POSTino: a mobile robot for delivery pickup

Introduction

The inspiration for this thesis project was derived from a task of the RoboCup@Home Education 2022. QThis competition is one of the main events in the field of educational robotics at a global scale, and in its @Home versions it has the purpose of making new generations of students interested in the fields of artificial intelligence and robotics approach the development of solutions that could have an effective impact on daily life, providing the educative instruments in order to develop innovative ideas.

The competition is divided in two categories: Standard Platform and Open Platform. The first one requires competing using only Pepper robots developed by Softbank Robotics, while the Open Platform (the one chosen in this case) admits participation using custom made robots. As described by the competition rulebook [@robocupRulebook], a score based on the overall completeness of the solution gets assigned to each team, which has the pobbility of accessing the final phase of the competition, where an additional task will be assignes. The chosen task for this project is named “Carry My Luggage”. The goal described consists in transporting a payload from the starting position to the final one while following an operator. Bonus points get rewarded in case it is demonstrated that the robot can navigate the surrounding environment in a “conscoius” way.

The inspiration, derived from the nature of the chosen task, brought to the development of an idea based on a robot whose function can have a real impact on daily life and in a domestic sphere. In particular, it sought to provide support for achieving the social distancing imposed by the COVID-19 pandemic to help effectively prevent new infections. The idea stems from the evidence of data reporting a marked increase since the start of the pandemic in the number of home deliveries of products of all kinds. Before illustrating this idea, it is useful to refer to the context in which it was born, that is food delivery (the delivery at home of ready meals), one of the fastest growing sectors with a market that has more than tripled since the beginning of 2019 [@marketEvolution], as highlighted in Figure 1.1{reference-type=“ref” reference=“fig:food_delivery_market”}.
\

Trend of the food delivery market between 2018 and2021{#fig:food_delivery_market width=“60%”}


However, the consequence of the surge in the number of deliveries has played havoc with the delivery operators themselves (the riders). Data retrieved from a study of Universidad de Las Américas [@ortiz2021high] expose how the occurrence of Covid-19 contagions can reach 15.2%, a significantly higher figure than the average for other workers, higher even than for physicians (incidence of 13.6%), which represents one of the most exposed categories. The main reason for this result is the continuous exposure to diverse people and low hygiene standards (as shown in Figure 1.2{reference-type=“ref” reference=“fig:riders_condition”}). This implies that diminishing contact between clients and riders can mean a reduction in the occurrence of new cases, bringing benefits to both categories.\

Main risk factors forriders{#fig:riders_condition width=“60%”}


The project, therefore, aims to develop a domestic robot that can act as an intermediary between the user and the rider, picking up delivery goods and bringing them to a predetermined point in the home.
Being in a dynamic context, where interaction with the environment and the users involved is required, the robot will have to be able to navigate autonomously within the environments (mapping functionality) of the house and will, in addition, have to interact with people (Text-To-Speech, Speech-To-Text), recognizing their faces (Face Detection). The robot employed in the implementation of this project is MARRtino, a robotic platform developed within our university and usually dedicated to educational robotics. The sensors and actuators that came with the robot enabled the implementation of the functionality described earlier. The structure of the robot and its operation will be explained in the chapters Tools Used and Methodology. Finally, the output of the project will be discussed in the chapters on Results and Conclusions.

Instrumentation

MARRtino Robot

MARRtino is an educational robotic platform developed by the department DIAG of Sapienza University. It was created with the aim of making a completely Open Hardware and Open Software project available to students of different levels (from high school students to master’s and doctoral students). This implies that the hardware and software specifications of the robot are freely disclosed, along with detailed documentation of its operation, allowing anyone who wants to be able to replicate the creation of the robot. Python, C++, and Matlab programming languages are supported, as well as various interfaces with which the robot can be programmed (block programming, text editor). The following will explain the hardware and software equipment the robot is composed of.

A complete view of the MARRtinorobot{#fig:marrtino_full_view width=“30%”}

Hardware components

The MARRtino robot can be thought of as the union of three basic components: sensors, actuators, and a logic and control unit. Briefly, the logic unit receives data about the environment collected by the sensors with which the robot is equipped and establishes control logic that determines the action of the actuators. The various components will be listed below, describing their function.

Logic and control unit
Sensors
Actuators
Input devices
Extras

One of the main risk factors for riders is the continuous exchange of cash. In a study by the European Central Bank [5] it was shown how the virus can persist for up to 72 hours on the surface of banknotes in a concentration that can cause infection. In order to prevent this risk, a bag with a reflective interior was mounted, to which a UV-C type LED strip was added. The effect of UV-C lights is indeed to deactivate the virus and prevent its replication, as reported by the U.S. Food and Drug Administration [2]. Therefore, the use of this device may contribute to the protection of both the food delivery operators and the users who use it, and also contribute to the sterilization of the delivery package.

MARRtino OS

ROS framework

ROS (Robot Operating System) is the framework on which MARRtino is based. The ROS project was born in 2007 at Stanford University to create a standard for programming robots of all shapes while maintaining an ease of use that makes it accessible to projects of any complexity. The fundamental insight of ROS is to implement a modular approach to robot development, relying on two fundamental components: nodes and topics. Each node represents a script that specializes in a single task to be performed, such as processing input data from a given sensor. The strength of this system is that the exchange of information between nodes is not direct, but is mediated by topics. A topic is a communication channel that specializes in a certain type of data to be communicated. Each piece of data defined as a message and is represented as a struct that combines several primitive data types. The node that inserts new data within a topic is named Publisher, while the nodes that receive data from that topic are called Subscribers. Figure 2.2 provides a graphical representation of this mechanism:

Publish/Subscribemechanism{#fig:ros_publish_subscribe width=“50%”}



Following the mechanism just described, the various functionalities of MARRtino are divided into atomic threads that behave like nodes, which are in turn grouped according to the functionality to which they contribute. Decisions regarding the implementation of the various functionalities will be discussed in Section 2.2.2 on software architecture. In addition to providing the basic mechanisms for exchanging information and executing processes, ROS provides a suite of applications useful for developing and testing the robot. In particular, the RViz (ROS Visualization) program used for visualizing information about the environment was employed during this experiment. This is used in two phases: during the mapping phase, it allows to see in real-time the construction of the map facilitating its construction, while in the navigation phase it allows visualizing the loaded map and shows the planning of the path that the robot will take.

RViz User Interfaceexample{#fig:rviz_interface width=“60%”}

Software achitecture

MARRtino’s operating system consists of a Ubuntu 18.04 image on which different instances of Docker containers corresponding to the robot’s various functionalities are run. Docker is open-source software that allows running containers, which are standardized units containing the software needed to run a given application. Unlike virtual machines, containers are distinguished by their lightness and speed, allowing a modular and secure approach to running different instances of applications. In the case of MARRtino, each container corresponds to an image of Ubuntu on which ROS is installed, along with the packages and custom software needed for certain functionality. The scripts that are launched within the containers are contained in the "marrtino_apps" folder, where each directory corresponds to a certain category of functionality. Each running container exposes a web-socket, making itself externally accessible via TCP protocol.
MARRtino can be accessed remotely by connecting to the hotspot generated by the robot itself. In this way, it is possible to connect to the terminal remotely via SSH protocol. In addition, a graphical user interface has been developed that simplifies the setup and execution of programs remotely accessible via a browser, the functionality of which will be discussed in Section 2.2.3 within which the suite of software made available with MARRtino is explained.

Softwarearchitecture{#fig:arch_software width=“70%”}

MARRtino Apps

Leveraging ROS’s modular approach, MARRtino’s operating system provides a series of scripts needed to operate the robot. Each process is represented by a file with a .launch extension, constituting a launch file. In the structure of ROS, a launch file is a document in XML format that contains the information needed to set up and launch one or more nodes. To execute such nodes simply invoke the roslaunch command by providing the launch file as a parameter. Launch files are organized so that they are grouped within folders according to the functionality and services to which they contribute. Specifically, the robot has the following functionality: orazio(robot), stage(simulator), navigation, mapping, vision, speech, objrec. Each of them runs in a separate Docker container. Given the educational purpose of the robot, in order to simplify its use, an abstraction layer has been implemented for the execution of the various launch files, which consists of a graphical user interface accessible via a Web browser at the robot’s IP address. Four sections constitute it: Configure, Bringup, Program, Viewer.

Homepage of the webinterface{#fig:int_fullview width=“50%”}

Configure

Section dedicated to the robot operating system. It allows you to check the status of the robot and perform operations such as system update.

Bringup

From this page, the status of active nodes and topics can be checked in real-time. It also lists all the robot’s functionality, which can be started or stopped using the Start and Stop commands. What happens during this interaction is the execution of the script via the robot shell that grabs the Docker container that contains that functionality. Through this page, the procedures outlined in Chapter 3 on Methodology are initiated.

 View of nodes and topics{#fig:nodes width=“60%”}

Program

An interface that lets the user write and execute Python code directly on the robot.

Viewer

It allows to visualize the feedback (such as the stream of images of robot’s sensors in real time

View of the Programpage{#fig:program width=“70%”}

Libraries used

Marrtino library

With MARRtino, is provided a Python library custom-developed to take exploit the robot’s various functionalities. The functions provided, in addition to enabling the robot to perform basic movements, combine various third-party technologies and services that expand its functionality.
The Text-To-Speech function makes use of Pico TTS. This is a cloudless service (that can be run locally), which given a text and the language to which it belongs, transforms it into audio (WAV file) which is then played by the robot’s speakers.
Speech-To-Text, on the other hand, requires a smartphone to process natural language through the Google Cloud Speech API. Communication with the robot is done through the Android LU4R application, which allows it to connect to the robot’s audio server and then transmit the processed information.
Finally, navigation functions make use of the chosen Navigator (in the case of the move_base experiment) to determine the robot’s path within the map.

OpenCV

OpenCV is an open-source software library used extensively in the fields of Computer Vision and Machine Learning. It provides more than 2,500 algorithms that are used in tasks such as moving object tracking, object detection, image stitching, and many others, which are implemented in professional solutions in different application fields. In the case of this project, the library is exploited for Face Detection. The Face Detection feature aims to detect and count the number of faces in a certain image. The implementation of this feature makes use of the Haar Cascade classifier. A cascade classifier is a classification algorithm that is based on the use of a cascading window. A cascading window is a window of fixed size that is scrolled within the image. At each new position, features are extracted from the identified quadrant. Figure 2.8 shows an example of the features of interest to the classifier.

Features examples{#fig:haar_features width=“40%”}

A version of Haar Cascade that specializes in face recognition (haarcascade_frontface_default) is used for the robot routine. Figure 2.9 shows an example of the classifier’s result applied to an image.

Detection of a face{#fig:sample_haar width=“70%”}

Methodology

Environment mapping

One of the fundamental functions of the robot is to be able to orient itself autonomously within the domestic environment in which it navigates. For this to be possible, it is necessary for the robot to know a priori the conformation of the environments in which it will move, which is encoded within a map. Therefore as a preliminary step to navigation, it was necessary to generate a new map by taking advantage of the robot’s LIDAR sensor.
The problem of having to create and update a map while simultaneously keeping track of the robot’s position within the map itself is named SLAM, which stands for Simultaneous Localization And Mapping. For this purpose was used the srrg_mapper script provided by MARRtino , which can be started via the dedicated web interface. A LIDAR sensor and a joystick are required for the execution of the mapping procedure. The first service to be started is the one dedicated to the joystick, which will allow the robot to be controlled via analog sticks. Once srrg_mapper is also started, the robot will set its home point to the position it is currently on and begin collecting data on the environment from the LIDAR sensor. A real-time representation of the map being constructed can be viewed via RViz within the virtual machine running on the laptop.
Figure 3.1 shows the robot creating a new map. The right quadrant shows the RViz screen representing the map being created. The lines in red show the edges that the LIDAR senses at that moment.
When you have finished the setup procedures, you will need to navigate within the environment using the joystick, replicating the same movements several times if necessary, to obtain a uniform map. To finish the procedure and save the map, simply select save map from the web interface.
Figure 3.2 shows the final result of the map, which will be used for the actual navigation of the robot. For a comparison of the quality of the scan, Figure 3.3 shows the overlay with a map created from the LIDAR sensor of an iPhone 13 Pro. As can be noted, the two maps are almost overlapping.

Comparison with a smartphone-generatedmap{#fig:iphone_map width=“70%”}

Comparison with a smartphone-generatedmap{#fig:iphone_map width=“50%”}

Comparison with a smartphone-generatedmap{#fig:iphone_map width=“50%”}

Speech-To-Text implementation

As described in Section 2.3.1 of Chapter 2, the Speech-To-Text function uses the MARRtino library, which takes advantage of communication with a smartphone to receive text from the Android LU4R application.

![Schematic representation of the Smartphone-Computer communication](assets/speech_to _text_structure.png){#fig:stt_communication width=“80%”}

Operationally, it is necessary to start the Audio Server from the MARRtino web interface, through which it will be possible to connect from the application. Once LU4R is open, it will be necessary to set the server’s IP address and port to connect. Once connected, the application will present the interface illustrated in Figure 3.5. To start voice recognition, simply press the ON icon. At that point, the text will be recognized automatically whenever speech is perceived and then communicated to the Audio Server. The MARRtino library provides the asr() function, which will have the effect of putting program execution on hold until new feedback is received from the Audio Server. The return value of this function will be the exact string corresponding to the recognized text, which can then be subjected to checks to verify the validity of the text.\

View of the LU4r Android app{#fig:lu4r width=“20%”}

View of the LU4R application

In order to navigate within an existing map, the robot will need to start the Localizer and Navigator modules. The first module is needed to determine in real-time the robot’s position within the map. In this case, the AMCL (Adaptive Monte Carlo Localization) Localizer was chosen. Its function is to determine the position of the robot as a result of a movement through the use of probabilistic calculations based on the data stream received from the LIDAR.
Given a certain target point to reach, the Navigator module will create the trajectory for the robot to follow within the map. The MARRtino operating system provides different types of Navigator, among them move_base was chosen. Move_base is a dedicated navigation framework belonging to the ROS distribution. Before the actual movement of the robot, the path planning operation takes place. This operation consists in tracing the path that the robot will have to follow within the virtual map. This is achieved through the cooperation of four basic units within the framework, which are responsible for processing data from the tracker and sensors. These units are: global planner, global costmap, local planner and local costmap. Global planner and global costmap refer to the map, identifying a cost function that depends on the obstacles already inside it and plotting the overall trajectory from the robot’s current point to its target. Local planner and local costmap, on the other hand, are the units responsible for correcting the estimates and path trajectory based on an assessment of the obstacles the robot faces, given a certain time and location. Figure 3.6 shows schematically how move_base works.

Schematic representation of move\_base{#fig:move_base_schema width=“80%”}

Once the necessary modules for navigation have been started, it will be necessary to establish a target point inside the map. This can be done by launching the RViz software from the laptop to view the map loaded on the robot and determine the gx,gy,gth coordinates of the target by activating the "focus camera" tool. The goto() function of the MARRtino library takes the coordinates of the target as input to initiate path planning and control the movement of the robot. An example of path calculation is shown in Figure 3.7. The line in purple represents the calculated path.

Computation of a trajectory usingmove\_base{#fig:path_planning width=“60%”}

Face Detection implementation

As explained in section 2.3.2, the Face Detection functionality is realized by using the haar cascade classifier belonging to the OpenCV library. The images to be processed come from the Orbbec Astra sensor mounted on the robot. To make the data stream from the RGB camera available to the program, it is necessary to activate the Camera module from the MARRtino web interface. Once started, the getImage() function of the MARRtino library will allow a frame to be saved, which can be stored as a variable, for later use within the program.
Face Detection functionality is accomplished by calling the countFaces function, which, taking as input the image and the classifier, converts the image to grayscale, and then proceeds to invoke the classifier that will extract information about the faces found within the faces variable. In the case of this project, the only significant information is the presence or absence of a face within the frame. The function is represented below:

def countFaces(image,classifier):
    #convert image to grayscale
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # Detect faces
    faces = classifier.detectMultiScale(gray, 1.1, 4)
    num_faces = len(faces)
    return num_faces

Discussion of results

The combination of the functionalities described in the previous chapter enabled the creation of a robot capable of navigating autonomously and interacting intelligently with the environment and users. Starting from its homepoint, the robot waits for the user to say the phrase "Delivery is coming." Subsequently, the house’s door is set as a target point of the robot, and a path is computed. Once there, the countFaces function is invoked until a person is recognized within the frame. When recognized, the robot will interact with the rider by reproducing the phrase "Hello, you can leave the delivery inside the bag. Let me know when you finished." and then wait for a response. Once the delivery is completed, the rider will have to confirm it by saying "delivered." The robot will greet the operator by saying "thank you and have a nice day," and then return to the homepoint.
Familiarization with the tools provided by the robot required several attempts for the complete execution of the routine. The most elaborate phase resulted to be the mapping phase since a thorough scan of the environments was required. This makes the creation of the map subject to several possible disturbances due to the imprecision that can be obtained by driving the robot manually using the joystick. The final map used during the routine was obtained after several iterations of this process.
Face Detection and Speech-To-Text features proved robust against the challenges presented by the environment. Both were tested against five different people, always returning positive results. People’s faces were always detected correctly, even under different lighting conditions and in presence of noise in the image derived from the webcam. Speech recognition was found to be robust with respect to background noise, always correctly recognizing the moment when the person starts to speak, although it did not always interpret the precise content of the speech.
The complete routine of the robot has been documented along with the mapping procedure in a video available online at the following url: [https://www.youtube.com/watchv=AbUo8WZJyv0&ab_channel=LudovicoComito]{style=“color: blue”}.
Figures 4.1 and 4.2 represent two frames extracted from the video, respectively showing the path planning execution and the interaction with the rider.

Face detection and interaction with theuser{#fig:routine_2 width=“90%”}

Face detection and interaction with theuser{#fig:routine_2 width=“90%”}

View of RViz during the routine of therobot{#fig:rviz_during_routine width=“100%”}

Routine of the robot{width=“40%”}

Conclusions

The use of the MARRtino robotics platform and the software made available by its operating system allowed to development of the prototype of a robot able to be actually useful inside a domestic environment. The implemented functions allowed for intelligent navigation within the environment in which the robot operates, as well as demonstrating the ability to interact with both the robot owner and the rider. This achievement aligns with the purpose of the Robocup@Home Education competition, which is to develop robotic solutions that are useful in everyday domestic life.
Programming the robot routine also allowed to delve into the subjects of robotics and artificial intelligence, ranging from topics such as mapping and autonomous navigation to computer vision. Ultimately becoming familiar with the ROS framework means having the ability to implement new features in the future while maintaining a modular approach.

Potential improvements

The development of this robot demonstrated that while it is possible to create a complete product demo even with an educational platform, interacting intelligently with the environment and the users is not an easy task to implement in a robot. One of the possible improvements can certainly be implementing voice interaction with users. In fact, the robot has been programmed to recognize a predefined string of text as a response, a procedure that is not very effective in real-life contexts. More advanced Natural Language Processing algorithms could therefore be implemented, which would allow more naturalness in the interaction with the user. Other improvements could be made to the robot’s equipment itself. Currently, the routine involves MARRtino reaching up to an already open door, as it does not have a mechanism to be able to open doors. A possible improvement could be equipping the robot with an additional manipulator that could open the door once the robot arrives. Finally, it should be pointed out that the robot is only able to move on flat surfaces. Therefore, the current wheels could be replaced with a system capable of moving on stairs, a function that could, for example, contribute positively to the daily experience of an elderly person who does not have complete mobility.