Autonomous Safety Mechanism for Building: Fire Fighter Robot with Localized Fire Extinguisher

With advent of robotics technology in the modern days, the use of fire bucket to put up a small fire is seen as outdated. The autonomous approach (by using robots) in safe guarding the human environment building from potential hazard such as fire is deemed necessary. Big fire can be prevented by detecting small fire as quickly as possible. Fire detection device in a building needs to be reliable and effective. Autonomous system can be leveraged to accomplish the hazard detection without human supervision. Moreover, if the hazard eradication follows the detection in an autonomous mode, the solution has a profound impact to the safety of a building. A conceptual Fire Fighter Robot prototype is proposed in this paper. The dynamic model of the conceptual robot is derived. The control system based on nonlinear feedback control is designed to control the movement of the robot when dispersing the sand to put out the fire source. The designed autonomous system detects small flame in a confined area and put it out before it turns to big fire and spreads. A microcontroller, forming the brain of the robot, is coded with a supervisory control algorithm integrated with flame sensor modules are used for the detection of flame. The robot scans the room from front to back and vice versa. When flame is detected, the robot is deployed to directly above the flame source. Sand is used to extinguish the flame by targeting precise location of flame source. At the same time, the alarm will ring and send the message to the owner via Wi-Fi. The robot is capable of extinguishing fire from chemical substances and petrol. Sensitivity of flame sensors at different environment with different brightness is tested through analog reading of the serial monitor

Modelamiento, simulación y control de posicionamiento automático de un robot móvil con tracción diferencial como herramienta para apoyar la formación en robótica en ambientes de aprendizaje SENA

This article demonstrates a theoretical and practical solution for modelling, simulation and the didactic implementation of an automatic control system locating of a mobile robot to be used as a learning tool. The displacement of the robot is based on the differential traction configuration whose approximate mathematical model was simulated with the Matlab-Simulink tool as a theoretical support. The construction and design of the robot requires image processing to validate the position of the prototype in a cartesian plane in the classroom. The system is used as a strategy of knowledge transfer to support the program of technological training on “Diseño e Intergración de Automatismos Mecatrónicos del Centro la Industria, la Empresa y los Servicios - SENA Regional Huila”.El presente artículo evidencia una solución teórico-práctica para el modelamiento, simulación e implementación didáctica de un sistema de control automático de localización de un robot móvil para ser utilizado como herramienta de aprendizaje. El desplazamiento del robot está basado en la configuración de tracción diferencial cuyo modelo matemático aproximado fue simulado con la herramienta Matlab - Simulink como apoyo teórico. La construcción y ejecución del robot requiere de procesamiento de imágenes para validar la posición del prototipo en un plano cartesiano dentro del aula. El sistema es utilizado como estrategia de transferencia de conocimientos para apoyar el programa de Formación Tecnológica en Integración de Automatismos Mecatrónicos del Centro de la Industria, la Empresa y los Servicios del SENA Regional Huila

Patrol Mobile Robots and Chaotic Trajectories

This paper presents a study of special trajectories attainment for mobile robots based on the dynamical features of chaotic systems. This method of trajectories construction is envisaged for missions for terrain exploration, with the specific purpose of search or patrol, where fast scanning of the robot workspace is required. We propose the imparting of chaotic motion behavior to the mobile robot by means of a planner of goal positions sequence based on an area-preserving chaotic map. As a consequence, the robot trajectories seem highly opportunistic and unpredictable for external observers, and the trajectories's characteristics ensure the quick scanning of the patrolling space. The kinematic modeling and the closed-loop control of the robot are described. The results and discussion of numerical simulations close the paper

Multi-Objective Iterative Learning Control: An Advanced ILC Approach for Application Diversity.

While ILC has been applied to repetitive applications in manufacturing, chemical processing, and robotics, several key assumptions limit the extension of ILC to various applications. Conventional ILC focuses on improving the performance of a single metric, such as tracking performance through iterative updates of the time domain control input. The application range is limited to systems that satisfy the assumption of iteration invariance of the plant, reference signal, initial conditions, and disturbances. We aim to relax this assumption to gain significant advantages. More specifically we focus on relaxing the strict reference tracking requirement to address multiple performance metrics and define the stability bounds across temporal and spatial domains. The aim of this research is expanding the application space of ILC towards non-traditional applications. Chapter III presents an initial framework to provide the foundation for the multi-objective ILC. This framework is validated by simulation and experimental tests with a wheeled mobile robot. Chapter IV extends the initial framework from the temporal domain to the spatial domain. The initial framework is generalized to address four classifications of performance objectives. Stability and performance analysis for each classification is provided. Simulation results on a high-resolution additive manufacturing system validate the extended framework. For the generalized framework, we present a distributed approach in which additional objectives are considered separately. Chapter V evaluates the difference between this distributed approach, and a centralized approach in which the objectives are combined into a single matrix depending on the classification. Chapter VI extends the multi-objective ILC to incorporate a region-based tracking problem in which reference uncertainty is addressed through the development of a bounded region. A multi-objective region-to-region ILC is developed and validated by a simulation of a surveillance problem with an UAV and multiple unattended ground sensors. Comparisons with point-to-point ILC, region-to-region ILC, and multi-objective region-based ILC demonstrate the performance flexibility that can be achieved when leveraging the regions. This dissertation provides new approaches for relaxing the classical assumption of iteration invariant reference tracking. New stability and convergence analysis is provided, resulting in a design methodology for multi-objective ILC. These approaches are validated by simulation and experimental results.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120875/1/ingyulim_1.pd

Trajectory Planning for Autonomous Vehicles for Optimal Exploration of Spatial Processes

Autonomous vehicles are becoming the platform of choice for large-scale exploration of environmental processes, owing to their low cost and dependability of sensors. Standard trajectory planning methods often preplan a trajectory in advance, or are based on information criteria, and do not use the observations taken at earlier locations on the trajectory to decide where the vehicle should go next. In this dissertation, we propose a framework for real-time, adaptive generation of trajectories that are optimal with respect to some exploration goal, with a focus on smooth continuous trajectories for nonholonomic vehicles. We develop an algorithm that enables the vehicle to gather data, reconstruct the environmental process, and generate piecewise optimal trajectory segments that use the data collected at previously visited locations. Our approach is based on Gaussian process priors and Bayesian optimal design. Gaussian processes provide a method to fuse any prior knowledge of the environmental process with the collected data to obtain the most updated estimation of the process. Through Bayesian optimal design, we develop reward functions that explicitly reflect the operational goal and naturally address the exploration-exploitation trade-off in a principled way. We include a number of empirical evaluations of the methodology and show the advantages of our algorithm on different archetypes of spatial processes. Field tests of the planner on an autonomous ground vehicle demonstrate the practical usefulness of our algorithm. We then extend the framework to incorporate supplementary information from different types of off-vehicle sensors. Three incorporation methods, depending on the nature of the supplementary source, are developed and tested, resulting in performance improvement, especially in the early stages of exploration. Finally, we turn to the case of multiple vehicles and develop an extension of our optimal trajectory algorithm that uses collective information to estimate the process and individual trajectory planning for each vehicle. As a result, computation time per vehicle does not increase as more vehicles are added, while the time required to achieve the exploration goal is reduced nearly linearly

Mechatronic Systems

Mechatronics, the synergistic blend of mechanics, electronics, and computer science, has evolved over the past twenty five years, leading to a novel stage of engineering design. By integrating the best design practices with the most advanced technologies, mechatronics aims at realizing high-quality products, guaranteeing at the same time a substantial reduction of time and costs of manufacturing. Mechatronic systems are manifold and range from machine components, motion generators, and power producing machines to more complex devices, such as robotic systems and transportation vehicles. With its twenty chapters, which collect contributions from many researchers worldwide, this book provides an excellent survey of recent work in the field of mechatronics with applications in various fields, like robotics, medical and assistive technology, human-machine interaction, unmanned vehicles, manufacturing, and education. We would like to thank all the authors who have invested a great deal of time to write such interesting chapters, which we are sure will be valuable to the readers. Chapters 1 to 6 deal with applications of mechatronics for the development of robotic systems. Medical and assistive technologies and human-machine interaction systems are the topic of chapters 7 to 13.Chapters 14 and 15 concern mechatronic systems for autonomous vehicles. Chapters 16-19 deal with mechatronics in manufacturing contexts. Chapter 20 concludes the book, describing a method for the installation of mechatronics education in schools