key architectures such as Deep Q-Networks, Actor-Critic models, and Proximal Policy Optimization are discussed in detail. Performance metrics, environment setups, and real-world case studies are included to illustrate the practical impact and limitations of DRL in autonomous navigation. This study concludes by highlighting future research directions and the potential of DRL to transform path planning in robotics.

Keywords: Autonomous Navigation, Path Planning, Deep Reinforcement Learning, Deep Q-Networks, Actor-Critic, Proximal Policy Optimization, Robotics.

Author: Dr. Meenal Rajput

Abstract:  Swarm robotics is an emerging discipline within robotics that focuses on the coordination of large numbers of relatively simple robots. These systems are inspired by the collective behavior of social insects and animals, aiming to achieve complex global objectives through local interactions. This paper explores adaptive control algorithms tailored for swarm robotics operating in unstructured environments, where traditional path-planning and mapping strategies often fail. We examine bio-inspired control techniques, reinforcement learning methods, and decentralized decision-making frameworks that enhance swarm adaptability, robustness, and scalability. The paper also evaluates the effectiveness of these adaptive algorithms through simulated and real-world case studies. Ultimately, the study underlines the potential of adaptive control in enabling autonomous swarm systems to operate efficiently in unpredictable and dynamic environments.

Keywords: Swarm Robotics, Adaptive Control, Reinforcement Learning, Decentralized Systems, Unstructured Environments, Autonomous Agents.

Authors:Anwar A Patel, Yogita Mistry

Abstract: : 

Mobile Robots & automation are used to pick and place objects with artificial intelligence. This is used to reduce man power and perform the task accurately and rapidly. This is also helpful in hazardous and high temperature area where human being can not work. [1]. Pick and place Robot is the most effective technology in industrial applications where it is specially designed to be used in manufacturing industries for pick and place functions. This will reduce the human efforts in industrial operations in case of lifting the objects. The pick and place robot consists of a robotic vehicle and robotic arm placed on it, with a soft catching grip to grab the objects with it [3]. The robotic movements and pick and place functionality everything can be controlled by the Mobile application. This pick and place function is most useful in the industries in abnormal conditions and unusual places where a human being cannot enter such as in high temperature and narrow areas.
The micro controller used in it receives the commands from the mobile phone and controls the DC motor connected to the robotic wheels and as well pick and place arm which is rotate in three dimensional [4].

Keywords: Hybrid Gripper Latest Technology; six to twelve degree of freedom; mobile app automation; Liquid handing technology; Automotive Guided Vehicle; modern software techniques.

Authors: Dr. Ritesh Ahuja, Ms. Shruti Menon

Abstract : Mass customization blends the efficiency of mass production with the flexibility of individual customization, aiming to meet unique customer demands at scale. With rapid advances in automation, intelligent systems driven by artificial intelligence (AI), machine learning (ML), and industrial Internet of Things (IIoT) are revolutionizing this domain. This paper explores how intelligent automation enables dynamic production scheduling, adaptive supply chains, and customer-driven design while maintaining high quality and low costs. Case studies across multiple industries are reviewed to show how technologies like digital twins, cyber-physical systems (CPS), and robotic process automation (RPA) are shaping the future of personalized manufacturing. Challenges in implementation and directions for future innovation are discussed to foster scalable, smart customization ecosystems

Keywords: Mass Customization, Intelligent Automation, Smart Manufacturing, AI in Industry, Digital Twins, IIoT, Robotics, CPS

Authors: Dr. Aakash Mehra, Prof. Neha Verma

Abstract: Machine learning (ML) techniques are increasingly being integrated into Computer-Aided Manufacturing (CAM) to enhance the predictive capability and automation of machining processes. Surface roughness is a critical metric in manufacturing, impacting product quality, performance, and cost. Traditional predictive models often fall short in capturing complex, nonlinear interactions among process parameters. This paper presents a comprehensive review and implementation strategy of machine learning algorithms—such as artificial neural networks (ANN), support vector machines (SVM), and decision trees—for predicting surface roughness in milling, turning, and grinding processes. The integration of ML within CAM environments enhances adaptability, real-time optimization, and closed-loop control, leading to superior surface finish and reduced production costs. Key challenges in data collection, model training, and real-time deployment are discussed along with recommendations for future developments in smart manufacturing systems.

Keywords: Surface Roughness, Machine Learning, CAM, Artificial Neural Networks, SVM, Smart Manufacturing, Process Optimization

Author: Dr. Sneha Kulkarni

Abstract: Bio-inspired robotics is revolutionizing the field of robotics by drawing inspiration from nature to enhance mobility, adaptability, and efficiency in robotic systems. This paper explores the principles of biomimicry applied in robotic design, examining how natural models such as insects, mammals, fish, and birds have shaped innovations in movement, terrain navigation, and environmental interaction. By mimicking biological structures and control mechanisms, bio-inspired robots demonstrate superior flexibility, agility, and resilience, enabling operations in complex and unpredictable environments. The paper reviews the state-of-the-art techniques in actuation, control, and morphology, highlights key applications in search and rescue, exploration, and medical robotics, and evaluates ongoing challenges such as material limitations, energy efficiency, and sensor integration. This study affirms the transformative potential of bio-inspired robotics in advancing the next generation of intelligent machines capable of seamlessly adapting to their surroundings.

Keywords: Bio-inspired robotics, biomimicry, adaptive mobility, robotic morphology, environmental adaptability.

Authors: Dr. Ayaan Mehta, Sneha Kapoor

Abstract: The rise of autonomous military robots presents significant ethical and legal challenges, particularly in warfare where human lives are at stake. These robots, equipped with artificial intelligence, can independently assess threats and engage in combat without direct human intervention. However, this technological advancement raises concerns about accountability, international humanitarian law, and moral decision-making. This paper explores the complexity of deploying autonomous robots in military operations, examining the current legal frameworks, potential violations of human rights, and the ethical dilemma of allowing machines to make life-and-death decisions. Through a multi-disciplinary approach, we analyze the pressing need for global governance structures and propose actionable recommendations to mitigate risks associated with this rapidly evolving domain.

Keywords: Autonomous Robots, Military Ethics, Legal Framework, Artificial Intelligence, Humanitarian Law, Accountability, Robotics Warfare

Authors: Dr. Ananya Rao, Ritvik Sharma

Abstract: Artificial Intelligence (AI) is increasingly becoming a crucial part of Computer-Aided Manufacturing (CAM), particularly in the domain of tool path optimization. Tool path generation is a core function in CAM software, determining the efficiency, accuracy, and productivity of machining processes. Traditional tool path generation methods rely on heuristic or deterministic algorithms, which often fall short in handling complex geometries or multi-objective criteria. AI techniques such as machine learning, genetic algorithms, and neural networks enable dynamic, adaptive, and intelligent tool path planning, leading to improved surface finish, reduced machining time, and lower tool wear. This paper presents a detailed analysis of the application of AI in tool path optimization, highlighting key methods, advantages, and challenges. Furthermore, the paper explores real-world implementations and the future potential of integrating AI with CAM systems for intelligent manufacturing. </div>

Keywords: Artificial Intelligence, Tool Path Optimization, CAM, Machine Learning, Genetic Algorithm, Smart Manufacturing, CNC, Deep Learning