A review: On path planning optimization criteria and mobile robot navigation

Mobile robots are growing more significant from time to time and have been applied to many fields such as agriculture, space, and even human life. It could improve mobile robot navigation efficiency, ensure path planning safety and smoothness, minimize time execution, etc. The main focus of mobile robots is to have the most optimal functions. An intelligent mobile robot is required to travel autonomously in various environments, static and dynamic. This paper article presents the optimization criteria for mobile robot path planning to figure out the most optimal mobile robot criteria to fulfill, including modeling analysis, path planning and implementation. Path length and path smoothness are the most parameters used in optimization in mobile robot path planning. Based on path planning, the mobile robot navigation is divided into three categories: global navigation, local navigation and personal navigation. Then, we review each category and finally summarize the categories in a map and discuss the future research strategies

A framework for roadmap-based navigation and sector-based localization of mobile robots

Personal robotics applications require autonomous mobile robot navigation methods that are safe, robust, and inexpensive. Two requirements for autonomous use of robots for such applications are an automatic motion planner to select paths and a robust way of ensuring that the robot can follow the selected path given the unavoidable odometer and control errors that must be dealt with for any inexpensive robot. Additional difficulties are faced when there is more than one robot involved. In this dissertation, we describe a new roadmapbased method for mobile robot navigation. It is suitable for partially known indoor environments and requires only inexpensive range sensors. The navigator selects paths from the roadmap and designates localization points on those paths. In particular, the navigator selects feasible paths that are sensitive to the needs of the application (e.g., no sharp turns) and of the localization algorithm (e.g., within sensing range of two features). We present a new sectorbased localizer that is robust in the presence of sensor limitations and unknown obstacles while still maintaining computational efficiency. We extend our approach to teams of robots focusing on quickly sensing ranges from all robots while avoiding sensor crosstalk, and reducing the pose uncertainties of all robots while using a minimal number of sensing rounds. We present experimental results for mobile robots and describe a webbased route planner for the Texas A&M campus that utilizes our navigator

A Novel Approach To Intelligent Navigation Of A Mobile Robot In A Dynamic And Cluttered Indoor Environment

The need and rationale for improved solutions to indoor robot navigation is increasingly driven by the influx of domestic and industrial mobile robots into the market. This research has developed and implemented a novel navigation technique for a mobile robot operating in a cluttered and dynamic indoor environment. It divides the indoor navigation problem into three distinct but interrelated parts, namely, localization, mapping and path planning. The localization part has been addressed using dead-reckoning (odometry). A least squares numerical approach has been used to calibrate the odometer parameters to minimize the effect of systematic errors on the performance, and an intermittent resetting technique, which employs RFID tags placed at known locations in the indoor environment in conjunction with door-markers, has been developed and implemented to mitigate the errors remaining after the calibration. A mapping technique that employs a laser measurement sensor as the main exteroceptive sensor has been developed and implemented for building a binary occupancy grid map of the environment. A-r-Star pathfinder, a new path planning algorithm that is capable of high performance both in cluttered and sparse environments, has been developed and implemented. Its properties, challenges, and solutions to those challenges have also been highlighted in this research. An incremental version of the A-r-Star has been developed to handle dynamic environments. Simulation experiments highlighting properties and performance of the individual components have been developed and executed using MATLAB. A prototype world has been built using the WebotsTM robotic prototyping and 3-D simulation software. An integrated version of the system comprising the localization, mapping and path planning techniques has been executed in this prototype workspace to produce validation results

Analysis of Human and Agent Characteristics on Human-Agent Team Performance and Trust

The human-agent team represents a new construct in how the United States Department of Defense is orchestrating mission planning and mission accomplishment. In order for mission planning and accomplishment to be successful, several requirements must be met: a firm understanding of human trust in automated agents, how human and automated agent characteristics influence human-agent team performance, and how humans behave. This thesis applies a combination of modeling techniques and human experimentation to understand the concepts aforementioned. The modeling techniques used include static modeling in SysML activity diagrams and dynamic modeling of both human and agent behavior in IMPRINT. Additionally, this research consisted of human experimentation in a dynamic, event-driven, teaming environment known as Space Navigator. Both the modeling and the experimenting depict that the agent\u27s reliability has a significant effect upon the human-agent team performance. Additionally, this research found that the age, gender, and education level of the human user has a relationship with the perceived trust the user has in the agent. Finally, it was found that patterns of compliant human behavior, archetypes, can be created to classify human users

The Rational Behavior Model: a multi-paradigm, tri-level software architecture for the control of autonomous vehicles

There is currently a very strong interest among researchers in the fields of artificial intelligence and robotics in finding more effective means of linking high level symbolic computations relating to mission planning and control for autonomous vehicles to low level vehicle control software. The diversity exhibited by the many processes involved in such control has resulted in a number of proposals for a general software architecture intended to provide an efficient yet flexible framework for the organization and interaction of relevant software components. The Rational Behavior Model (RBM) has been developed with these requirements in mind and consists of three levels, called the Strategic, the Tactical, and the Execution levels, respectively. Each level reflects computations supporting the solution to the global control problem based on different abstraction mechanisms. The unique contribution of the RBM architecture is the idea of specifying different programming paradigms to realize each software level. Specifically, RBM uses rule-based programming for the Strategic level, thereby permitting field reconfiguration of missions by a mission specialist without reprogramming at lower levels. The Tactical level realizes vehicle behaviors as the methods of software objects programmed in an object-based language such as Ada. These behaviors are initiated by rule satisfaction at the Strategic level, thereby rationalizing their interaction. The Execution level is programmed in any imperative language capable of supporting efficient execution of real-time control of the underlying vehicle hardware.http://archive.org/details/therationalbehav1094544438Major, United States ArmyApproved for public release; distribution is unlimited

Conference on Intelligent Robotics in Field, Factory, Service, and Space (CIRFFSS 1994), volume 1

The AIAA/NASA Conference on Intelligent Robotics in Field, Factory, Service, and Space (CIRFFSS '94) was originally proposed because of the strong belief that America's problems of global economic competitiveness and job creation and preservation can partly be solved by the use of intelligent robotics, which are also required for human space exploration missions. Individual sessions addressed nuclear industry, agile manufacturing, security/building monitoring, on-orbit applications, vision and sensing technologies, situated control and low-level control, robotic systems architecture, environmental restoration and waste management, robotic remanufacturing, and healthcare applications

Gestural Human-Machine-Interface (HMI) for an autonomous wheelchair for kids

El Trabajo de Fin de Master (TFM) se desarrolla a partir de una plataforma de ayuda a la movilidad destinada a niños. La arquitectura general de la plataforma se describe en anteriores trabajos. La plataforma consta de distintos nodos para suplir todas las funciones, alimentación, electrónica de potencia, control y navegación, interacción con el entorno e interfaz humano-maquina. Este TFM se centra en el nodo PC, el cual se basa en un ordenador con sistema operativo Linux y caracterizado por el uso de Robot Operating System (ROS). Sobre esta base se asienta la interfaz humano máquina gestual que se desarrolla en este trabajo. Este integra en el sistema existente una cámara RGBD Intel Realsense D435, ya que esta aplicación necesita tanto imagen RGB como imagen en profundidad. La información que proporciona la cámara se utiliza por medio de los paquetes que ofrece el fabricante de la cámara en ROS. Posteriormente se realiza la detección de personas. Para ello se utiliza una red neuronal entrenada para la detección de objetos basada en Tensorflow. A partir de los resultados de detección de la red, se obtiene la posición de las personas detectadas, transformando la posición en el plano de la persona su localización en el entorno virtual de la aplicación. Además se aplican técnicas de filtrado y tracking para mejorar esta localización. Por último, se implementa un sistema de reconocimiento de gestos, mediante el cual se pueda seleccionar fácilmente que usuario que desea interactuar con la plataforma y ejecutar una aplicación determinada. En el caso de este trabajo, la aplicacion elegida se basa en una estrategia denominada Follow Me, en la que la plataforma interactúe con el usuario y navegue por el entorno siguiéndole. La aplicación se incluye dentro del entorno de ROS, compatibilizando de esta forma su actuación con el resto de funciones de la plataforma.The Master's thesis is based on a mobility support platform for children. The general architecture of the platform is described in previous works. The platform consists of different nodes to provide all functions, power supply, power electronics, control and navigation, interaction with the environment and human-machine interface. This Master's thesis focuses on the PC node, which is based on a computer with a Linux operating system and characterised by the use of Robot Operating System (ROS). This is the basis for the gestural human-machine interface developed in this work. An Intel Realsense D435 RGBD camera is integrated into the existing system, as both RGB image and depth image are required for this application. The information provided by the camera is used by means of the packages offered by the camera manufacturer in ROS. Subsequently, the detection of persons is carried out. For this purpose, a neural network trained for object detection based on Tensorflow is used. From the detection results of the network, the position of the detected persons is obtained, transforming the position in the plane of the person to the location in the virtual environment of the application. In addition, f ltering and tracking techniques are applied to improve this localisation. Finally, a gesture recognition system is implemented, by means of which the user can easily select which user wants to interact with the platform and execute a given application. In the case of this work, the chosen application is based on a navigation strategy called Follow Me, in which the platform follows the user and navigates the environment in this way. The application is merged within the ROS environment, thus making it compatible with the rest of the platform's functions.Máster Universitario en Ingeniería Industrial (M141

Autonomous mobile robot navigation using fuzzy logic control

Traditionally the type of robot used in the workplace consisted mainly o f the fixed arm variety. Any mobile robots that were commercially available required that the environment be altered to accommodate them. This involved the installation of guide lanes or some form of sensor units placed at various locations around the workplace to facilitate the robot in determining its position within the environment. Such approaches are costly and limit the use of robots to environments where these methods are feasible. The inadequacies in this technology has led to research into autonomous mobile robots that offer greater flexibility and do not require changes in the enviromnent. There are many technical issues to be addressed in designing such a robot. These stem from the necessity that the robot must be able to navigate through an environment unaided. Other problems such as the cost of the vehicle must be considered so that prospective customers will not be put off. This thesis discusses the strategies taken in addressing the problems associated with navigation in an obstacle strewn environment. Such issues include position estimation, path planning, obstacle avoidance and the acquisition and interpretation of sensor information. It also discusses the suitability of fuzzy logic for controlling a robot. A graphical user interface runs on the PC which communicates with the robot over a radio link. The robot uses a fuzzy logic controller to follow a planned path and avoid unknown obstacles by controlling the velocity and steering angle o f the drive unit. It is a tracked vehicle which is suitable for indoor use only. The results of path planning and the robots attempts at following the paths and avoiding obstacles are illustrated and discussed

Mobile robots and vehicles motion systems: a unifying framework

Robots perform many different activities in order to accomplish their tasks. The robot motion capability is one of the most important ones for an autonomous be- havior in a typical indoor-outdoor mission (without it other tasks can not be done), since it drastically determines the global success of a robotic mission. In this thesis, we focus on the main methods for mobile robot and vehicle motion systems and we build a common framework, where similar components can be interchanged or even used together in order to increase the whole system performance