Intelligent strategies for mobile robotics in laboratory automation

In this thesis a new intelligent framework is presented for the mobile robots in laboratory automation, which includes: a new multi-floor indoor navigation method is presented and an intelligent multi-floor path planning is proposed; a new signal filtering method is presented for the robots to forecast their indoor coordinates; a new human feature based strategy is proposed for the robot-human smart collision avoidance; a new robot power forecasting method is proposed to decide a distributed transportation task; a new blind approach is presented for the arm manipulations for the robots

Study on the development of an autonomous mobile robot

Dissertação para obtenção do Grau de Mestre em Engenharia Eletrotécnica e de ComputadoresThis dissertation addresses the subject of Autonomous Mobile Robotics (AMR). It is aimed to evaluate the problems associated with the orientation of the independent vehicles and their technical solutions. There are numerous topics related to the AMR subject. Due to the vast number of topics important for the development of an AMR, it was necessary to dedicate different degrees of attention to each of the topics. The sensors applied in this research were several, e.g. Ultrasonic Sensor, Inertial Sensor, etc. All of them have been studied within the same environment. Employing the information provided by the sensors, a map is constructed, and based on this map a trajectory is planned. The Robot moves, considering the planned trajectory, commanded by a controller based on Linear Quadratic Regulator (LQR) and a specially made model of the robot, through a Kalman Filter (KF). Some of the researched topics were implemented in a real robot in an unstructured environment, collecting measurement data. A final conclusion is indicating the future direction of development

Cooperative simultaneous localization and mapping framework

This research work is a contribution to develop a framework for cooperative simultaneous localization and mapping with multiple heterogeneous mobile robots. The presented research work contributes in two aspects of a team of heterogeneous mobile robots for cooperative map building. First it provides a mathematical framework for cooperative localization and geometric features based map building. Secondly it proposes a software framework for controlling, configuring and managing a team of heterogeneous mobile robots. Since mapping and pose estimation are very closely related to each other, therefore, two novel sensor data fusion techniques are also presented, furthermore, various state of the art localization and mapping techniques and mobile robot software frameworks are discussed for an overview of the current development in this research area. The mathematical cooperative SLAM formulation probabilistically solves the problem of estimating the robots state and the environment features using Kalman filter. The software framework is an effort toward the ongoing standardization process of the cooperative mobile robotics systems. To enhance the efficiency of a cooperative mobile robot system the proposed software framework addresses various issues such as different communication protocol structure for mobile robots, different sets of sensors for mobile robots, sensor data organization from different robots, monitoring and controlling robots from a single interface. The present work can be applied to number of applications in various domains where a priori map of the environment is not available and it is not possible to use global positioning devices to find the accurate position of the mobile robot. Therefore the mobile robot(s) has to rely on building the map of its environment and using the same map to find its position and orientation relative to the environment. The exemplary areas for applying the proposed SLAM technique are Indoor environments such as warehouse management, factory floors for parts assembly line, mapping abandoned tunnels, disaster struck environment which are missing maps, under see pipeline inspection, ocean surveying, military applications, planet exploration and many others. These applications are some of many and are only limited by the imagination.Diese Forschungsarbeit ist ein Beitrag zur Entwicklung eines Framework für kooperatives SLAM mit heterogenen, mobilen Robotern. Die präsentierte Forschungsarbeit trägt in zwei Aspekten in einem Team von heterogenen, mobilen Robotern bei. Erstens stellt es einen mathematischen Framework für kooperative Lokalisierung und geometrisch basierende Kartengenerierung bereit. Zweitens schlägt es einen Softwareframework zur Steuerung, Konfiguration und Management einer Gruppe von heterogenen mobilen Robotern vor. Da Kartenerstellung und Poseschätzung miteinander stark verbunden sind, werden zwei neuartige Techniken zur Sensordatenfusion präsentiert. Weiterhin werden zum Stand der Technik verschiedene Techniken zur Lokalisierung und Kartengenerierung sowie Softwareframeworks für die mobile Robotik diskutiert um einen Überblick über die aktuelle Entwicklung in diesem Forschungsbereich zu geben. Die mathematische Formulierung des SLAM Problems löst das Problem der Roboterzustandsschätzung und der Umgebungmerkmale durch Benutzung eines Kalman filters. Der Softwareframework ist ein Beitrag zum anhaltenden Standardisierungsprozess von kooperativen, mobilen Robotern. Um die Effektivität eines kooperativen mobilen Robotersystems zu verbessern enthält der vorgeschlagene Softwareframework die Möglichkeit die Kommunikationsprotokolle flexibel zu ändern, mit verschiedenen Sensoren zu arbeiten sowie die Möglichkeit die Sensordaten verschieden zu organisieren und verschiedene Roboter von einem Interface aus zu steuern. Die präsentierte Arbeit kann in einer Vielzahl von Applikationen in verschiedenen Domänen benutzt werden, wo eine Karte der Umgebung nicht vorhanden ist und es nicht möglich ist GPS Daten zur präzisen Lokalisierung eines mobilen Roboters zu nutzen. Daher müssen die mobilen Roboter sich auf die selbsterstellte Karte verlassen und die selbe Karte zur Bestimmung von Position und Orientierung relativ zur Umgebung verwenden. Die exemplarischen Anwendungen der vorgeschlagenen SLAM Technik sind Innenraumumgebungen wie Lagermanagement, Fabrikgebäude mit Produktionsstätten, verlassene Tunnel, Katastrophengebiete ohne aktuelle Karte, Inspektion von Unterseepipelines, Ozeanvermessung, Militäranwendungen, Planetenerforschung und viele andere. Diese Anwendungen sind einige von vielen und sind nur durch die Vorstellungskraft limitiert

A Comprehensive Review on Autonomous Navigation

The field of autonomous mobile robots has undergone dramatic advancements over the past decades. Despite achieving important milestones, several challenges are yet to be addressed. Aggregating the achievements of the robotic community as survey papers is vital to keep the track of current state-of-the-art and the challenges that must be tackled in the future. This paper tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, path planning and following, sensor fusion methods, obstacle avoidance, and SLAM. The urge to present a survey paper is twofold. First, autonomous navigation field evolves fast so writing survey papers regularly is crucial to keep the research community well-aware of the current status of this field. Second, deep learning methods have revolutionized many fields including autonomous navigation. Therefore, it is necessary to give an appropriate treatment of the role of deep learning in autonomous navigation as well which is covered in this paper. Future works and research gaps will also be discussed

A stacked LSTM based approach for reducing semantic pose estimation error

© 1963-2012 IEEE. Achieving high estimation accuracy is significant for semantic simultaneous localization and mapping (SLAM) tasks. Yet, the estimation process is vulnerable to several sources of error, including limitations of the instruments used to perceive the environment, shortcomings of the employed algorithm, environmental conditions, or other unpredictable noise. In this article, a novel stacked long short-term memory (LSTM)-based error reduction approach is developed to enhance the accuracy of semantic SLAM in presence of such error sources. Training and testing data sets were constructed through simulated and real-time experiments. The effectiveness of the proposed approach was demonstrated by its ability to capture and reduce semantic SLAM estimation errors in training and testing data sets. Quantitative performance measurement was carried out using the absolute trajectory error (ATE) metric. The proposed approach was compared with vanilla and bidirectional LSTM networks, shallow and deep neural networks, and support vector machines. The proposed approach outperforms all other structures and was able to significantly improve the accuracy of semantic SLAM. To further verify the applicability of the proposed approach, it was tested on real-time sequences from the TUM RGB-D data set, where it was able to improve the estimated trajectories

Experimental Performance of Mobile Robotic System by Involving IoT Technique

In this paper, a concept for the mobile robotic system by using enabling IoT technique is described and architecture for enabling the mobile robotic system using web application is also discussed. The mobile robotic system is controlled through web-based applications and the air quality of the environment is also measured. A node Micro Controller Unit (node MCU) for wireless communications is used which is interacted with robots and their microcontroller. This node MCU uploads the data to the cloud network system. The robot can be remotely controlled using web applications and data are sent and stored in the cloud successfully. By demonstrating the robotic system capabilities, it is revealed that web-based applications can be used for controlling and monitoring the robotic system, and data can be stored which can be accessed from anywhere through mobile android applications. Thus, this gives an affordable solution for accessing/monitoring the pipelines/tunnels of coal mines, oil pipelines, etc. where the exploitation of the human being is very difficult. The use of the IoT cloud also facilitates storing data which leads to a new generation of the robotic system

Mobile Robots

The objective of this book is to cover advances of mobile robotics and related technologies applied for multi robot systems' design and development. Design of control system is a complex issue, requiring the application of information technologies to link the robots into a single network. Human robot interface becomes a demanding task, especially when we try to use sophisticated methods for brain signal processing. Generated electrophysiological signals can be used to command different devices, such as cars, wheelchair or even video games. A number of developments in navigation and path planning, including parallel programming, can be observed. Cooperative path planning, formation control of multi robotic agents, communication and distance measurement between agents are shown. Training of the mobile robot operators is very difficult task also because of several factors related to different task execution. The presented improvement is related to environment model generation based on autonomous mobile robot observations

Objective error criterion for evaluation of mapping accuracy based on sensor time-of-flight measurements

An objective error criterion is proposed for evaluating the accuracy of maps of unknown environments acquired by making range measurements with different sensing modalities and processing them with different techniques. The criterion can also be used for the assessment of goodness of fit of curves or shapes fitted to map points. A demonstrative example from ultrasonic mapping is given based on experimentally acquired time-of-flight measurements and compared with a very accurate laser map, considered as absolute reference. The results of the proposed criterion are compared with the Hausdorff metric and the median error criterion results. The error criterion is sufficiently general and flexible that it can be applied to discrete point maps acquired with other mapping techniques and sensing modalities as well