Contributed papers presented at the conference held at Central Mechanical Engineering Research Institute, Durgapur.

This book comprises the proceedings of the 12th National Technical Symposium on Unmanned System Technology 2020 (NUSYS’20) held on October 27–28, 2020. It covers a number of topics, including intelligent robotics, novel sensor technology, control algorithms, acoustics signal processing, imaging techniques, biomimetic robots, green energy sources, and underwater communication backbones and protocols, and it appeals to researchers developing marine technology solutions and policy-makers interested in technologies to facilitate the exploration of coastal and oceanic regions.

Localization and mapping are the essence of successful navigation in mobile platform technology. Localization is a fundamental task in order to achieve high levels of autonomy in robot navigation and robustness in vehicle positioning. Robot localization and mapping is commonly related to cartography, combining science, technique and computation to build a trajectory map that reality can be modelled in ways that communicate spatial information effectively. This book describes comprehensive introduction, theories and applications related to localization, positioning and map building in mobile robot and autonomous vehicle platforms. It is organized in twenty seven chapters. Each chapter is rich with different degrees of details and approaches, supported by unique and actual resources that make it possible for readers to explore and learn the up to date knowledge in robot navigation technology. Understanding the theory and principles described in this book requires a multidisciplinary background of robotics, nonlinear system, sensor network, network engineering, computer science, physics, etc.

Of the 300 papers presented during IROS '94, 48 were selected because they are particularly significant and characteristic for the present state of the technology of intelligent robots and systems. This book contains the selected papers in a revised and expanded form.Robotics and intelligent systems constitute a very wide and truly interdisciplinary field. The papers have been grouped into the following categories:– Sensing and Perception – Learning and Planning– Manipulation– Telerobotics and Space Robotics– Multiple Robots– Legged Locomotion– Mobile Robot Systems– Robotics in MedicineOther additional fields covered include; control, navigation and simulation. Since many researchers in robotics are now apparently interested in some combination of learning, mobile robots and robot vision, most of the articles included relate to at least one of these fields.

The oceans are a hostile environment, and gathering information on deep-sea life and the seabed is incredibly difficult. Autonomous underwater vehicles are robot submarines that are revolutionizing the way in which researchers and industry obtain data. Advances in technology have resulted in capable vehicles that have made new discoveries on how th

We are delighted to present the proceedings of the 5th International Conference on Advances in Additive Manufacturing Technologies (ICAAMT 2023). This conference serves as a premier forum for researchers, practitioners, and industry experts to share their latest findings, innovations, and insights in the field of additive manufacturing. The rapid advancements and the increasing adoption of these technologies across various sectors underscore the importance of this gathering. The conference was held from November 27-29, 2023, in Chennai, India and organized by the Department of Mechanical Engineering, Chennai Institute of Technology, Chennai, India.

This volume contains the proceedings of the RAAD 2018 conference, covering major areas of research and development in robotics. It provides an overview on the advances in robotics, more specifically in novel design and applications of robotic systems; dexterous grasping, handling and intelligent manipulation; intelligent cooperating and service robots; advanced robot control; human-robot interfaces; robot vision systems and visual serving techniques; mobile robots; humanoid and walking robots; field and agricultural robotics; bio-inspired and swarm robotic systems; developments towards micro and nano-scale robots; aerial, underwater and spatial robots; robot integration in holonic manufacturing; personal robots for ambient assisted living; medical robots and bionic prostheses; intelligent information technologies for cognitive robots etc. The primary audience of the work are researchers as well as engineers in robotics and mechatronics.

This book gathers selected high-impact articles from the 1st International Conference on Data Science, Machine Learning & Applications 2019. It highlights the latest developments in the areas of Artificial Intelligence, Machine Learning, Soft Computing, Human–Computer Interaction and various data science & machine learning applications. It brings together scientists and researchers from different universities and industries around the world to showcase a broad range of perspectives, practices and technical expertise.

This thesis is concerned with the theoretical and practical development of reliable and robust localisation algorithms for autonomous land vehicles operating at high speeds in unstructured, expansive and harsh environments. Localisation is the ability of a vehicle to determine its position and orientation within an operating environment. The need for such a localisation system is motivated by the requirement of developing autonomous vehicles in applications such as mining, agriculture and cargo handling. The main drivers in these applications are for safety, efficiency and productivity. The approach taken to the localisation problem in this thesis guarantees that the safety and reliability requirements imposed by such applications are achieved. The approach also aims to minimise the engineering or modification of the environment, such as adding artificial landmarks or other infrastructure. This is a key driver in the practical implementation of a localisation algorithm. In pursuit of these objectives, this thesis makes the following principal contributions: 1. The development of an Iterative Closest Point - Extended Kalman Filter (ICP-EKF) algorithm - a map-based iconic algorithm that utilises measurements from a scanning laser rangefinder to achieve localisation. The ICP-EKF algorithm entails the development of a map-building algorithm. The main attraction of the map-based localisation algorithm is that it works directly on sensed data and thus does not require extraction and matching of features. It also explicitly takes into account the uncertainty associated with measurements and has the ability to include measurements from a variety of different sensors. 2. The development and implementation of an entropy-based metric to evaluate the information content of measurements. This metric facilitates the augmentation of landmarks to the ICP-EKF algorithm thus guaranteeing reliable and robust localisation. 3. The development and adaptation of a view-invariant Curvature Scale Space (CSS) landmark extraction algorithm. The algorithm is sufficiently robust to sensor noise and is capable of reliably detecting and extracting landmarks that are naturally present in the environment from laser rangefinder scans. 4. The integration of the information metric and the CSS and ICP-EKF algorithms to arrive at a unified localisation framework that uses measurements from both artificial and natural landmarks, combined with dead-reckoning sensors, to deliver reliable vehicle position estimates. The localisation framework developed is sufficiently generic to be used on a variety of other autonomous land vehicle systems. This is demonstrated by its implementation using field data collected from three different trials on three different vehicles. The first trial was carried out on a four-wheel drive vehicle in an underground mine tunnel. The second trial was conducted on a Load-Haul-Dump (LHD) truck in a test tunnel constructed to emulate an underground mine. The estimates of the proposed localisation algorithms are compared to the ground truth provided by an artificial landmark-based localisation algorithm that uses bearing measurements from a laser. To demonstrate the feasibility and reliability of both the natural landmark extraction and localisation algorithms, these are also implemented on a utility vehicle in an outdoor area within the University's campus. The results demonstrate the robustness of the proposed localisation algorithms in producing reliable and accurate position estimates for autonomous vehicles operating in a variety of unstructured domains.