Towards a Unified Platform for Agent-based Cloud Robotics

Francisca Rosique, Pedro Sánchez, Diego Alonso and Juan Antonio López

Systems and Electronic Engineering Division (DSIE), Universidad Politécnica de Cartagena, Cartagena, Spain

Keywords: Teleo-reactive, Agents, Cloud, Robotics, Drone.

Abstract: This paper describes a platform that aims to design, build, and validate a new generation of cloud robotic

platforms that enable agent-based intelligent control of robots deployed in unknown and dynamic

environments. The platform will consider: (1) novel techniques for programming reactive plans and robotic

behaviours through missions and novel mechanisms for building new behaviours from existing ones, both

for experienced and non-expert users; (2) novel multi-layered cloud platform as the infrastructure to

maintain a continuous link between the robots acting on a physical environment and their agent

counterparts, to provide sensor data from robots to agents, and to provide high-level autonomous decisions

from agents to robots.

1 INTRODUCTION

Intelligent agents are autonomous and heterogeneous

entities that can perceive their environment and are

able to achieve tasks on their own or

collaborating/competing with other agents (Russell

and Norvig, 2003). Agent technology advances can

be helpful in the robotic areas providing capabilities

such as planning, learning and decision-making

(Thrun et al., 2005), (Kaminka, 2012). Distributed

robotic applications can be more easily obtained

considering robotic devices as avatars of virtual

agents (Dipsis, 2010). These agents can use Teleo-

Reactive (TR) programs to support flexible agent

control, high-level goal achievement, and

collaboration between robots (Benson and

Nilsson, 1995), (Clark and Robinson, 2014).

Traditionally, robotic developers have to set up

multiple computing layers to begin building an

application. From identifying a server, installing the

Operating System, to deciding on programming

languages and running preliminary testing. It is

anything but simple. It needs a team of experienced

developers to make the solution work. In this

context, we propose a unified platform for agent-

based cloud robotics (Robotic Agents in the Cloud -

RAC) that will leverage the knowledge and

capabilities needed for developing such systems.

RAC will abstract away from almost all of this

through the development of a cloud-based execution

platform that augments low-level cognitive robots

with high-level cognitive behaviours using theTR

approach suitably extended with reasoning under

uncertainty. The TR formalism will make the

specification of the expected behaviour easier than

traditional programming languages, since programs

are written in terms of a sequence of goals that the

system has to reach (i.e., missions). Because TR

programs are much easier to read than to write, non-

technical users could easily understand TR programs

written by experienced developers, deciding which

missions fit best to the problem they try to solve. So,

the programming of robots can reach a wider set of

end users. At the same time, the cloud infrastructure

will abstract almost all the resources needed to

perform simulation and computation tasks.

In this context, the proposal will provide technical

and non-technical users with intuitive application

development tools to easily create custom

assemblies of autonomous robots to solve

application specific tasks, as if they were involved in

a mission. Applications will be hosted in the cloud

without the need of programming code, using

simple, and easy-to-use visual development tools

based on a drag and drop development model (see

Scratch tool (Resnick, 2009) from MIT as an

example).

The proposal will be validated through a cloud-

based platform with scenarios from the civil area

including: critical infrastructure surveillance and

search and rescue in emergencies. By using

Unmanned Aerial Vehicles (UAVs), commonly

known as drones, we expect our approach to be

Rosique F., Sánchez P., Alonso D. and López J..

Towards a Uniﬁed Platform for Agent-based Cloud Robotics.

DOI: 10.5220/0005553700590064

In Proceedings of the 10th International Conference on Software Engineering and Applications (ICSOFT-EA-2015), pages 59-64

ISBN: 978-989-758-114-4

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

transferrable to other potential drone applications

areas like agriculture, commercial and logistics and

transport.

Figure 1 shows an overview of the platform from the

point of view of the end-user. Though drones have

been the robots selected for the demonstrators, it is

meant that other type of robots could be consider

and integrated in the solution. So, a typical non-

technical user could build a mission by: (1)

accessing the TR behaviour repository with missions

previously tested by other, experienced users, so that

the sharing of knowledge and experiences between

users is promoted; (2) configure, modify or extend

the reused missions; (3) simulate in a 3D

environment the expected behaviour of the TR

program associated to the mission; (4) deploy in real

the validated TR program to execute mission; and

(5) visualize and analyse the data (video, sensor

data, etc.) received from the drone during the

execution.

Figure 1: Overview of the approach with the involved

actors.

Moreover, RAC is aligned with the Robotics 2020

Strategic Research Agenda (SRA) of Europe

(produced by Euroboticsaisbl) (EUROP, 2010), as it

allows exploiting technology in key areas such as

agriculture, commercial and society security. It is

then needed more disruptive technologies ahead of

the wave, more markets to exploit emergent

robotics, and more awareness in Society of the

potential for robotic systems. Robot’s ability to

monitor large areas from the air and to work in

hazardous environments will provide new and cost

effective ways to gather valuable data in civil

security (border protection, patrolling of plants,

rescue, surveillance, etc.). This may represent an

additional opportunity for the creation of jobs and

the stimulation of the European economy given the

potential set of applications and SME that can

benefit from the results. Besides, making robot

programming easier and more affordable will greatly

increase their demand, boosting the creation of new

manufacturers of drones.

This paper is structured as follows: Section 2

describes a general overview of the proposal. Next,

Section 3 presents several works related to TR and

Cloud systems development. This is followed, in

Section 4, by the description of the scenarios of

implementation in which the proposal is used for

demonstrating the application of the approach.

Finally, in Section 5, we discuss our future work and

present some concluding remark.

2 GENERAL OVERVIEW

The main idea behind RAC is the development of a

cloud-based execution platform that augments low-

level cognitive robots with high-level cognitive

behaviours using the TR approach extended with

reasoning under uncertainty. The work will be

undertaken from the next four main perspectives:

intelligent robotic agent modelling, tools and

infrastructure (TR program implementation, 3D

simulation, support in the cloud, etc.), drone

integration, and real life demonstrators in domain of

high interest as stated in the SRA for Robotics in

Europe.

For this proposal, we will provide the TR

specification language and the implementations for

executing missions (with possible collaboration of

drones, making use of probabilistic reasoning under

uncertainty). These outcomes will be needed in a 3D

simulation environment for simulating the TR

programs executed on drones. Moreover, we will

provide a platform in the cloud that supports and

brings together the software artefacts developed,

giving to users a front-end to develop and deploy TR

missions. This cloud platform will cover the full life-

cycle of TR applications enabling users to: (i)

develop TR programs, (ii) test them with the 3D

simulation environment, and (iii) deploy them in real

drones, monitor their operation in the real

environment and store sensors readings for future

needs. A reusable repository of TR specifications

will enhance the reuse of open missions between

users and developers. Users will be able to learn

about the TR paradigm through educational material

specifically conceived for the proposal. The front-

end will provide users a novel visual language to

build their own TR missions. The demonstrators will

need first to be modelled and simulated using the

Virtual Simulator. Once the results of the simulation

are deemed adequate the missions can be executed.

ICSOFT-EA2015-10thInternationalConferenceonSoftwareEngineeringandApplications

Analysis of the simulations will also be used to

cross-validate with the results of the real-world

missions. To address this idea, the functionality

provided for RAC is organized in a three layer

architecture: (1) Infrastructure Layer; (2) Platform

Layer; and (3) Application/Service Layer. Figure 2

shows the main components of the RAC platform

where the stakeholders make use of the services

provided at the Application Service Layer (TR

program simulation and execution, TR program

edition, educational resources for learning the

paradigm, repositories of TR missions, etc.). The

platform layer includes those modules to be

developed for the proposal.

Figure 2: Architecture of RAC.

3 RELATED WORK

The use of TR programs for intelligent agents as

well as robotics has been extensively studied in the

literature. In (Benson and Nilsson, 1995), a quite

elaborate agent architecture is described that makes

use of TR procedures represented as trees, which

was introduced in (Nilsson, 1994). Nilsson’s agent

tower architecture paper (Nilsson, 2001) extended

his robust goal-directed TR robotic agent

programming language with an inference capability

using the tower of rule defined predicates sitting on

top of a set of rapidly changing percept facts. The

predicate definitions allow TR procedures towards

the top of the tower of TR procedures to have

Guard -> Action rules with guards that query and

interpret current percepts using application specific

concepts and a declarative model of the agent’s

environment. TR programs have also been

experimented in the RoboCup competitions

(Gubsich et al., 2008). In RAC we will interpret TR-

procedures with TeleoR (Clark and

Robinson, 2014), a typed higher-order major

extension of Nilsson’s Teleo-Reactive robotic agent

language. We will also combine TeleoR with a

flexibly-typed moded logic and functional language

QuLog (Clark and Robinson, 2015) for representing

the agent’s belief store and making inferences.

On the other hand, several research groups are

exploring the idea of robots that rely on cloud-

computing infrastructures to access vast amounts of

processing power and data. Some of the existing

cloud robotics projects and initiatives are listed

below:

 RoboEarth (Roboearth, 2013) is a European

project led by the Eindhoven University of

Technology, in the Netherlands, to develop a

“World Wide Web for robots”, a giant database

where robots can share information about

objects, environments and tasks;

 Researchers at Singapore’s ASORO (A-Star

Social Robotics Laboratory) have built a cloud-

computing infrastructure that allows robots to

generate 3D maps of their environments much

faster than they could with their on board

computers. It allows robots to perform

simultaneous localization and mapping much

efficiently than relying on their on-board

computers;

 Google engineers developed Android-powered

robot software that allows a smartphone to

control robots based on platforms like

LegoMindStorms, iRobot Created, and Vex Pro;

 Researchers at Laboratory of Analysis and

Architecture of Systems in Toulouse, France,

have created a user manual repository for

everyday objects to help robots with

manipulation tasks like opening a door. The idea

is to develop a software framework where

objects come with a “user-manual” for the robot

to manipulate them.

 Yinong Chen et al., (2010), presented a research

done on service oriented robotics computing and

their design, implementation and evaluation of

Robot as a Service (RaaS) unit. Their work was

TowardsaUnifiedPlatformforAgent-basedCloudRobotics

sponsored by Microsoft, which demonstrates the

interest that big companies are putting in these

topics.

From the above list, RoboEarth is the initiative

that has more in common with RAC. The platform

obtained with RoboEarth allows robots connected to

the Internet to directly access the powerful

computational, storage, and communications

infrastructure of modern data centres (Google,

Facebook, Amazon, etc.) for robotics tasks and robot

learning. To create a system for robots to

communicate with each other, RoboEarth’s

researchers use a language that organizes

information into environments, objects, and actions.

The developed PaaS (Platform as a Service) solution

allows robots to perform complex functions like

mapping, navigation, or processing of human voice

commands in the cloud, at a fraction of the time

required using on-board computers.

Cloud robotics can be applied to any kind of

robots, large or small, humanoid or not. Eventually,

some of these robots could become more

standardized and sharing applications would be

easier. The “app paradigm” is one of the crucial

factors behind the success of Apple and Google.

Applications (apps) that are easy to develop, install,

and use. This is the main characteristic that

distinguishes RAC from existing approaches (like

RoboEarth): the idea of providing an intuitive and

easy to use notation for representing the desired

behaviour of robots (the TR formalism) combined

with a cloud computing infrastructure that allows to

deploy complex missions into cheap devices (the

micro-drones). This significantly will reduce the

learning curve, requiring less training and effort to

develop small-size missions (which perhaps

represent the largest portion of cases). At the same

time, the cloud serves not only as the common

infrastructure where the missions are executed, but

also provides a repository of missions where

developers can take the benefit of sharing and

reusing programs to control their own robots,

obtaining in doing so the benefits of the mentioned

app store philosophy.

4 SCENARIOS

As a demonstrator of the proposal, three scenarios

have been defined. These scenarios will allow us to

implement the final architecture shown in Figure 2

in a staggered and progressive way. The evolution of

architecture in different scenarios is as follows:

 Scenario 1: this scenario implements the

minimum part of the architecture required in

order to run a first demonstration. The

Infrastructure Layer features the local cloud

infrastructure. The Platform Layer includes the

TR interpreter and a basic interface to the

selected drones. Finally, the Application Layer

implements the TR repository and the facilities

needed for loading, saving and executing

concrete TR programs and configure drones. It

should be highlighted that in this first scenario

we only consider reusing previously defined TR

programs that use a single agent.

 Scenario 2: Continuing with the architecture

developed in Scenario 1, the Platform Layer will

provide the possibility of using external Web

services (useful for example for image

processing). In the Application Layer we will

add a graphical TR editor like Scratch in order to

create and modify TR programs, and a module

for 3D simulation. In this scenario several agents

are incorporated, obtaining a more complex and

mature case study.

 Scenario 3: Finally, in this scenario the proposed

architecture is fully implemented.

Below the scenario 1 that has already been

implemented is described.

4.1 Scenario 1 Description

For the scenario demonstration missions, a real

environment replica has been deployed in a medium

size area located in Cartagena (Spain). The mission

considered the detection and tracking of persons in

an emergency situation. Appendix A contains an

excerpt of the TR rules developed in order to

accomplish the mission requirements.

For this task, we have selected micro-drones.

Micro-drones are a particular type of UAVs of great

interest to test new technological approaches given

their low cost, the large number of application fields,

and the possibility to use the results obtained with

them in other fields. We have selected the Ar. Drone

2.0 from Parrot, which has a good quality-price

ration and includes two cameras (HD 720p frontal

and QVGA vertical) and other sensors that assure a

high stability during the flight and provides an API

that allows us reading any sensor of the quadcopter

as well as sending commands to the propellers,

among other specifications. In addition, the well-

known Robot Operating System (ROS) (Quigley et

al., 2009) is compatible with this aerial platform

thanks to the ardrone_autonomy ROS driver. ROS

runs in a Raspberry Pi B+ installed on the drone.

ICSOFT-EA2015-10thInternationalConferenceonSoftwareEngineeringandApplications

This Single Board Computer (SBC) is connected to

the Internet by using a 4G USB dongle. Therefore,

data can be stored in the cloud and evaluated by the

TR approach.

As figure 3 shows, the user carries out a passive

visual beacon that could be detected by the HD

frontal camera. This images as other sensor data are

sent to the cloud by using the C++ application

running in the SBC. This application provides

several high level commands (UP, DOWN,

FORWARD, among others) which are executed by

the implemented TR algorithm.

Figure 3: Scenario 1.

5 CONCLUSIONS

RAC aims to design, build, and validate a new

generation of cloud robotic platforms that enable

agent-based intelligent control of robots deployed in

unknown and dynamic environments. The system

will consider: (1) state-of-the-art resources to control

physical robots; (2) state-of-the-art simulation

techniques to define a 3D simulation environment

which can be used to simulate TR mission

execution; (3) state-of-the-art Artificial Intelligence

and Knowledge Representation techniques to realize

high-level robotic cognitive abilities; (4) novel

techniques for programming reactive plans and

robotic behaviours through missions and novel

mechanisms for building new behaviours from

existing ones, both for experienced and non-expert

users; (5) novel multi-layered cloud platform as the

infrastructure to maintain a continuous link between

the robots acting on a physical environment and

their agent counterparts, to provide sensor data from

robots to agents, and to provide high-level

autonomous decisions from agents to robots.

RAC seeks to develop robots with the capability

to interact with humans and operate dynamically in a

constantly changing robotic-mission environment.

As the notion of robotics often refers to a very broad

term, encompassing such diverse capabilities as low-

level perception and action, network connectivity

and decision making, the focus of RAC’s effort is to

close the innovation gap between by extending these

different technologies in the civil robotics market

and its associated stakeholders.

Among the expected benefits of the proposal, we

want to highlight that we expect RAC to

significantly improve the level of development of

service robots; to exploit the results in civil markets;

to provide a holistic robotic system development

process, facilitating validation and deployment; to

share knowledge and to harmonise the design of

software for robots; to get a novel action planning

technique to increase both flexibility and

programming easiness of robotic missions; to

improve upon the cognitive tasks of robots using

goal-oriented reactive approaches, their autonomy

and ability to react to changes in the environment; to

leverage the use of knowledge representation and

reasoning methods; and to get the resources for

enabling a culture of robot programming for

everyone, among others.

ACKNOWLEDGEMENTS

This work has been partially funded by the Spanish

Ministry of Economy and Competitiveness under the

CICYT Projects cDrone (ref.~TIN2013-45920-R)

and ViSelTR (ref.~TIN2012-39279).

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