Abstract

The integration of wireless sensor networks (WSNs) and multirobot systems (MRS) represents an active research area supporting a wide range of applications. This is because it enables ubiquitous applications due to the robots' mobility and detection capabilities associated with its deployment. These systems have many benefits, such as perception with extended coverage that facilitate wider exploration and surveillance, efficiency in data routing, effective and reliable task environment management, etc. However, integrating two fields of research means dealing with a range of challenges such as using effective architecture for WSNs and MRS, efficient communication protocols within a network of sensors nodes and robots, cost of sensor network deployment, mobile robots coverage and deployment, robots' navigation and cooperation. This thesis presents the development of an autonomous networked robots (ANR) system featuring WSNs and MRS for event detection and surveillance of a large operational environment with IoT/cloud integration for data storage and analytics. The operational environment is divided into zones and each zone comprises static sensor nodes (SSNs), a mobile robot with onboard static sensor nodes (O-SSNs) and other sensors supporting its navigation. The zone mobile robot with its sensors is called the mobile robot sensor nodes (MRSNs). Additional MRSNs are used to navigate the spaces not covered by a zone and they are called global-MRSNs (G-MRSNs). G-MRSNS are identical to MRSNs but with different task assignments. The MRSN and G-MRSN communicate their data and the data from assigned spaces to the IoT BS through the base station gateway (BSG). A simulation development environment (SDE) is developed to facilitate the development, testing and validation of the different components of the system. The developed ANR system has a hybrid architecture with a two-tier communication network that enables the system simulation, hardware-in-the-loop (HIL) based simulation and real-time operation with results aggregation and visualization at the IoT base station (BS) graphic user interfaces (GUIs) and the integration of two cloud platforms whereby remote users can access the results for further processing and analytics. Hence, the hybrid architecture enables concurrent operation and integration between all system components in the simulation, HIL based simulation and real-time operation whereby the SSNs within zones detect and characterize events using a fuzzy logic decision making system and communicated them with possible alerts to the zone MRSNs through ZigBee network modules. When no alert is issued, the MRSNs and G-MRSN navigate the assigned paths within their spaces, while relaying detected information by the SSNs iv and O-SSNs through a wireless serial network to the IoT BS. In case one of the SSNs issued an alert, the zone MRSN relays the event to the IoT BS and starts to generate and navigate its path to the alerting SSN using the A* algorithm. If the zone MRSN is outside a reference distance to the alerting SSN location, it coordinates with the G-MRSN using contract net protocol (CNP) algorithm to assign the closer G-MRSN, generate the path to the alerting SSN and navigate it. The developed hybrid ANR architecture was implemented with all of its software and hardware components, and tested on the different levels i.e. the simulation, HIL based simulation and real-time operation, through the use of a selected factory type task environment representing four zones with its connected spaces. The robots' navigation paths are tracked and visualized on the MRS GUI, while the detection sensory data (temperature/RH, carbon monoxide gas, smoke, fire and motion) from the zones are aggregated by the robots and received at the IoT BS, updated and visualized on the SND GUI. From the SND GUI, the sensory information and the event classification are aggregated to the MS cloud platform for storage and basic analytics, while they are directly sent to the ThingSpeak cloud platform for advance analytics by the BSG. The ThingSpeak cloud data analytics involves the evaluation of various operational conditions within each zone such as temperature/relative humidity using regression analysis. Furthermore, it involves using a trained neural network for preservation metrics prediction per zone whereby the duration of storage and type of materials to be stored within the zones are determined. The developed hybrid ANR system was tested at all levels: the simulation, HIL based simulation and hardware implementation. Full hardware implementation of the system components was physically assembled and tested in a lab environment. The overall results showed a good performance and validated the concept of the developed hybrid architecture supporting the integration and the concurrent operation of all developed system components.

School

School of Sciences and Engineering

Department

Robotics, Control & Smart Systems Program

Degree Name

MS in Robotics, Control and Smart Systems

Graduation Date

Fall 1-6-2020

Submission Date

1-6-2020

First Advisor

Maki, K. Habib

Committee Member 1

Karim, Seddik

Committee Member 2

Mohammed, Watheq El-Kharashi

Extent

173 p.

Document Type

Master's Thesis

Rights

The author retains all rights with regard to copyright. The author certifies that written permission from the owner(s) of third-party copyrighted matter included in the thesis, dissertation, paper, or record of study has been obtained. The author further certifies that IRB approval has been obtained for this thesis, or that IRB approval is not necessary for this thesis. Insofar as this thesis, dissertation, paper, or record of study is an educational record as defined in the Family Educational Rights and Privacy Act (FERPA) (20 USC 1232g), the author has granted consent to disclosure of it to anyone who requests a copy. The author has granted the American University in Cairo or its agents a non-exclusive license to archive this thesis, dissertation, paper, or record of study, and to make it accessible, in whole or in part, in all forms of media, now or hereafter known.

Institutional Review Board (IRB) Approval

Not necessary for this item

Comments

AUC Graduate Research Grant

Creative Commons License

Creative Commons Attribution-No Derivative Works 4.0 International License
This work is licensed under a Creative Commons Attribution-No Derivative Works 4.0 International License.

Available for download on Tuesday, September 26, 2023

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Robotics Commons

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