Design and Implementation of Indoor Disinfection Robot System

After the outbreak of COVID-19 virus, disinfection has become one of the important means of epidemic prevention. Traditional manual disinfection can easily cause cross infection problems. Using robots to complete disinfection work can reduce people's social contact and block the spread of viruses. This thesis implements an engineering prototype of a indoor disinfection robot from the perspective of product development, with the amin of using robots to replace manual disinfection operations. The thesis uses disinfection module, control module and navigation module to compose the hardware of the robot. The disinfection module uses ultrasonic atomizers, UV-C ultraviolet disinfection lamps, and air purifiers to disinfect and disinfect the ground and air respectively. The control module is responsible for the movement and obstacle avoidance of the robot. The navigation module uses Raspberry Pi and LiDAR to achieve real-time robot positioning and two-dimensional plane mapping. In terms of robot software,we have done the following work: (1) Based on the ROS framework, we have implemented functions such as SLAM mapping, location positioning, and odometer data calibration.(2) Customize communication protocols to manage peripheral devices such as UV-C lights, ultrasonic atomizers, air purifiers, and motors on the control board. (3) Develop an Android mobile app that utilizes ROSBridge's lightweight communication architecture to achieve cross platform data exchange between mobile devices and navigation boards, as well as network connectivity and interaction between mobile phones and robots Finally, this thesis implements an engineering prototype of a household disinfection robot from the perspective of product development

Desarrollo de una arquitectura para la implementación y simulación de misiones multi-UAV en la competición de robótica aérea MBZIRC

En este documento, se detalla la solución aplicada a la competición de robótica internacional MBZIRC que tuvo lugar en Abu Dhabi en Febrero del año 2020. Aquí, el lector podrá encontrar la arquitectura genérica realizada para resolver el reto propuesto por los organizadores, incluyendo detalles sobre el hardware empleado y realizando una extensa revisión del software y de su estructura interna, así como las herramientas utilizadas para llevarlas a cabo. También se mostrarán la condiciones de la competición, así como una descripción del Tercer Reto de la competición MBZIRC, junto con imágenes reales tomadas durante la misma.This document details the solution applied to the MBZIRC international robotics competition that took place in Abu Dhabi in February 2020. Here, the reader will find the generic architecture made to solve the Challenge 3 proposed by the organizers, including details about the hardware used and making an extensive revision of the software and its internal structure, as well as the tools used to carry them out. The conditions of the competition will also be shown, as well as a description of the MBZIRC Challenge 3, together with real images taken during the competition.Universidad de Sevilla. Máster en Ingeniería Electrónica, Robótica y Automátic

Robotic navigation and inspection of bridge bearings

This thesis focuses on the development of a robotic platform for bridge bearing inspection. The existing literature on this topic highlights an aspiration for increased automation of bridge inspection, due to an increasing amount of ageing infrastructure and costly inspection. Furthermore, bridge bearings are highlighted as being one of the most costly components of the bridge to maintain. However, although autonomous robotic inspection is often stated as an aspiration, the existing literature for robotic bridge inspection often neglects to include the requirement of autonomous navigation. To achieve autonomous inspection, some methods for mapping and localising in the bridge structure are required. This thesis compares existing methods for simultaneous localisation and mapping (SLAM) with localisation-only methods. In addition, a method for using pre-existing data to create maps for localisation is proposed. A robotic platform was developed and these methods for localisation and mapping were then compared in a laboratory environment and then in a real bridge environment. The errors in the bridge environment are greater than in the laboratory environment, but remained within a defined error bound. A combined approach is suggested as an appropriate method for combining the lower errors of a SLAM approach with the advantages of a localisation approach for defining existing goals. Longer-term testing in a real bridge environment is still required. The use of existing inspection data is then extended to the creation of a simulation environment, with the goal of creating a methodology for testing different configurations of bridges or robots in a more realistic environment than laboratory testing, or other existing simulation environments. Finally, the inspection of the structure surrounding the bridge bearing is considered, with a particular focus on the detection and segmentation of cracks in concrete. A deep learning approach is used to segment cracks from an existing dataset and compared to an existing machine learning approach, with the deep-learning approach achieving a higher performance using a pixel-based evaluation. Other evaluation methods were also compared that take the structure of the crack, and other related datasets, into account. The generalisation of the approach for crack segmentation is evaluated by comparing the results of the trained on different datasets. Finally, recommendations for improving the datasets to allow better comparisons in future work is given

Modeling and Control for Vision Based Rear Wheel Drive Robot and Solving Indoor SLAM Problem Using LIDAR

abstract: To achieve the ambitious long-term goal of a feet of cooperating Flexible Autonomous Machines operating in an uncertain Environment (FAME), this thesis addresses several critical modeling, design, control objectives for rear-wheel drive ground vehicles. Toward this ambitious goal, several critical objectives are addressed. One central objective of the thesis was to show how to build low-cost multi-capability robot platform that can be used for conducting FAME research. A TFC-KIT car chassis was augmented to provide a suite of substantive capabilities. The augmented vehicle (FreeSLAM Robot) costs less than $500 but offers the capability of commercially available vehicles costing over$2000. All demonstrations presented involve rear-wheel drive FreeSLAM robot. The following summarizes the key hardware demonstrations presented and analyzed: (1)Cruise (v, ) control along a line, (2) Cruise (v, ) control along a curve, (3) Planar (x, y) Cartesian Stabilization for rear wheel drive vehicle, (4) Finish the track with camera pan tilt structure in minimum time, (5) Finish the track without camera pan tilt structure in minimum time, (6) Vision based tracking performance with different cruise speed vx, (7) Vision based tracking performance with different camera fixed look-ahead distance L, (8) Vision based tracking performance with different delay Td from vision subsystem, (9) Manually remote controlled robot to perform indoor SLAM, (10) Autonomously line guided robot to perform indoor SLAM. For most cases, hardware data is compared with, and corroborated by, model based simulation data. In short, the thesis uses low-cost self-designed rear-wheel drive robot to demonstrate many capabilities that are critical in order to reach the longer-term FAME goal.Dissertation/ThesisDefense PresentationMasters Thesis Electrical Engineering 201

Localization And Mapping Of Unknown Locations And Tunnels With Unmanned Ground Vehicles

The main goals of this research were to enhance a commercial off the shelf (COTS) software platform to support unmanned ground vehicles (UGVs) exploring the complex environment of tunnels, to test the platform within a simulation environment, and to validate the architecture through field testing. Developing this platform will enhance the U. S. Army Engineering Research and Development Center’s (ERDC’s) current capabilities and create a safe and efficient autonomous vehicle to perform the following functions within tunnels: (1) localization (e.g., position tracking) and mapping of its environment, (2) traversing varied terrains, (3) sensing the environment for objects of interest, and (4) increasing the level of autonomy of UGVs available at the ERDC. The simulation experiments were performed in the STAGE Simulator, a physics-based multi-scale numerical test bed developed by Robotic Operating System (ROS). Physical testing was conducted in Vicksburg, MS using a Coroware Explorer. Both the simulation and physical testing evaluated three SLAM algorithms, i.e., Hector SLAM, gMapping, and CORESLAM to determine the superior algorithm. The superior algorithm was then used to localize the robot to the environment and autonomously travel from a start location to a destination location. Completion of this research has increased the ERDC’s level of autonomy for UGVs from tether to tele-operated to autonomous

10081 Abstracts Collection -- Cognitive Robotics

From 21.02. to 26.02.2010, the Dagstuhl Seminar 10081 ``Cognitive Robotics \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Design and implementation of a domestic disinfection robot based on 2D lidar

In the battle against the Covid-19, the demand for disinfection robots in China and other countries has increased rapidly. Manual disinfection is time-consuming, laborious, and has safety hazards. For large public areas, the deployment of human resources and the effectiveness of disinfection face significant challenges. Using robots for disinfection therefore becomes an ideal choice. At present, most disinfection robots on the market use ultraviolet or disinfectant to disinfect, or both. They are mostly put into service in hospitals, airports, hotels, shopping malls, office buildings, or other places with daily high foot traffic. These robots are often built-in with automatic navigation and intelligent recognition, ensuring day-to-day operations. However, they usually are expensive and need regular maintenance. The sweeping robots and window-cleaning robots have been put into massive use, but the domestic disinfection robots have not gained much attention. The health and safety of a family are also critical in epidemic prevention. This thesis proposes a low-cost, 2D lidar-based domestic disinfection robot and implements it. The robot possesses dry fog disinfection, ultraviolet disinfection, and air cleaning. The thesis is mainly engaged in the following work: The design and implementation of the control board of the robot chassis are elaborated in this thesis. The control board uses STM32F103ZET6 as the MCU. Infrared sensors are used in the robot to prevent from falling over and walk along the wall. The Ultrasonic sensor is installed in the front of the chassis to detect and avoid the path's obstacles. Photoelectric switches are used to record the information when the potential collisions happen in the early phase of mapping. The disinfection robot adopts a centrifugal fan and HEPA filter for air purification. The ceramic atomizer is used to break up the disinfectant's molecular structure to produce the dry fog. The UV germicidal lamp is installed at the bottom of the chassis to disinfect the ground. The robot uses an air pollution sensor to estimate the air quality. Motors are used to drive the chassis to move. The lidar transmits its data to the navigation board directly through the wires and the edge-board contact on the control board. The control board also manages the atmosphere LEDs, horn, press-buttons, battery, LDC, and temperature-humidity sensor. It exchanges data with and executes the command from the navigation board and manages all kinds of peripheral devices. Thus, it is the administrative unit of the disinfection robot. Moreover, the robot is designed in a way that reduces costs while ensuring quality. The control board’s embedded software is realized and analyzed in the thesis. The communication protocol that links the control board and the navigation board is implemented in software. Standard commands, specific commands, error handling, and the data packet format are detailed and processed in software. The software effectively drives and manages the peripheral devices. SLAMWARE CORE is used as the navigation board to complete the system design. System tests like disinfecting, mapping, navigating, and anti-falling were performed to polish and adjust the structure and functionalities of the robot. Raspberry Pi is also used with the control board to explore 2D Simultaneous Localization and Mapping (SLAM) algorithms, such as Hector, Karto, and Cartographer, in Robot Operating System (ROS) for the robot’s further development. The thesis is written from the perspective of engineering practice and proposes a feasible design for a domestic disinfection robot. Hardware, embedded software, and system tests are covered in the thesis

Localization and 2D Mapping Using Low-Cost Lidar

Autonomous vehicles are expected to make a profound change in auto industry. An autonomous vehicle is a vehicle that is able to sense its surroundings and travel with little or no human intervention. The four key capabilities of autonomous vehicles are a comprehensive understanding of sensor data, knowledge of their positions in the world, building the map of unknown environment, as well as following the planed route and collision avoidance. This thesis is aimed at building a low-cost autonomous vehicle prototype that is capable of localization and 2D mapping simultaneously. In addition, the prototype should be able to detect obstacles and avoid collision. In this thesis, a Redbot is utilized as a moving vehicle to evaluate collision avoidance functionality. A mechnical bumper in front of the Redbot is used to detect obstacles, and a remote user can send appropriate commands to control the Redbot via Zigbee network, then Redbot acts accordingly, including driving straightly, changing direction to right or left, and stop. Redbot are also used to carry the lidar scanner which consists of Lidar Lite V3 and a servo motor. Lidar data are sent back to a Laptop running ROS via Zigbee network. In ROS, Hector SLAM metapackage is adopted to process the lidar data, and realize the functionality of simultaneous localization and 2D mapping. After implementing the autonomous vehicle prototype, a series of tests are con- ducted to evaluate the functionality of localization, 2D mapping, obstacle detection, and collision avoidance. The results demonstrated that the prototype is capable of building usable 2D maps of unknown environment, simultaneous localization, obstacle detection and collision avoidance in time. Due to the limited scan range of the low-cost lidar scanner, boundary missing problem can happen. This limitation can be solved through the use of a lidar scanner with larger scan range

Autonomous navigation with ROS for a mobile robot in agricultural fields

Autonomous monitoring of agricultural farms and fields has recently become feasible due to continuing advances in robotics technology, but many notable challenges remain. In this paper, we describe the state of ongoing work to create a fully autonomous ground rover platform for monitoring and intervention tasks on modern farms that is built using inexpensive and off the shelf hardware and Robot Operating System (ROS) software so as to be affordable to farmers. The hardware and software architectures used in this rover are described along with challenges and solutions in odometry and localization, object recognition and mapping, and path planning algorithms under the constraints of the current hardware. Results obtained from laboratory and field testing show both the key challenges to be overcome, and the current successes in applying a low-cost rover platform to the task of autonomously navigating the outdoor farming environment