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%%%%% CHAPTER 2. Navigation in Service Robotics
\chapter{Navigation in Service Robotics}\label{Title_Navigation_in_Service_Robotics}

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\par This chapter first introduces the background and principles of service robotics, classifying the modern day service robots based on application and introduces the technologies in use. With regards to the work of this thesis, the role of service robotics in precision agriculture is explained. This leads to defining the navigation and localization technologies in precision agriculture service robotics. At the end, the approach taken in the thesis towards collaborative positioning solutions and the use of Android smartphones to represent COTS GNSS receivers is discussed. %

%% SECTION 2.1
\section{Service Robotics Background}\label{ServiceRobotics}

\par In the early days of robotics, the basic principle of automation was strictly applied to its use., i.e., the work processes were divided into individual action sequences in order to examine which of these sequences could be automated \cite{decker2017service}. Mechanical repeatable tasks were performed by machines while non-automatable tasks were handled by humans, all towards reducing labour costs to increase industrial efficiency. Nowadays robots are widely seen as machines capable of carrying out a complex series of actions \cite{Singer2009WiredFW}. Due to technological advances in engineering and integration of robotics with the fields of artificial intelligence, cloud computing, big data, etc., a wide range of innovation in robotics implementation and application is being seen with the potential to change service industries being immense. As a consequence of machine intelligence and coherent interaction also between machines only, the possible application fields for robots continuously expand and increasingly leave well-defined and protected environments like factories \cite{decker2017service}. New technologies and services, such as the Internet of Robotic Things \cite{simoens2018internet} emerge as opportunities of complex merger of robots and autonomous systems  with ubiquitous sensors. It can be foretold that in the future virtually all service robots will be connected and embedded into a bigger system (e.g. via knowledge bases and cloud-based systems)\cite{pagallo2013robots}. From a paradigm of cooperation between humans and machines, today robots take up of substitution, cooperation and also expansion of human skills. The role of robotics has moved even beyond the important economic cost–benefit analysis, for example with the comparison of skill of a robot to human while performing tasks such as deep sea ocean exploration, landing on Mars or reacting to disaster situations in complicated environments.


\subsection{Modern day Service Robotics} \label{Chapter 2_1_1}

\par With a basic examination based on the development activities across applications and industries, four broad categories can be assigned to modern service robots \cite{lee2021service}.
	
\subsubsection{Professional Non-Social Service Robots}  \label{Prof_non_social}

\par The services of these robots do not necessarily involve social interaction with employees and customers and their work could be in both indoor and outdoor environments. The specific environment and application defines their development and research direction. Examples can include from agricultural robots  \cite{shivaprasad2014design} and outdoor hazardous terrain climbing robots \cite{luk2005intelligent} to hospital service robots \cite{shieh2004design} and autonomous sewer robots \cite{scholl1999articulated}.

\subsubsection{Professional Social Service Robots} \label{Prof_social}

\par These service robots provide employees and customers with interactive situation-specific services and their roles are seen in use in business organisations or in public services. Social service robots require versatile and robust perception systems and solid interaction strategies \cite{pinillos2016long}. Examples can include from a hotel bellboy service robot to a mobile guidance robot at an airport \cite{triebel2016spencer}. 

\subsubsection{Domestic/Personal Non-Social Service Robots} \label{Dom_non_social}

\par Domestic/personal non-service robots are typically used for non-commercial services and are built to be highly autonomous in their predefined task area. Examples include outdoor lawn mowing robots\cite{verne2020adapting} and indoor vacuum robots \cite{fink2013living}.

\subsubsection{Domestic/Personal Social Service Robots} \label{Dom_social}
\par Interaction with humans is the key role of these robots and with the innovations in machine learning, deep learning, and artificial intelligence, service robots have become more social, intelligent, and adaptive. Examples include an elderly care and wellness robot \cite{hendrich2015architecture} to a social companion robot \cite{bien2008intelligent}.

\begin{figure*}[!hbt]
\centering
\includegraphics[width=0.90\textwidth]{TechnologyStack.png}
\caption{The technological stack of a modern service robot}
\label{fig:TechnologyStack}
\end{figure*}


\par As detailed in \cite{gonzalez2021service} and shown in  Figure \ref{fig:TechnologyStack}, service robotics today are a merger of various technologies. The technological stack of a modern service robot must address an uncontrolled environment and the complex integration of varied sensors should efficiently perform the expected robotic task. The three main technical groups to enable a service robot are software layers, contextualization and human-robot interfaces \cite{gonzalez2021service}. 

\par The software layers enable the connection, integration and synchronisation of the sensors within the robot while establishing a standard communication system for the components. Robot task planning and predefined and/or real time path planning is also a vital role of the software layer. Artifitial intelligence has always been important in service robotics dealing with specific aspects of their applications, such as analyzing images collected in agricultural settings, filtering operational data in manufacturing environments, or coordinating swarms of mobile robots in logistics. Contextualization includes the vital functions of localization and sensorization which allow the service robot to be aware of the spatial context of its environment and provide the tools to carry out its roles. Human-robot interfaces impelement the integration of the service robot to the human workflow. Web dashboards, touch screens, mobile device applications and speech recognition are the broad enablers of this.

\par Further reading on modern day service robots can be found at \cite{wirtz2018brave,4799444,ifr}.

\subsection{Service Robots in Precision Agriculture} \label{Chapter 2_1_2}

\par The work of this thesis is funded by the PoliTO Interdepartmental Center for Service Robotics (PIC4SER) who aims to coordinate the activities of several research groups on the enabling technologies necessary for the development of the highly innovative and multi-disciplinary area of service robots. As detailed in Section \ref{Objectives} focus of the work was put on precision agriculture service robotics, targetting low cost and robust navigation solutions. Precision agriculture, emerging in the 1980s, is a kind of management system that combines multiple technologies, including sensors, computer information technology, mechanical technique, etc \cite{gebbers2010precision}. Its development has been driven by the desire to better handle the spatial and temporal variability, e.g. in soil water-content or crop varieties, from farm-scale, down to field-scale, through to sub-field scale \cite{blackmore2009new}. 

\begin{figure*}[!hbt]
\centering
\includegraphics[width=0.90\textwidth]{SensorsConstellation.png}
\caption{Types of sensors in agricultural robots}
\label{fig:SensorsConstellation}
\end{figure*}

\par The introduction of agricultural robots gave a significant boost to the process of precision agriculture through automation. Robotic technologies potentially can also increase the window of opportunity for intervention, for example, being able to travel on wet soils, work at night, etc. Sensory data collected by robotic platforms in the field can further provide a wealth of information about soil, seeds, livestock, crops, costs, farm equipment and the use of water and fertiliser whereas Low-cost Internet of Things (IoT) technologies and advanced analytics are already beginning to help farmers analyze data on weather, temperature, moisture, prices, etc \cite{duckett2018agricultural}. These service robots include both UAV and UGVs can carry out a variety of tasks such as seeding, spraying, fertilizing, etc., to help producers manage the farmland better and improve the yield. The sensors deployed on these agricultural robots are the essential components for them to implement their functions and the constellation of sensors can be broadly listed as in Figure \ref{fig:SensorsConstellation} and detailed in \cite{li2019summary}.

\par With the development and integration of these multiple sensors, these service robots can accomplish greater outputs and efficiency through precision, cooperation and enhancement of human capabilities in the agricultural sector. A lot of these robots are however still not intelligent enough and remain prototypes without much commercialization \cite{li2019summary} due to reasons such as deficiencies in integration algorithms and processors, adaptation to complex environments and operating power capabilities. Therefore research into development of better sensors and streamlined algorithms is essential to realise the potential of agricultural robots in complex agricultural environments. 

\par \cite{bechar2016agricultural} reviews most of the recent research and development in agricultural field robotics.



%% SECTION 2.2
\section{Navigation and in Service Robotics} \label{Navigation_in_Service_Robotics}

\par The concept of fully autonomous agricultural vehicles is far from new and examples of early ‘driverless tractor’ prototypes using leader cable guidance systems date back to the 1950s and 1960s \cite{li2019summary,morgan1958step}. However autonomous navigation in an agricultural environment is a difficult task due to the inherent uncertainty in the environment where shapes, sizes and colors of plants, light intensity and overall surroundings vary \cite{pattinson2019gnss}. Hence different auto navigation systems for agricultural machinery have been invented and evaluated in the past decades and \cite{wilson2000guidance} provides a brief overview of research efforts over the past 50 years directed toward the development of guidance systems for agricultural vehicles. A modern day autonomous agricultural robot utilizes sensors to collect its ambient information as well as the state of the vehicle. This helps the robot inticipate its next variable state and steering angle which is controlled by dedicated algorithms. The process is detailed in \cite{mousazadeh2013technical} and can be summarized in a basic control diagram of autonomous vehicle as shown in Figure \ref{fig:controldiagram}. The list is not exhaustive and only the most popular sensors, algorithms and steering controllers are mentioned. Detailed descriptions and pros and cons of all of them the are beyond the scope of this thesis and can be found in \cite{mousazadeh2013technical,wilson2000guidance,keicher2000automatic}. 

\begin{figure*}[!hbt]
\centering
\includegraphics[width=0.90\textwidth]{controldiagram.png}
\caption{A basic control diagram of an autonomous vehicle}
\label{fig:controldiagram}
\end{figure*}


\par For any modern outdoor navigation system, localization is essential. Mission and path planning along with navigation control and guidance of a service robot follows a localized system of a service robot and its environment. The modern localization methodologies can be categorized into GNSS based localization, Vision based localization and Sensor fused localization.

\subsection{GNSS Based Localization}\label{GNSS_Based_Localization}

\par GNSS receivers have been used as global localization and guidance sensors since the early 1990s due to them providing an absolute position based guidance system compared to relative guidance of other sensors. The desired application accuracy of localization in agriculture ranges from 12-40 cm for tillage to 2-4 cm for planting \cite{han2018recent}. For path guidance within agricultural fields, the required accuracies could range from 10-20 cm to a meter depending on the agricultural environment. A standard stand-alone GNSS receiver is of course not capable of providing such accuracies and instead many different correction services are available such as free differential satellite corrections, commercial satellite differential corrections forming Precise Point Positioning (PPP) services and Real Time Kinematic (RTK) corrections, either virtually or with a base GNSS station within 10 kms. Multiple studies and analysis conclude that Real Time Kinematics (RTK) clearly provides the best accuracy performance in agricultural environments, if cost constraints of devices are not considered \cite{coyne2003comparison,rovira2015role}. With an experimental testbed and methodology for assessing RTK GNSS receivers in precision agricultre environments, \cite{pini2020experimental} concluded that RTK GNSS can match target performance requirements and, in turn, be used for machinery guidance and automatic field operations, only with reliable wireless channels and mobile network coverage. Further GNSS modernization has been targeted during the last decade with the addition of new navigation signals (L2C, L5 and L1C) to respective GNSS satellite constellations. Further oher GNSS systems have borne fruition with China's BEIDOU being operational and Europe's GALILEO almost reaching full operational capability. This will enable faster signal acquisition, enhanced reliability, greater operating range, and better signal reception in challenging environments.With such improved accuracy and enhanced reliability, GNSS will be the first choice of localization sensor for agricultural vehicle navigation in the future as well \cite{han2018recent}.

\par Chapter \ref{GlobalNavigationSatelliteSystems} is dedicated to describe GNSS technology in further detail.

\subsection{Vision Based Localization}\label{Vision_Based_Localization}

\par GNSS alone is insufficient in agricultural applications where a robotic vehicle has to follow crop lines or rows with accuracy of centimeters. For this robotic vehicles have vision based systems, either or both of monocular and stereo based, to enable it to find relative positions between the vehicle and the rows. Localization with a monocular vision systems involves the steps of image acquisiton, image calibration, image segmentation, row detection and finally calculation of navigational errors \cite{han2018recent}. With the advancement of computing and processing technology, vehicle localization and guidance using stereo vision systems have become possible with affordable hardware and its main advantage over monocular vision remains in its capability of range detection in addition to the color/feature detection \cite{han2018recent}. Stereo vision based vehicle guidance has been an active topic of research and \cite{kise2005stereovision} reported a stereo vision-based crop row detection system to automatically navigate a tractor in a soybean field with a lateral deviation of less than 5 cm at the speeds up to 3.0 m/s. The vision system can provide very rich information, including color, shape, and depth of objects (rows), which can be easily integrated onto a vehicle due to the small footprint. \cite{costa2019stereovision} aimed to verify the closeness of agreement between manual and stereo-image measurements, and thus to provide helpful information regarding safety and working purposes. With the addition of advanced vision systems, including depth perception, scanning sensors such as LiDAR and artificial intelligence for decision making and classification, the concept of precision can be potentially taken to another level \cite{duckett2018agricultural}. A vision based system could also be an alternative localization sensor for agricultural vehicle guidance when GNSS signals are unavailable.

\subsection{Sensor Fused Localization}\label{Sensor_Fused_Localization}

\par As listed in Figure \ref{fig:SensorsConstellation}, there are generally multiple sensors playing a role in the overall navigation guidance system of agricultural robots. Each type of sensor has limitations in agricultural environments. GNSS receivers are most susceptible to harsh environment conditions such as tree canopy, buildings, etc. A vision based guidance system suffers when there is illumination variance or a cluttered background in agricultural fields. Most tactile and laser sensors work in only pre-defined limited operations and inertial sensors have time accumulated error problems. Therefore a multi-modal systems on a robot based on a combination of GPS, INS, LiDAR, vision, etc has the best potential to provide robust and accurate solutions with requiring external in-field infrastructure. A simple sensor fusion strategy would be to select only the best sensor at a time based on the ambient conditions of the robot. However most research based and commercial modern day navigation system designs fuse the output of each sensor to achieve robustness, reliability and higher positioning velocity and accelaration update rates. However developing fusing algorithms are challenging due to sensors being in different coordinate systems, time synchronizations and weight estimates of each sensor output. Traditionally this is tackled using the Kalman filter algorithm \cite{sun2004multi} and its derivatives such as the extended Kalman filter and unscented Kalman filter. Adaptive and optimization-based approaches are used to overcome problems of fine tuning the Kalman filter parameters and it is still an active field of research.

%\subsection{Challenges to UAV-UGV Navigation}\label{Navigation_Challenges}%CHANGE THE TITLE definitely
% mention costs, multipath, etc. and network issues as well. BRING IN SECURITY ISSUE AS WELL.

%% SECTION 2.3
\section{Towards Low Cost Navigation Solutions in Service Robotics}\label{Low_Cost_Navigation_Solutions}

\par GNSS, being an absolute positioning method form the core of most navigation systems in outdoor agricultural activities and is one of two positioning methods along with dead reckoning sensors based relative positioning. As mentioned before, the focus of the work on this thesis has been to achieve low cost and robust navigation solutions for precision agriculture.  Multiple navigation sensors integration strategies \cite{yoon2007navigation} and robust GNSS receivers and external aidings \cite{saleem2015integration} of course come at the price of steeper costs and high-complexity set-ups. Lowering costs and higher performance of COTS GNSS receivers along with the constant modernization of GNSS signals leads the work to exploiting already existing set-ups in agricultural robotics to improve GNSS positioning solutions. For example, agricultural UAVs are assumed to self-locate with a decimeter-level precision to guarantee reliable in-flight operational capabilities. Thanks to the in-flight stability they can also provide reliable relative positioning of objects included within the field of view of the on-board vision system (i.e. digital camera) thus turning in a promising external positioning sensor for ground vehicles \cite{kurdi2018proposed}. 

\par Heading towards low cost navigation solutions lead to exploring a GNSS Collaborative Positioning (CP) approach in this thesis work exploiting standard UAV-UGV paradigms in agricultural robotics with active communication networks. Advances in communication networks will allow the integration of relative measurements among connected GNSS receivers which can enable the receivers to either improve on or even determine their own location through this collaboration. In fact, most of the early contributions addressed CP to provide positioning and navigation in GNSS-denied environment and they were mostly focused on sensor networks \cite{minetto2020gnss}. \cite{roumeliotis2002distributed,mourikis2006performance} were some of the first contributions to CP strategies in the field or robotics and later CP methodologies to solve for localization of multiple robotic vehicles were introduced \cite{kia2016cooperative,shames2011cooperative}. A novel approach to range only localization has been also proposed in \cite{uluskan2018circular} where terrestrial range measurements retrieved from occasional anchors allow to approximate the position estimate. The arguement of superiority of collaborative methodologies over differential GPS techniques have been presented in literature \cite{minetto2019gnss,minetto2018tight} and thus the fusion of differential GNSS measurements and collaborative navigation techniques could be explored for future generation of GNSS receivers.

\par In parallel, the availability of ultra-low cost embedded Global Navigation Satellite System (GNSS) has enabled several affordable Location Based Services (LBS). New chipsets supporting dual frequency and multi-constellation GNSS signals are reducing the gap between high grade and mass market GNSS device performances. Among these devices, Android smartphones represent valuable and affordable tools for many LBS in the early advent of Intelligent Transportation Systems and smart vehicular navigation. Mobile networks natively provide a multiplicity of connected devices, thus enabling a family of applications demanding for a communication channel among GNSS receivers and in the case of this thesis, CP based approaches. A remarkable amount of works in literature is present about range-based collaborative methods for positioning and navigation in robotics \cite{1067998} and more recently such interesting approaches have been considered within a GNSS-only framework suitable for low-cost hardware \cite{8373543}. Successful implementation of such with the ultra-low cost GNSS chipsets of Android smartphones are going to open a plethora of applications. For example, the impact of relative positioning on pedestrian navigation or the use of raw measurements for basic proximity indication of users among LBS. The shift in improvement of quality and features (multi-frequency, multi-constellation) of smartphone modules could extend similar applications to drones and service robotics as well.

\par The performance of GNSS receivers on Android smartphones is provided in Section \ref{AndroidSmartpohnes_Based_GNSS} and the CP approaches applicable to agricultural service robotics carried out in this work is detailed in Section \ref{Collaborative_GNSS_Methodology_and_Application}. 

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