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

Author: Prof. Sushmita Rao

Abstract: Swarm robotics is an emerging field that mimics the behaviour of social insects, such as ants, bees, and termites, to solve complex tasks autonomously. This paper explores the advancements in swarm robotics with a focus on its applications in industrial automation. The paper highlights the integration of swarm intelligence algorithms, communication systems, and autonomous decision-making in industrial environments. It discusses the challenges and solutions involved in deploying swarm robots for tasks like assembly, packaging, inspection, and maintenance in industries. Furthermore, it delves into the benefits of swarm robotics, including flexibility, scalability, robustness, and efficiency in industrial processes. The potential future directions of swarm robotics in industrial automation are also discussed, along with key developments and trends shaping this technology.

Keywords: Swarm robotics, industrial automation, swarm intelligence, autonomous robots, industrial processes, communication systems, flexibility, scalability, efficiency.

Authors: Simran Kaur, Mayank Bhardwaj

Abstract: Autonomous Underwater Vehicles (AUVs) are pivotal in a range of marine applications, such as environmental monitoring, underwater exploration, and military operations. Effective path planning for AUVs is essential to optimize mission efficiency, reduce energy consumption, and enhance their ability to navigate complex underwater environments. With the advancement of Artificial Intelligence (AI) technologies, AI-driven path planning offers promising solutions to these challenges. This paper explores the integration of AI algorithms, such as Reinforcement Learning (RL), Deep Learning (DL), and Genetic Algorithms (GA), into the path planning systems of AUVs. We analyze the challenges faced by AUVs in path planning, including underwater navigation uncertainties, dynamic obstacles, and communication limitations. Additionally, the paper investigates various AI techniques, their applications, and their effectiveness in real-world scenarios. Through the development of AI-based systems, AUVs can achieve optimal path planning in highly dynamic and uncertain environments. We also present tables and figures that highlight AI model comparisons, performance metrics, and case studies that demonstrate the advancements in AUV path planning.

Keywords: Autonomous Underwater Vehicles, Path Planning, Artificial Intelligence, Reinforcement Learning, Deep Learning, Genetic Algorithms, Underwater Navigation

Author: Radhika Soni

Abstract: Human-robot interaction (HRI) has become a focal point in the evolution of collaborative workspaces, where both humans and robots are required to work alongside each other. This paper investigates the current trends in HRI within industrial and office environments, exploring the benefits, advancements, and challenges faced by the integration of robotic systems into collaborative settings. With a growing shift towards automation and enhanced human-robot synergy, this study evaluates the mechanisms of interaction, safety protocols, user acceptance, and the role of artificial intelligence in facilitating seamless collaboration. The paper also provides insights into the future prospects and innovations in the field, offering recommendations to optimize the effectiveness of human-robot collaborations.

Keywords: Human-Robot Interaction, Collaborative Workspaces, Automation, Robotics, Safety Protocols, Artificial Intelligence, User Acceptance, Industrial Robotics, Workplace Innovation, Robotics Challenges.

Authors: Shalini Verma, Lisha Menon

Abstract: Soft robotics, a rapidly evolving field, has emerged as a promising technological advancement in medical applications, especially in surgery and rehabilitation. This paper explores the innovations in soft robotics that have transformed surgical procedures and rehabilitation therapies, enhancing precision, flexibility, and safety in complex medical environments. Soft robotic systems, characterized by their flexible, adaptive, and lightweight nature, offer significant improvements over traditional rigid robotic tools. This paper discusses the principles behind soft robotics, recent innovations, key applications in minimally invasive surgeries, rehabilitation, and patient-centered care, and the challenges and future directions of this transformative technology. Additionally, we analyze the integration of artificial intelligence (AI) and machine learning with soft robotics to optimize outcomes and improve patient experiences.

Keywords: Soft Robotics, Medical Applications, Surgery, Rehabilitation, Minimally Invasive Surgery, Robotics, AI Integration, Adaptive Systems

Author: Manish Gupta

Abstract: The increasing demand for automation across industries has sparked a revolution in robotics. With this expansion, the need for energy-efficient systems has become paramount. Sustainable robotics focuses on designing energy-efficient autonomous systems that can operate for extended periods without depleting resources, thereby enhancing the performance and environmental sustainability of robotic applications. This paper explores various design approaches, technologies, and innovations in sustainable robotics, particularly focusing on energy efficiency in autonomous systems. It examines advancements in energy harvesting, battery technologies, power management, and the use of lightweight materials. Additionally, it discusses the challenges faced by engineers and researchers in developing energy-efficient autonomous systems and the future of sustainable robotics.

Keywords: Sustainable robotics, energy efficiency, autonomous systems, energy harvesting, power management, lightweight materials, battery technologies, robotics design, environmental sustainability, energy-efficient systems.

Author: Rajat Sharma

Abstract: This paper presents a reinforcement learning-based approach for autonomous exploration and mapping in unknown environments. Autonomous exploration is a fundamental capability required for robots operating in unstructured or unfamiliar surroundings. Our proposed approach leverages deep reinforcement learning techniques to enable robots to learn optimal exploration policies and construct accurate maps of their surroundings. We demonstrate the efficacy of our approach through extensive simulations and real-world experiments, showcasing its ability to autonomously explore and map unknown environments efficiently and accurately.

Keywords: Autonomous Exploration, Mapping, Reinforcement Learning, Unknown Environments, Robotics

Author: Rohit Gupta

Abstract: Autonomous Underwater Vehicles (AUVs) play a crucial role in oceanographic research, environmental monitoring, and underwater exploration. This paper delves into the latest advancements in navigation and communication technologies for AUVs, addressing the unique challenges posed by the underwater environment. We present a novel integrated navigation system that combines inertial navigation, acoustic positioning, and machine learning algorithms to achieve precise localization and path planning. Additionally, we explore advancements in underwater communication, focusing on acoustic communication techniques and data compression methods to enhance the reliability and speed of information transfer. Extensive field trials in various underwater environments demonstrate the effectiveness of our approach, highlighting significant improvements in navigation accuracy and communication efficiency. The findings underscore the potential for these advancements to enhance the capabilities and applications of AUVs in scientific and commercial domains.

Keywords:  Autonomous Underwater Vehicles, Inertial Navigation, Acoustic, Machine Learning, Underwater Exploration

Authors: Deepak Patel, Dr. Sneha Mehta

Abstract: Collaborative robotics, where multiple robots work together to accomplish tasks, has emerged as a powerful paradigm in the field of autonomous systems. This paper investigates the development and implementation of multi-agent systems (MAS) for enhancing the efficiency and effectiveness of collaborative robotics. By leveraging communication protocols and coordination strategies, our approach enables autonomous robots to share information, allocate tasks, and cooperate seamlessly in dynamic environments. The proposed system utilizes decentralized control and machine learning algorithms to adapt to changing conditions and optimize performance. Extensive experiments conducted in various industrial and service scenarios demonstrate the advantages of our MAS approach, including improved task completion times, reduced energy consumption, and increased operational reliability. The research highlights the potential of collaborative robotics to transform industries by enabling more flexible and efficient automation solutions.

Keywords: Collaborative Robotics, Multi-Agent Systems, Decentralized Control, Communication Protocols, Task Allocation

Author: Preeti Reddy

Abstract: Autonomous drones are increasingly being utilized in various applications, from delivery services to surveillance and environmental monitoring. A critical challenge in the deployment of autonomous drones is the development of efficient path planning algorithms that can navigate dynamic and unpredictable environments. This paper explores the use of machine learning techniques, specifically reinforcement learning and neural networks, to develop advanced path planning strategies for autonomous drones. Our proposed system dynamically adjusts to changes in the environment, learning optimal paths through continuous interaction and feedback. Experimental results, obtained from both simulated environments and real-world tests, indicate that our machine learning-based approach significantly outperforms traditional path planning methods in terms of efficiency, adaptability, and robustness. The findings suggest that machine learning has a transformative potential in enhancing the capabilities of autonomous drones.

Keywords: Path Planning, Autonomous Drones,Reinforcement Learning, Neural Networks, Dynamic Environments

Author: Dr. Rohit Bhargava

Abstract: In the rapidly evolving field of autonomous robotics, the integration of multiple sensor modalities has become a critical component for enhancing navigational capabilities and ensuring robust performance in diverse environments. This paper presents a comprehensive approach to sensor fusion, combining data from LiDAR, cameras, and inertial measurement units (IMUs) to create a cohesive and reliable perception system for autonomous navigation. By leveraging advanced algorithms such as Extended Kalman Filters (EKF) and Deep Learning techniques, our system achieves high accuracy in object detection, localization, and mapping. Extensive real-world testing in various scenarios, including urban settings and off-road environments, demonstrates the effectiveness of our integrated sensor fusion approach. The results show significant improvements in navigation accuracy and obstacle avoidance, highlighting the potential for broader applications in autonomous vehicles and robotics.

Keywords: Sensor Fusion, Autonomous Navigation, LiDAR, Deep Learning, Extended Kalman Filter

Author’s:Deepika Sharma, Neha Gupta

AbstractThis paper investigates the enhancement of decision-making capabilities in autonomous systems through the integration of Artificial Intelligence (AI) with robotics, focusing on advanced environmental adaptation. Autonomous systems, which are increasingly prevalent in various sectors, face significant challenges in dynamic and unpredictable environments. The core methodology involves a comparative analysis of AI algorithms in robotics, emphasizing their adaptability in changing conditions. Experimental simulations are conducted to evaluate the performance of AI-integrated robotic systems in various environmental scenarios. Results indicate a marked improvement in decision-making efficiency and adaptability to environmental changes when advanced AI algorithms are employed. This study contributes to the field by providing insights into the effective integration of AI in autonomous robotics, demonstrating enhanced environmental responsiveness and decision-making accuracy. The findings hold significant implications for the future development of autonomous systems, particularly in applications requiring high adaptability and precision.

Keywords- Autonomous Systems; Artificial Intelligence; Robotics; Environmental Adaptation; Decision-Making

Author: Gopal Reddy

AbstractAutonomous navigation in unstructured environments poses significant challenges for robotic systems. This paper provides an overview of various machine learning approaches employed to enhance autonomous navigation in unstructured environments. The focus is on methods that enable robots to adapt to dynamic and unpredictable surroundings. The paper discusses key algorithms, their advantages, and limitations, providing insights into the current state of the field.

Keywords- Autonomous Navigation, Machine Learning, Unstructured Environments, Deep Neural Networks, Reinforcement Learning, Simultaneous Localization and Mapping (SLAM), Evolutionary Algorithms, Sensor Fusion.

Author’s:Preeti Yadav, Devesh Parshonikar

Abstract: This paper delves into the ethical dimensions surrounding the development and deployment of autonomous robotic systems. As technology advances and these systems become more integrated into various aspects of society, it is imperative to critically assess the ethical challenges and dilemmas that may arise. The paper explores key considerations such as accountability, transparency, societal impact, and the potential consequences on employment.

Keywords- Autonomous Robotic Systems, Robotics Ethics, Accountability, Transparency, Explainable AI (XAI), Societal Impact, Employment Disruption, Human-Robot Collaboration, Machine Learning, Ethical Frameworks

Author’s: Naina Talwar, Hitesh Bisht

Abstract: This paper explores the integration of robots into industrial settings to enhance productivity and safety through human-robot collaboration (HRC). The increasing complexity of industrial tasks and the need for efficient production processes have led to the adoption of robotic systems working alongside human operators. We delve into the various aspects of HRC, including the benefits, challenges, and potential applications in diverse industrial scenarios. Additionally, we present case studies and use tables to illustrate the successful implementation of HRC, highlighting key performance indicators and outcomes.

Keywords- Human-Robot Collaboration, Industrial Automation, Collaborative Robotics, Robotics in Manufacturing, Human-Robot Interaction, Industrial Productivity, Safety in Robotics, Robotics Applications, Advanced Manufacturing.

Author’s: Dr. K. V. Trivedi, Sanjay Raval

AbstractHuman-Robot Interaction (HRI) has become an increasingly vital area of research as robots play a growing role in various aspects of our lives. The success of these interactions hinges on the establishment of trust and the optimization of user experience. This paper delves into the key factors influencing HRI, exploring strategies to enhance user experience and build trust between humans and robots.

Keywords-Human-Robot Interaction (HRI), User Experience, Trust, Robot Design, Communication Modalities, Context-Aware Interaction, Ease of Use, Efficiency, Satisfaction, Reliability, Transparency, Explainability, Artificial Intelligence, Robot Autonomy

Authors: Bhaskar Reddy

Abstract: With the rapid advancement of robotics and artificial intelligence (AI), the field of autonomous vehicles has witnessed significant growth in recent years. Intelligent control systems play a vital role in ensuring the safe and efficient operation of autonomous vehicles. This research paper provides a comprehensive review of robotics and AI techniques used in intelligent control systems for autonomous vehicles. The paper discusses various aspects of autonomous vehicle control, including perception, planning, decision-making, and control strategies. By analyzing research findings and case studies, this paper highlights the current state of the art, identifies challenges, and explores future directions in intelligent control systems for autonomous vehicles.

Keywords: Intelligent Control Systems, Autonomous Vehicles, Robotics, AI Techniques.

Authors: Deepika Soni, Brijesh Thakur

Abstract:The application of robotics and autonomous systems in healthcare has shown great promise in revolutionizing patient care, diagnostics, and medical procedures. This paper presents an overview of the current trends and future directions in the use of robotics and autonomous systems in healthcare, highlighting their potential to enhance efficiency, precision, and patient outcomes. The paper begins by examining the various applications of robotics in healthcare, including robotic-assisted surgery, rehabilitation robotics, and robotic prosthetics. It explores the advancements in robotic technologies, such as surgical robots with enhanced dexterity and haptic feedback, exoskeletons for rehabilitation, and prosthetic limbs with natural movement capabilities. These technologies enable surgeons to perform minimally invasive procedures with increased precision, assist in patient rehabilitation and therapy, and provide individuals with limb loss greater mobility and functionality. The paper discusses the role of autonomous systems in healthcare, such as telemedicine, remote patient monitoring, and medication management. It explores how autonomous robots can support healthcare professionals in remote consultations, collect patient data, and deliver medication to patients. The integration of artificial intelligence and machine learning algorithms enables autonomous systems to analyze medical data, provide accurate diagnoses, and assist in treatment planning. The paper also addresses the challenges and considerations associated with the implementation of robotics and autonomous systems in healthcare, including safety concerns, regulatory frameworks, ethical considerations, and the need for interdisciplinary collaboration between engineers, clinicians, and researchers. It discusses ongoing research and development efforts aimed at addressing these challenges and ensuring the safe and effective integration of robotics and autonomous systems into healthcare settings.

Keywords: Robotics, Autonomous Systems, Healthcare, Applications, Current Trends, Future Directions.

Authors: Paurush Gupta

Abstract:The use of robotics and autonomous systems in agriculture has gained significant attention in recent years due to their potential to enhance efficiency and sustainability in agricultural practices. This paper provides an overview of the various applications and benefits of robotics and autonomous systems in agriculture, highlighting their role in improving productivity, reducing labor costs, optimizing resource utilization, and minimizing environmental impact. The paper first examines the use of robots in agricultural tasks such as planting, harvesting, and crop monitoring. It explores the advancements in robotics technologies, including sensors, actuators, and machine learning algorithms, that enable robots to perform these tasks with precision and accuracy. The benefits of robotic automation include increased speed and accuracy, reduced dependency on human labor, and improved crop quality and yield. The paper discusses the application of autonomous systems, including drones and unmanned ground vehicles (UGVs), in agriculture. It explores how drones can be used for aerial surveillance, crop health assessment, and pesticide spraying, while UGVs can assist in tasks such as soil sampling, weed control, and data collection. The integration of autonomous systems with precision agriculture techniques enables farmers to make data-driven decisions and implement targeted interventions, leading to improved resource efficiency and reduced environmental impact. The paper also addresses the challenges and limitations associated with the adoption of robotics and autonomous systems in agriculture, including high initial costs, limited adaptability to diverse agricultural environments, and the need for specialized training and technical support. It discusses ongoing research and development efforts aimed at overcoming these challenges and making robotics and autonomous systems more accessible and user-friendly for farmers.

Keywords: Robotics, Autonomous Systems, Agriculture, Efficiency, Sustainability.

Authors: Amelia Lee, Oliver Brown

Abstract: The rapid advancement of artificial intelligence (AI) and autonomous systems has brought numerous benefits and transformative changes to various industries and sectors. However, this progress has also raised significant ethical concerns regarding the decision-making capabilities of these systems. In this paper, we delve into the ethical considerations associated with autonomous systems and explore the importance of responsible AI decision-making. We discuss the potential risks and challenges arising from the deployment of autonomous systems and present key principles and frameworks that can guide the development and implementation of ethical AI systems. By understanding and addressing these ethical considerations, we can ensure the responsible and beneficial integration of AI into our society.

Keywords: Autonomous systems, artificial intelligence, responsible AI, ethical considerations, transparency, fairness, bias, privacy, accountability, human-centric design, ethical guidelines, regulatory frameworks, ethical impact assessments.

Authors: Reshma Saksena, Arti Gupta

Abstract:Soft robotics is an emerging field that focuses on the design, modeling, and control of robots with compliant and flexible bodies. This paper provides an overview of recent advances in soft robotics, discussing key design principles, modeling techniques, and control strategies. The paper highlights the unique capabilities of soft robots, including their adaptability, safety, and dexterity, and explores their potential applications in various domains. Additionally, the paper presents relevant tables and figures to support the discussion and showcase the progress made in this exciting field.

Keywords: Soft robotics, design principles, modeling techniques, control strategies, compliance, deformability, bio-inspired robotics, human-robot interaction, healthcare applications.

Authors: Payal Rastogi, Aanchal Kashyap, Swati Purohit

Abstract: UAV is defined as Unmanned Aerial Vehicle with some greater or lesser degree of ‘automatic intelligence’ part of an unmanned aircraft systems. Although all UAV systems have many elements other than the air vehicles, UAVs are usually categorized by the capability or size of the air vehicle that is required to carry out the mission. However, it is possible that one system may employ more than one type of air vehicle to cover different types of missions, and that may pose a problem in its designation. However, these definitions are constantly being changed as technology advances, allowing a smaller system to take on the roles of the one above. The boundaries, therefore, are often blurred so that the following definitions can only be approximate and subject to change. The main challenge in design and making any system for integration into the main research system, such as robot or UAV, requires careful attention on the interaction of each electric and electrical unit or component on the circuit board. This paper highlights the fabrication and the evaluation of the Inshore Fishing UAV gripper, the acrylic board, and the wireless communication module. The communication transmission distance between the UAV on shore and UAV over the sea is about 1 kilometer with stable flight.

Keywords: Unmanned Aerial Vehicle; UAS; Gripper; Drone; Seashore; twe litle.

Authors: Dr. Balveer Dohare, Rahul Sahu, Hansraj Thakur

Abstract: The need for more apps to be employed inside and outside the car is growing as autonomous development technology develops. As a consequence of the literature research, several programmes have been created to show vehicle data directly on the monitor, with reflections on glass, and on hardware devices programmes have been created to show vehicle data directly on the monitor, with reflections on glass, and on hardware devices. These apps have only been created for a certain autonomous system and a defined issue. In this study, an Android-compatible mobile augmented reality application and a rudimentary autonomous car software infrastructure have been created. Both inside and outside of the car, the mobile augmented reality software is useful. Additionally, it has been demonstrated that this programme supports a number of autonomous system infrastructures.

Keywords: Augmented Reality, Deep Learning, Mobile Application, Autonomous Vehicle

Authors: Sakib Mujawar, Monika Ghadge, Sakshi Tonemare, Parth Mulye

Abstract: The work investigated and presented a virtual simulation system based on an entirely digital model of the moon, coupled with modules for kinematics and dynamics as well as a module for simulating autonomous navigation. The simulation models for the systems have been created. Investigated are enabling technologies, including autonomous navigation, kinematics and dynamics simulation, and a digital lunar surface module. Based on these technologies, a prototype system for simulating the movement of lunar rovers is created. Despite being infrequently used in virtual simulation systems, autonomous navigation is a crucial component of lunar rover systems. This prototype system includes an autonomous navigation simulation module, which was added to demonstrate that the system can effectively support research on the autonomous navigation of lunar rovers. The simulation results showed that the system's synthetic simulation and visualising analysis systems are established.

Keywords: Lunar rover, virtual simulation, autonomous navigation, full-digital lunar terrain.