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2nd International Conference on Advanced Robotics, Mechatronics and Artificial Intelligence , will be organized around the theme “Boundless Implications of Automation and Control Systems in Mechatronics”

Advanced Robotics 2018 is comprised of keynote and speakers sessions on latest cutting edge research designed to offer comprehensive global discussions that address current issues in Advanced Robotics 2018

Submit your abstract to any of the mentioned tracks.

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Mechatronics is a multidisciplinary field of science. Mechatronics includes the combination of mechanics and electronics. As technical systems have become more and more complex the definition has been broadened to include more technical areas like telecommunications engineering, computer engineering, control engineering and systems engineering. Examples of Mechatronics System are robots, aircraft flight control, automated guided vehicles, navigation systems, digitally controlled combustion engines and self-adaptive tools etc.  The sensors in these systems receives signals from the surroundings, react to these signals using appropriate processing to generate acquired output signals. Mechatronics can be referred as Electromechanical systems.

  • Track 1-1Mechatronics Basics
  • Track 1-2Nano/Micro-Systems
  • Track 1-3Sensors and Signal Processing
  • Track 1-4Visual Sensing and Image Processing
  • Track 1-5Actuators and Motion Control
  • Track 1-6Modeling and Control
  • Track 1-7Simulations and Simulation Software
  • Track 1-8Transportation Systems

Deep learning allows the computer to build complex concepts out of simpler concepts. Deep learning system can represent the concept of an image of a person by combining simpler concepts, such as corners and contours, which are in turn defined in terms of edges. The idea of learning the right representation for the data provides one perspective on deep learning. Another perspective on deep learning is that depth allows the computer to learn a multi-step computer program. Each layer of the representation can be thought of as the state of the computer’s memory after executing another set of instructions in parallel. Networks with greater depth can execute more instructions in sequence. Deep learning is a specific kind of machine learning.

  • Track 2-1Ambient Intelligence
  • Track 2-2Artificial Intelligence
  • Track 2-3Brain Modeling and Simulation
  • Track 2-4Computational Intelligence
  • Track 2-5Deep Learning
  • Track 2-6Neural Networks and Neuro-Fuzzy Systems
  • Track 2-7Intelligent Control
  • Track 2-8Intelligent Medical Diagnostics
  • Track 2-9Intelligent Networks
  • Track 2-10Probabilistic Reasoning
  • Track 2-11Swarm Intelligence

Genetic Algorithms are heuristic search approaches that are applicable to a wide range of optimization problems. This flexibility makes them attractive for many optimization problems in practice. Evolution is the basis of Genetic Algorithms. It follows 3 rules and they are Selection rule, Cross over rule and Mutation Rule. Genetic operators change the solutions. Crossover operators combine the genomes of two or more solutions. Mutation adds randomness to solutions and should be scalable, drift-less, and reach each location in solution space. Genetic Algorithms are search based algorithms based on the concepts of natural selection and genetics. Genetic Algorithms are a subset of a much larger branch of computation known as Evolutionary Computation. Genetic algorithms optimize a given function by means of a random search. They are best suited for optimization and tuning problems in the cases where no prior information is available. As an optimization method genetic algorithm are much more effective than a random search. Genetic Algorithms are adaptive heuristic search algorithms that belong to the larger part of evolutionary algorithms. Genetic Algorithms have demonstrated to be effective procedures for solving multicriterial optimization problems. It is a very popular meta-heuristic technique for solving optimization problems. These algorithms mimic models of natural evolution and can adaptively search large spaces in near-optimal ways. They are commonly used to generate high-quality solutions for optimisation problems and search problems.

  • Track 3-1Genetic Algorithms
  • Track 3-2Fuzzy Control
  • Track 3-3Decision Support Systems
  • Track 3-4Machine Learning in Control Applications
  • Track 3-5Knowledge-Based Systems Applications
  • Track 3-6Hybrid Learning Systems
  • Track 3-7Distributed Control Systems
  • Track 3-8Evolutionary Computation and Control
  • Track 3-9Optimization Algorithms
  • Track 3-10Soft Computing
  • Track 3-11Software Agents for Intelligent Control Systems
  • Track 3-12Neural Networks based Control Systems
  • Track 3-13Planning and Scheduling
  • Track 3-14Intelligent Fault Detection and Diagnosis
  • Track 3-15Engineering Applications

The concept of biomimetic control, i.e., control systems that mimic biological animals in the way they exercise control, rather than just humans, has led to the definition of a new class of biologically inspired robots that exhibit much greater robustness in performance in unstructured environments than the robots that are currently being built. A key feature of biomimetic robots is their capacity to adapt to the environment and ability to learn and react fast. However, a biomimetic robot is not just about learning and adaptation but also involves novel mechanisms and manipulator structures capable of meeting the enhanced performance requirements. Thus, biomimetic robots are being designed to be substantially more compliant and stable than conventionally controlled robots and will take advantage of new developments in materials, microsystems technology, as well as developments that have led to a deeper understanding of biological behaviour. 

  • Track 4-1Robot Control
  • Track 4-2Mobile Robotics
  • Track 4-3Micro and Nano Robots
  • Track 4-4Rescue and Field Robotics
  • Track 4-5Medical Robots and Bio-robotics
  • Track 4-6Space and Underwater Robots
  • Track 4-7SLAM (Simultaneous Localization and Mapping)
  • Track 4-8Assistive Robotics
  • Track 4-9Autonomous Robots
  • Track 4-10Bio-inspired Robotics
  • Track 4-11Biomechanics
  • Track 4-12Biomedical Robots
  • Track 4-13Biomimetic Robotics
  • Track 4-14Humanoid Robots
  • Track 4-15Multi-Robots

Usually, the procedure of the planning and development of a process of an assembly, inspection and measurement equipment using machine vision is split into precise determination of tasks and goals like detection, recognition, grasping, handling, measurement, fault detection, etc. and into machine vision component selection and working conditions determination like camera, computer, lenses and optics, illumination, position determination, etc. 

  • Track 5-1Robotics Ethics and Policy
  • Track 5-2Social Robotics and Safety
  • Track 5-3Sensors for Robot Safety
  • Track 5-4Intelligent Autonomous Robots and Safety
  • Track 5-5Wearable Robots and Safety
  • Track 5-6Rehabilitation System, Transfer Machine and Safety
  • Track 5-7Interaction Control of Assistive Robots and Safety
  • Track 5-8Human-in-Loop and Safety
  • Track 5-9Multi-Agent Coordination for Human
  • Track 5-10Human-Robot Interaction and Interfaces
  • Track 5-11Machine Vision for Robot Safety

A design-centric contribution to human-robot interaction. This includes the design of new robot morphologies and appearances, behaviour paradigms, interaction techniques and scenarios, and telepresence interfaces. The design research should support unique or improved interaction experiences or abilities for robots. The understanding and study of fundamental HRI principles that span beyond individual interfaces or projects. This includes detailing underlying interaction paradigms, theoretical concepts, new interpretations of known results, or new evaluation methodologies.

  • Track 6-1Technical Advances in Human-Robot Interaction
  • Track 6-2Theory and Methods in Human-Robot Interaction
  • Track 6-3Human-Robot Interaction Design

A Programmable Logic Controller (or PLC) is a specialised digital controller that can control machines and processes. it monitors inputs, makes decisions, and controls outputs in order to automate machines and processes. A building automation system is a system that controls and monitors building services. These systems can be built up in several different ways. In this chapter a general building automation system for a building with complex requirements due to the activity, such as a hospital, will be described. Real systems usually have several of the features and components described here but not all of them. The Automation level includes all the advanced controllers that controls and regulates the Field level devices in real time. 

  • Track 7-1Electronics Automation and Electrical Engineering
  • Track 7-2Automation Instrument and Device
  • Track 7-3Plc and Micro-Controllers
  • Track 7-4Automation in Chemical Engineering
  • Track 7-5Cloud Computing for Automation
  • Track 7-6Building Automation
SCADA systems are a type of Industrial Control System. They are used to gather information and exercise control from remote locations. In situations where integrated data procurement is as significant as control, SCADA systems are employed to monitor remote units. These systems find applications in distribution processes such as water supply and wastewater collection systems, oil and gas pipelines, electrical utility transmission, and rail and other public transportation systems. SCADA systems perform consolidated control for various process inputs and outputs by integrating Human Machine Interface (HMI) software and data transmission systems with data acquisition systems. The transfer of data between operator terminals, such as Remote Terminal Units (RTUs) and Programmable Logic Controllers (PLCs), and the central host computer is included in SCADA systems. A SCADA system collects relevant data, transfers the data back to a central site, then notifies the home station about the event, implementing the required analysis and control, and then displays the data in a logical and systematic manner using graphs or text, thus enabling the operator to control a whole process in real time.
  • Track 8-1ANN - Artificial neural network
  • Track 8-2DCS - Distributed Control System
  • Track 8-3HMI - Human Machine Interface
  • Track 8-4SCADA - Supervisory Control and Data Acquisition
  • Track 8-5PLC - Programmable Logic Controller
  • Track 8-6Instrumentation
  • Track 8-7Motion Control
  • Track 8-8Robotics

Augmented Reality (AR) is a general-purpose term used for any view of reality where elements of that view are augmented with virtual imagery. Augmented Reality (AR) is a technology where the reality is augmented, enhanced with different types of virtual information. This information can be e.g. 3D models, text and images. With AR the user sees this information as an overlay on top of the real world. Unlike virtual reality where the user it totally immersed in the virtual world and cannot see anything but the virtual environment. To be able to place the overlay in the correct position the AR software can use different types of techniques. Some of these techniques are marker tracking, image recognition and the use of embedded sensors. Augmented reality (AR) creates an environment where computer generated information is superimposed onto the user’s view of a real-world scene.

  • Track 9-1Vision, Recognition and Reconstruction
  • Track 9-2Robot Design, Development and Control
  • Track 9-3Tele-robotics and Tele-operation
  • Track 9-4Industrial Networks and Automation
  • Track 9-5Modelling, Simulation and Architecture
  • Track 9-6Augmented Reality
  • Track 9-7Perception and Awareness
  • Track 9-8Surveillance, Fault detection and Diagnosis
  • Track 9-9Haptics
  • Track 9-10Modelling, Identification and Control
  • Track 9-11Signal and Image Processing

Vehicle control can be defined as the set of tasks involved in navigating, guiding, and manoeuvring a vehicle2 via control of the electrical, mechanical and other devices provided on the vehicle for these purposes. Vehicle control can in its broadest sense be either entirely manual, entirely automated, or on a point somewhere along the continuum between these two extremes. The application of telematics to Vehicle Control involves the development and deployment of Autonomous and Infrastructure linked systems whose aim is to assist drivers in controlling their vehicle. ‘Automated Highway Systems’ or ‘Automated Vehicle Guidance’ is where vehicle guidance and control inputs are derived from on-board sensors, which can be supplemented by equipment residing outside the vehicle. Vehicle control is affected without intervention by the driver although driver override is still possible.

  • Track 10-1Control and Supervision Systems
  • Track 10-2Intelligent Transportation Technologies and Systems
  • Track 10-3Engineering Applications
  • Track 10-4Industrial Automation and Robotics
  • Track 10-5Vehicle Control Applications

When a traditional feedback control system is closed via a communication channel, which may be shared with other nodes outside the control system, then the control system is called a Networked control system. An NCS can also be defined as a feedback control system wherein the control loops are closed through a real-time network. The defining feature of an NCS is that information (reference input, plant output, control input, etc.) is exchanged using a network among control system components (sensors, controllers, actuators, etc.,). Network controllers allow data to be shared efficiently. It is easy to fuse the global information to take intelligent decisions over a large physical space. They eliminate unnecessary wiring. It is easy to add more sensors, actuators and controllers with very little cost and without heavy structural changes to the whole system. Most importantly, they connect cyber space to physical space making task execution from a distance easily accessible.

  • Track 11-1Marine and Aerospace Guidance and Control
  • Track 11-2Space Control Systems
  • Track 11-3Linear and Nonlinear Systems Control
  • Track 11-4Fractional Order Systems
  • Track 11-5Chaotic Systems
  • Track 11-6Complex Systems
  • Track 11-7Automatic Control and Technology
  • Track 11-8Networked Control Systems
  • Track 11-9Signal Processing Systems for Control
  • Track 11-10Hybrid Systems and Control

In open loop control, it is assumed that the dynamical model of the system is well known, that there is little or no environmental noise and that the control signal can be applied with high precision. This approach is generally utilized when there is a target value, to achieve at a particular final time, T. The disadvantage of open-loop control is that the performance of the controller is highly susceptible to any unanticipated disturbances. In feedback control, continuous or discrete time measurements of the system output, y(t), are used to adjust the control signal in real time. At each instant, the observed process, y is compared to a tracking reference, r(t), and used to generate an error signal. Feedback therefore provides the backbone of most modern control applications. In learning control, a measurement of the system, y(t), is also used to design the optimal feedback signal; however, it is not done in real time. Instead, a large number of trial control signals are tested in advance, and the one that performs best is selected to be u ◦ (t).

  • Track 12-1Dynamic Programming in Continuous Time
  • Track 12-2Kalman Filter and Certainty Equivalence
  • Track 12-3Observability
  • Track 12-4Controllability
  • Track 12-5Continuous-Time Markov Decision Processes
  • Track 12-6Programming Average-Cost
  • Track 12-7Optimal Stopping Problems
  • Track 12-8Dynamic Programming over the Infinite Horizon
  • Track 12-9Markov Decision Problems
  • Track 12-10Dynamic Programming
  • Track 12-11Optimization Problems in Control Engineering
  • Track 12-12Automotive Control Systems and Autonomous Vehicles
  • Track 12-13Process Control and Automatic Control Theory
  • Track 12-14Control System Modeling
  • Track 12-15Control Theory and Application
  • Track 12-16Control Theory and Methodologies

Autonomous systems have the capability to independently (and successfully) perform complex tasks. Consumer and governmental demands for such systems are frequently forcing engineers to push many functions normally performed by humans into machines. s a functional architecture for an intelligent autonomous controller with an interface to the process involving sensing (e.g., via conventional sensing technology, vision, touch, smell, etc.), actuation (e.g., via hydraulics, robotics, motors, etc.), and an interface to humans (e.g., a driver, pilot, crew, etc.) and other systems. The “execution level” has low-level numeric signal processing and control algorithms (e.g., PID, optimal, adaptive, or intelligent control; parameter estimators, failure detection and identification (FDI) algorithms). The “coordination level” provides for tuning, scheduling, supervision, and redesign of the execution-level algorithms, crisis management, planning and learning capabilities for the coordination of execution-level tasks, and higher-level symbolic decision making for FDI and control algorithm management. The “management level” provides for the supervision of lower-level functions and for managing the interface to the human(s) and other systems.

  • Track 13-1Adaptive Control
  • Track 13-2Robust Control
  • Track 13-3Optimal Control
  • Track 13-4Process Control
  • Track 13-5Stochastic Systems Control and Remote Supervisory Control
  • Track 13-6Manufacturing Systems Control
  • Track 13-7Co-Operative Control
  • Track 13-8Predictive, Intelligent and Servo Control
  • Track 13-9Cooperative, Coordinated and Decentralized Control
  • Track 13-10Advanced Process Control