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The RoCS Framework to Support the Development of Autonomous Robots


Abstract and Figures

With the expansion of autonomous robotics and its applications (e.g. medical, competition, military), the biggest hurdle in developing mobile robots lies in endowing them with the ability to interact with the environment and to make correct decisions so that their tasks can be executed successfully. However, as the complexity of robotic systems grows, the need to organize and modularize software for their correct functioning also becomes a challenge, making the development of software for controlling robots a complex and intricate task. In the robotics domain there is a lack of reference software architectures and, although most robot architectures available in the literature facilitate the creation process with their modularity, existing solutions do not provide development guidance on reusing existing modules. Based on the well-known IBM Autonomic Computing reference architecture (known as MAPE-K), this work defines a refined architecture following the Robotics perspective. To explore the capabilities of the proposed refinement, we implemented the RoCS (Robotics and Cognitive Systems) framework for autonomous robots. We successfully tested the framework under simulated robotics scenarios that mimic typical robotics tasks. Finally, we understand the proposed framework needs experimental evaluation as well as assessments on real-world scenarios.
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The RoCS Framework to Support the
Development of Autonomous Robots
Leonardo Ramos, Gabriel Divino, Breno Bernard Nicolau de Fran¸ca,
Leonardo Montecchi, and Esther Colombini
Universidade Estadual de Campinas
Campinas, SP, Brazil,,
Abstract. With the expansion of autonomous robotics and its applica-
tions (e.g. medical, competition, military), the biggest hurdle in develop-
ing mobile robots lies in endowing them with the ability to interact with
the environment and to make correct decisions so that their tasks can
be executed successfully. However, as the complexity of robotic systems
grows, the need to organize and modularize software for their correct
functioning also becomes a challenge, making the development of soft-
ware for controlling robots a complex and intricate task. In the robotics
domain there is a lack of reference software architectures and, although
most robot architectures available in the literature facilitate the creation
process with their modularity, existing solutions do not provide devel-
opment guidance on reusing existing modules. Based on the well-known
IBM Autonomic Computing reference architecture (known as MAPE-K),
this work defines a refined architecture following the Robotics perspec-
tive. To explore the capabilities of the proposed refinement, we imple-
mented the RoCS (Robotics and Cognitive Systems) framework for au-
tonomous robots. We successfully tested the framework under simulated
robotics scenarios that mimic typical robotics tasks. Finally, we under-
stand the proposed framework needs experimental evaluation as well as
assessments on real-world scenarios.
Keywords: Robotics, Software Architecture, Autonomous Computing.
1 Introduction
With the expansion of autonomous robotics and its applications (e.g. medical,
competition, military), the biggest hurdle in developing mobile robots lies in
endowing them with the ability to interact with the environment and to make
correct decisions so that their tasks can be executed successfully.
Typically operating in the real world — of continuous, unknown, and of-
ten unpredictable nature — it is expected that robots act through their per-
ception, reasoning, planning, and decision-making process to accomplish their
goals. With the expansion of cooperative, distributed and assistive robotics and
2 L. Ramos et al.
the widespread utilization of bipedal, aerial, and aquatic robots, other chal-
lenges were incorporated, such as multiple robot coordination, human-robot in-
teraction, and three-dimensional control and navigation. These new scenarios
demand more complex algorithms and the interaction of various AI techniques.
As the complexity of robotic systems grows, the need to organize and mod-
ularize software for their correct functioning also becomes a challenge, as in-
formation to be processed becomes distributed in space and time. The most
desirable qualities for robotics software are: modularity, portability, robustness,
and reusability for different kinds of robotics applications.
Several architectures and frameworks oriented to robotic systems are avail-
able in the literature, e.g., see [5, 10, 11, 13–16, 18, 21, 23]. Nevertheless, when
considering heterogeneous robot architectures and applications, it is still an ar-
duous and costly job to reuse existing software, either in partial or complete
form, as most are ad-hoc solutions. Furthermore, it is almost impossible to per-
form fair comparisons of specialized algorithms (e.g., navigation, vision) in a real
scenario, when a modular architecture is not available.
In this work we define RoCS, a development framework to support the current
state of autonomous robotics, targeting easier reuse and portability of modules.
The framework’s architecture instantiates the Autonomic Computing Architec-
ture defined by IBM [1], known as MAPE-K, under a robotics perspective.
The remaining sections are organized as follows. Section 2 presents the back-
ground and relevant concepts from the robotics domain. Section 3 discusses
related work. Section 4 presents our instantiation to the MAPE-K reference
architecture. Section 5 presents the RoCS Framework for autonomous robots.
Section 6 presents the framework evaluation for an usual robotics scenario. Fi-
nally, Section 7 presents the final remarks and future work.
2 Background
2.1 Service robotics
Robotics has migrated from industrial applications to service robots, where
robots help or replace humans in services [3]. In this new scenario, robots are
usually autonomous or semi-autonomous, and they have to interact with each
other and with humans in dynamic environments efficiently and securely.
The increased complexity of these new applications requires developing new
robot platforms and coordinating several modules to accomplish the tasks pro-
posed, as well as measuring the degree of success. Moreover, robots are often
very expensive and their batteries have a short autonomy, two factors that limit
the feasibility of extensive physical testing. High-fidelity simulation is commonly
applied; hence, approaches supporting a smooth transition from the simulated
environment to the real robot are mandatory.
To foster advancements in service robots, the RoboCup Federation [4] has
proposed a set of challenges for evaluating the success of developments in a
variety of domains. From playing soccer to assisting in typical tasks at home,
these autonomous robots need to coordinate a variety of elements to succeed.
The RoCS Framework to Support the Development of Autonomous Robots 3
Although RoboCup standardizes the tasks that will be addressed, how they
will be evaluated, and, in few cases, which robot platforms are allowed, the job
of defining the software and hardware components of the robots is left open. A
quick look at different domains [4] such as the playing soccer task, where robots
can vary from humanoids and wheeled, to those with a standard platform , or
the assistive robots in home tasks, show the variety of solutions that are applied
in the software domain to solve the problems.
Because teams that work in RoboCup challenges participate in various edi-
tions, and because the code is necessarily shared among groups after the compe-
tition, solutions from some teams become widely used, such as the B-Human [2]
framework. However, reuse of this framework mostly happens due to the qual-
ity of specific algorithms that it implements for solving certain problems, rather
than the flexibility and organization of the code itself. We aim instead at defining
a framework that can guide the user in structuring and reuse its code.
2.2 Robotic Programming Paradigms
The development of control paradigms for robots in dynamic environments has
been the subject of research in the field of robotics. The approaches proposed
in the literature are usually divided into three paradigms: deliberative [5],
reactive [8, 20], and hybrid [7, 9].
In the deliberative paradigm, the robot uses the available sensory information
and its knowledge of the world to reason about and create a plan. A search
is conducted on possible scenarios, to find the one that best fulfills the task.
This requires the robot to look ahead, and think about the consequences of each
action, which can take a long time. When enough time is available, this approach
allows the robot to act accordingly. However, it may not be practical if the robot
has to react quickly to environmental changes.
The reactive paradigm tightly couples sensory inputs to actuation. It allows
the robots to react almost instantaneously to environmental changes and it ex-
pects that intelligence emerges from the collective conjunction of very simple
behaviors. Typically, the information acquired by sensors is directly used for
actuation and it is not retained as internal memory. For this reason, internal
representation of the environment is limited, which prevents long-term planning.
In the hybrid paradigm, which is what most of the current architectures clas-
sify as, there is a combination of the responsiveness, robustness, and flexibility of
reactive systems with more traditional deliberative approaches where reasoning
is mandatory. The challenge in this kind of paradigm is solving conflicts between
the two different natures, and the organization of components.
3 Related Work
Several works suggest a structured approach to the control of robots.
The Robot Operating System (ROS) [18] is a set of software libraries and
tools for robot development, which provides the functionality typical of an op-
erating system for a heterogeneous cluster of robots. ROS has gained popularity
4 L. Ramos et al.
because it abstracts the hardware devices, thus being compatible with multiple
simulators and robot models. However, it provides the basic software compo-
nents of a robot without necessarily prescribing an architecture, also limited to
Unix-compatible platforms. Other framework that adopt a similar approach are
frameworks such as Player, [11], ORCA [15] and OPRos [10].
The Task Control Architecture (TCA) [23] consists of task-specific modules
connected to a central control module. The task modules perform all the required
processing and communicate with the control via messages. The control routes
these messages to their destination and maintains task control information. The
architecture defines control constructs to support both deliberative and reactive
behaviors. TCA provides a set of commonly needed mechanisms, such as task
decomposition, resource management, execution monitoring and error recovery.
Although the TCA facilitates the modular and incremental design of complex
robot systems, the centralized control can easily become the bottleneck.
To dilute the centralization problem, layered architectures were proposed
[13,21]. Three to four layers are typically used, depending on the implementation.
In practice, layers are organized differently, based on the kind of robot and kind
of task, which leads to large variety in architectures and prevents reuse.
The 4D/RCS reference architecture provides a theoretical foundation for en-
gineering software for unmanned vehicle systems [5]. The architecture consists of
a multi-layered hierarchy of computational nodes, each having the capability of
world observing, self-orientation, decision-making, and autonomous action. This
decision cycle is mostly known as the OODA-loop: observe, orient, decide and
act. It is realized by five elements for each node: sensory processing,world model-
ing,value judgment,behavior generation, and knowledge database. The behavior
generation module of a node is connected to those of the adjacent nodes, creating
a command tree. Furthermore, each robot is partitioned into subsystems, that in
turn are partitioned in primitives and then in servo computational nodes. This
granularity, while feasible in specific scenarios, poses a problem when the robot’s
components are not unambiguously separable.
4 Framework Architecture
The lack of reference software architectures for autonomous robots led us to
base our work on the more general Autonomic Computing reference architecture,
known as MAPE-K [1], which is summarized in Section 4.1. Then, Section 4.2
discusses the details of our architecture and the choices that we made in its
definition. Finally, Section 4.3 discusses the hybrid robotics paradigm.
4.1 The MAPE-K Reference Architecture
Autonomic, or self-adaptive, systems are intended to continuously adjust its op-
eration in response to changes perceived in themselves or the environment, with
minimal outside intervention. To do this, in MAPE-K, systems are composed by
autonomic managers and the associated managed resources (Figure 1).
The RoCS Framework to Support the Development of Autonomous Robots 5
Fig. 1: Autonomic Manager in the MAPE-K Architecture (adapted from [1]).
According to the MAPE-K architecture [1], the autonomic manager should
be composed of five basic building blocks (Figure 1): Monitor,Analyze,Plan,Ex-
ecute, and Knowledge. Besides them, the Sensor and Actuator (Effector) touch-
points work as supporting components for sensing (data collection) and acting
upon the managed resources, respectively.
This reference architecture was designed to deal mostly with IT systems,
like business information systems, distributed services and web applications.
Later, MAPE-K extensions for adaptive systems such as FORMS [24] and Ac-
tivFORMS [12,17] have been proposed. However, although the authors of these
works use robotics scenarios, their proposal advocates an extension that allows
the agent to adapt itself to a changing environment, rather than addressing the
typical problems of autonomous service robotics presented in Section 1. Accord-
ingly, there is a gap when applying MAPE-K concepts to robotic systems.
4.2 Detailed RoCS Architecture
In this section, we present the instantiation from the MAPE-K reference archi-
tecture (Section 4.1) building blocks considering the robotic perspective.
The Monitor block (Figure 2a) gathers and interprets raw data incoming
from Sensors. One or more SensorDriver interact directly with physical sensing
devices through the monitor port. The RawDataInterpreter translates raw
data into final values, e.g., voltage from a temperature sensor into the actual
temperature value. Finally, the MonitorPublisher module is the one responsible
for publishing the interpreted data through the publish port.
Observed data from Sensors can be of diverse types, structures, and dynam-
ics. The sensors can be either real or simulated, and their communication proto-
col and data nature (e.g. analogical vs. digital) may vary enormously. Regardless
of sensors characteristics, a Monitor block must rapidly organize, interpret and
forward the data for being analyzed.
The Analyze block (Figure 2b) filters, processes, and aggregates the data
received from the Monitor block to determine if a change in the world has hap-
pened. It is where complex algorithms like time-series forecasting, queuing mod-
els, and advanced filtering can be executed to analyze the monitored data.
6 L. Ramos et al.
: RawDataInterpreter [1]
: MonitorPublisher [1]
: SensorDriver [1..*]
monitor [1]
publish [1]
: MonitorSubscriber [1]
: DataProcessor [1]
: DataAggregator [1]
: AnalyzePublisher [1]
subscribe [1]
publish [1]
: AnalyzeSubscriber [1]
: Planner [1]
: BehaviorPipeline [1]
subscribe [1]
consumeAction [1]
: PipelineIterator [1]
: ActionDecomposer [1]
: AtomicActionExecutor [1]
: ActuatorDriver [1..*]
execute [1]
act [1]
Fig. 2: Representation of the internal structures of the Monitor (a), Analyze (b),
Plan (c) and Execute (d) components.
The MonitorSubscriber block receives data from the subscribe port. The
DataProcessor module runs data processing algorithms to search for alterations
with respect to the known world state. The DataAggregator module translates
the results in application-level concepts, possibly fusing the results of multiple
data processing algorithms. Finally, if a relevant change in the world is per-
ceived, the AnalyzePublisher sends this information to the next block, which
can elaborate a response accordingly.
The Plan block (Figure 2c) creates, selects, and forwards behaviors to be
executed by the Execute block. Specific changes trigger the generation of new
behavior. The realization of this block may range from simple Finite State Au-
tomatas to complex Cognitive Systems fine-tuned for specific tasks.
First, the block receives changes detected by the Analyze block via its
subscribe port, which is delegated to the AnalyzeSubscriber module. Their
received change is sent to the Planner module, which is the module that decides
which behaviors the robot should execute based on the tasks at hand and oc-
curred changes. Lastly, this new behavior is sent to a pipeline (BehaviorPipeline)
that will be read by the Execute block.
In the Execute block (Figure 2d), a planned behavior gets transformed into
real robot actions like head movement or team communication. Once the next
behavior has been decided, multiple actions might be needed to actually change
the world state and realize the intended behavior.
The Plan block sends the desired behavior in the pipeline, which the Execute
block has to iterate over. This is done by the PipelineIterator module, which
is directly connected to the execute port. Whenever a behavior is received, it
has to be decomposed in a sequence of atomic actions (ActionDecomposer).
Then, each of these actions is executed by the AtomicActionExecutor, which
is in charge of sending the correct message to the specific actuator drivers. One
or more ActuatorDriver modules then directly communicate with the physical
actuators. This is repeated until no atomic actions are left, after which the next
behavior is extracted from the pipeline. This block is also responsible for any
outgoing communication to other robots, via TCP, UDP, or other protocols.
The RoCS Framework to Support the Development of Autonomous Robots 7
AKnowledge source is a grouping of data structures designed with the sole
purpose of providing access to the world information to all the building blocks.
Information in the Knowledge block is organized in four structures.
The WorldModel represents any data that the robot can perceive from the
world, such as the temperature, a map of the robot’s surroundings, or the po-
sition of other robots. The RobotModel deals with information about the robot
structure and internal configuration, such as the parameters of its actuators,
kinematics and inverse kinematics. The StrategyModel is a mapping of pre-
defined strategies (if any) from which the robot can choose for executing its
tasks and behaviors, e.g., the “defender” or “attacker” roles in case of a football
competition. Finally, the ReactiveModel is responsible for storing the rules that
trigger a reactive action by the Execute block.
In robot swarms or other cooperative scenarios, agents would share this
Knowledge source, or part of it, among all of them. Each robot would be re-
sponsible for sending any new acquired world information to the other robots it
knows, until all robots share a common database.
4.3 Design for the Hybrid Paradigm
For the architecture to be consistent with robotics practices, it has to obey
one of the Robotics Programming Paradigms (Section 2.2). We designed our
architecture to support the hybrid paradigm, as it represents the current state
of the art, and it is the most flexible solution. In the following, we discuss how
our architecture supports both the deliberative and the reactive routes.
Deliberative Route. To implement a deliberative system, an agent has
to go through the following phases: sensory input acquisition, task generation
and behavior filtering, action selection, and action execution. In the proposed
architecture, the first step is performed by the Monitor and the Analyze blocks.
Meanwhile, the Plan block is responsible for generating the task plan, filtering
the robot’s behavior, and selecting the actions to be performed. Finally, the
Execute block executes them. Therefore, the full MAPE-K classical cycle (solid
arrows in Figure 1) works as a Deliberative System.
Reactive Route. A reactive robot system tightly couples perception to
action without the use of intervening abstract representations or history [6]. The
selection of the reaction to the sensory input is done in an inhibitory way, in
which a hierarchy of behaviors is defined and then enacted by actuators.
Such mechanism can be defined in our architecture, although a different
abstraction is needed. There are two ways of implementing a reactive system
within the the MAPE-K architecture: i) implementing a high-priority reactive
layer in the Plan block, or ii) capturing a low-level view of the world in the
Knowledge, based on which a reactive action can be triggered immediately.
Biologically, a reactive action, like the reflex arc, is processed by a neural
pathway that can act on an impulse without the assistance of the brain. That
is, the response to specific stimuli does not need conscious thought. From the
computational point of view, the deliberative planning and the reactive behavior
will run independently, at different frequencies, and based on different data.
8 L. Ramos et al.
Therefore, the first abstraction (high-priority module in the Plan) could not
be interpreted as biologically correct, as the sensory input reaches the Plan block,
which can be thought as the robot’s conscious brain. The second abstraction,
instead, considers the Knowledge Source as that neural pathway, connecting the
receptors (the Monitor block), to the effectors (the Execute block). The Execute
block thus triggers one of the many pre-defined reflexes once a specific change
in the Knowledge Source occurs (dashed arrows in Figure 1).
5 The RoCS Framework
Based on the software architecture described in Section 4, we designed and im-
plemented the RoCS Framework, a concrete framework to guide robot developers
in structuring their code. The framework has been implemented in C++, which
is one of the most popular languages in the robotics domain, and its source
code is available on the GitHub platform [19]. The structure of the framework
is shown in the Class Diagram in Figure 3 and is described in the following.
5.1 Framework Structure
The core of the framework consists of one class for each of the five main blocks
of the architecture: Monitor,Analyze,Plan,Execute, and Knowledge, depicted
with their respective colors in Figure 3, as in Section 4.
While there may exist, in general, multiple independent instances of the Mon-
itor and Analyze blocks, this is not the case for Plan, Execute, and Knowledge,
and for this reason they apply the Singleton pattern. It is reasonable to constrain
the framework user to have i) a single consistent knowledge base (Knowledge),
ii) a single place where robot reasoning is performed, to avoid the production
of conflicting plans (Plan), and iii) a single engine responsible for executing the
planned actions (Execute).
+next(): Action
1..* StrategyModel
Fig. 3: Class Diagram of the RoCS Framework.
The RoCS Framework to Support the Development of Autonomous Robots 9
The Monitor, Analyze, and Plan classes communicate using a publish/subscribe
messaging pattern. As such, each of them extends the Publisher and/or Observer
abstract classes, which realize communication channel. However, it should be
noted that the attach operation, which is needed to register an observer in
the list of recipients of a publisher, is not present in the Publisher class itself.
Instead, the more specific attach(Analyze) and attach(Plan) methods are
added to the Monitor and Analyze classes, respectively. This choice constrains
the framework user to follow the proposed architecture, preventing him from con-
necting blocks in an arbitrary way. Otherwise, with a generic attach(Observer)
it would be possible to make the Plan observe the Analyze, or even an Analyze
to observe itself.
Communication between the Plan and the Execute occurs through a queue,
implemented by the Pipeline class. This queue contains a list of actions that are
meant to be executed, as objects of type Action. Because the possible actions
depend on the kind of robot and scenario at hand, the Action class is abstract
and is meant to be extended by users of the framework. The queue is actually a
priority queue, to allow the reactive behavior to override the decisions taken by
the Plan block (deliberative behavior), by submitting actions of a higher priority.
The Execute class owns the Pipeline, which is also a Singleton.
The Knowledge class maintains a knowledge base that is shared among all the
other blocks. It is composed of four main parts. The WorldModel class contains a
representation of the world as known to the robot. It is also a Singleton, to avoid
inconsistencies. We just provide an abstract class to be extended by the user,
since this is application-specific. The StrategyModel class is meant to contain
pre-defined strategies, if they exist, to be accessed by the Plan block. Its usage
is optional and also widely dependent from the target application. As such, we
provide an abstract class to be extended by the user.
The RobotModel class contains a description of the physical structure and
status of the robot, in terms of its position (coordinates), orientation, as well as
the available sensors and actuators. The information about available sensors and
actuators is maintained by aggregation with objects of type Sensor or Actuator.
These two classes are also abstract.
The Sensor class has a getData(Value) method, which is used to read the
data from the sensor. Sensor is a C++ template class, parameterized with the
Value type, in order to support sensors returning different kinds of data. Imple-
mentation of the getData method is however specific for each kind of sensor. A
Monitor block may monitor the value of one or more sensors (e.g., a group of
sensors of the same type), as represented by the association between the Monitor
and Sensor classes. In general, the opposite is also true: the value of a sensor
can be used for multiple different purposes requiring different processing paths.
The ReactiveModel class represents a portion of reactive behavior, that is, a
known deterministic mapping between input observed at sensor and actions to be
executed by the robot. In general, the Knowledge may contain multiple instances
of ReactiveModel, each one addressing a specific aspect of the robot’s behavior,
e.g., a reactive model to avoid falling, and another one to avoid obstacles. The
10 L. Ramos et al.
ReactiveModel class accesses sensors via the Knowledge class and, when specific
thresholds are exceeded, it issues actions to the Pipeline.
5.2 Control Flow
In the initialization phase, the setup of the robot configuration is performed. This
involves creating instances of the required blocks, that is, the Knowledge (and
consequently its parts), the Execute, the Plan, and a certain number of Sensor,
Actuator,Analyze, and Plan instances, according to the physical structure of
the robot and the target application.
Then, the required associations are established, which means: i) associating
each Monitor with the Sensors it will monitor, ii) subscribing each Analyze to
the publishing channel of one or more Monitor, and subscribing the Plan to the
publishing channel of one or more Analyze.
The execution is multi-threaded, that is, there is a thread running for each
instance of the main blocks of the architecture: Monitor,Analyze,Plan, and
Execute. All of these threads may access the Knowledge to read or write informa-
tion from/to the knowledge base, typically the WorldModel or the RobotModel.
A separate thread is also running for each existing ReactiveBehavior.
AMonitor thread cyclically runs the interpret abstract method, in which
it is supposed to read the sensors to which it is connected, and return the value
to be published to its observers. The implementation of this function is of course
dependent of the application and should be implemented by the user. The pub-
lishing part is instead handled by the framework, transparently to the user.
An Analyze thread cyclically runs the process abstract method. In this
method the data values received from the Monitor instances should be merged
and processed to return a high-level information, which is then published towards
the Analyze. Also in this case the publishing part is handled by the framework.
The Plan thread cyclically runs the run abstract method, which is meant to
be implemented by the user of the framework. In this method the actual planning
is performed, based on data received from the various Analyze instances. The
results of planning is one or more Action objects, which are enqueued to the
Pipeline. An Action can be atomic, when its behavior is entirely defined in its
class, or can be composed of a sequence of other simpler actions (macro-action).
The Execute thread cyclically runs the run method. In this case the concrete
implementation of this method is provided by the framework, and Execute is a
concrete class. The behavior is simple: at each iteration it gets the next Action in
the pipeline and executes it, by calling the act method. This method is abstract
and its implementation depends on the concrete kind of action. A macro-action
would typically call the act operation on its children according to a specific
sequence, possibly complementing it with additional behavior.
Finally, threads corresponding to ReactiveModel instances also run period-
ically, executing the run operation. At each iteration the reactive model reads
the value of the sensors of its interest (e.g., a gyroscope to avoid falling) and,
in case specific conditions are met, it issues a new high-priority Action that is
added to the Pipeline. Such conditions are again application-dependent.
The RoCS Framework to Support the Development of Autonomous Robots 11
5.3 Extension Points
The user of the framework may extend the provided classes to implement the be-
havior of a specific robot. We consider four kinds of extension points: mandatory,
deliberative,reactive, and optional.
Mandatory. The user must extend the Sensor and Actuator abstract classes,
implementing concrete classes based on the specific scenario. It must provide an
implementation of the getData method in Sensor, and a method to control the
Actuator. The user must also extend the Action abstract class with concrete
classes, based on the kinds of actions that are possible in the considered scenario,
and provide an implementation of the act method for each of them.
Deliberative. These are extension points that must be used when the robot
should implement a deliberative behavior. In this case, the user must extend the
Monitor and Analyze classes and implement the intepret and process meth-
ods, respectively. The user may create a hierarchy of classes, in case different
data should be monitored or analyzed separately. To implement deliberative be-
havior, the user must also extend the Plan class and implement the run method.
Reactive. These are extension points that must be used when the robot
should implement a reactive behavior. In this case, the user must extend the
ReactiveModel class and implement the run method. The user may create a
hierarchy of classes extending ReactiveModel, in case different of reactive be-
haviors should be executing concurrently.
Optional. If needed: i) the RobotModel class can be extended to contain
further properties that reflect the current status of the robot; ii) the WorldModel
class can be extended to contain properties that reflect the current status of the
world as known to the robot; and iii) the StrategyModel abstract class can be
extended to contain predefined strategies to be accessed by the Plan. Whether
these extensions are needed or not depends on the application.
6 Evaluation
To assess the application of the framework in a typical robotics task, we de-
ployed a Pioneer P3DX robot in the V-REP [22] simulator running the frame-
work. The robot has the following configuration. As sensors, it uses 16 range
sensors (sonars) distributed around the robot circumference, 1 position and 1
orientation sensor that act as entities that are aware of the real position (x, y, z)
and orientation (θ) of the robot. This special kind of sensors are provided by
the simulator; in a real deployment they would be replaced by a localization
algorithm running in the Analyze. As actuators, it uses two rotate motors re-
sponsible for driving the differential robot in the scene. In terms of actions, we
consider two atomic actions and three macro-actions (see Table 1).
To evaluate the capability of the framework of supporting a hybrid approach,
we defined a task where both deliberative and reactive routes can be activated.
The robot task is to reach a specific position in the environment (origin 0,0) while
avoiding collisions. It is important to mention that the robot is not aware of the
12 L. Ramos et al.
Table 1: Actions included in the experiment and their decomposition.
Action Decomposition
SetWheelSpeed atomic
TurnAngle atomic
AvoidCollision TurnAngle;SetWheeelSpeed
GoToOrigin TurnToPosition;SetWheeelSpeed
TurnToOrigin TurnAngle
existence of obstacles while it computes its plan to reach the goal. Therefore, the
“avoid the obstacle” behavior will be executed in a reactive fashion. At the same
time, another robot with a simple ’explore the world’ behavior (not implemented
using the framework) is running in the environment to add more complexity to
the scene.
Figure 4a presents the simulated environment. In this scene, the robot starts
in the top of the environment, close to the walls and the box, and it should
reach the origin of the system, defined as a point with coordinates (0,0). Figure 4
depicts the resulting trajectory performed by the robot and the macro-actions
enqueued in the Pipeline. Each small dot represents an action, while the squares
indicate a change in the executing paradigm (deliberative or reactive). As the
pipeline is cleared once the reactive action route is active, the robot has to
re-plan based on the information on its new position.
6.1 Implementation of Extension Points
To accomplish the task described in Section 6, the following extension points (see
Section 5.3) were implemented according to the Diagram presented in Figure 5.
Each sensor described earlier was implemented through the extension of ei-
ther Sensor or one of its heirs, like Range. The same applies to WheelVREP,
which implements the motion of robot wheels and is the concrete implementation
of an Actuator in our scenario. Three Monitors were created, one for watch-
-0.5 0 0.5 1 1.5 2 2.5
2.5 Robot trajectory and behavior change
Deliberative route
Reactive route
Fig. 4: Simulated environment for the experiment (a) with two robots deployed.
The robot at the bottom is the secondary robot that is not running the frame-
work. (b) the resulting trajectory with the corresponding active paradigm for
the robot that implements the framework. A video with the resulting simulation
can be found at:
The RoCS Framework to Support the Development of Autonomous Robots 13
RangeVREPMonitor PositionVREPMonitor OrientationVREPMonitor
AvoidObstacleGoToOrigin TurnToOriginTurnAngle
WheelWheelVREP Actuator
PionerP3DXModel RobotModel
Fig. 5: Implementation using the RoCS framework for the proposed scenario.
Associations that can be inferred from Figure 3 have been omitted for simplicity.
ing each type of Sensor, and three correspondly Analyzers: PassOrientation,
PassPosition and PassRange.
The GoToOriginPlanner is responsible for defining which actions should
be sent to the queue, whereas AvoidObstacle,GoToOrigin,SetWheelSpeed,
TurnAngle and TurnToOrigin represent the actual actions that will be per-
formed and transformed into actuators commands. Finally, AvoidObstacleModel
and PioneerP3DXModel are the extensions of the ReactiveModel and of the
RobotModel, respectively.
It is important to remember that one of the requirement of the framework is
to facilitate the transfer from the simulated robot to the real one. In this case,
we would only have to change the concrete Sensor and Actuator classes, which
facilitates enormously such work.
7 Final Remarks
We presented an instantiation to the MAPE-K reference architecture towards the
Robotics perspective, mainly for service robots applications with heterogeneous
physical platforms. From this, we developed the RoCS framework to support the
development of autonomous robots under a known architecture. We understand
this approach can assist students or novices in robot development, and help
experienced developers focus on their specific problems like machine learning
algorithms for computer vision, sensor fusion techniques, and locomotion for
robot models using particular physical devices (e.g., wheels, legs, propulsion).
Initially, the RoCS framework was evaluated in a simple but usual scenario for
service robots as proof of concept. For future work, we envision the development
of robots using the RoCS framework for competitions such as the RoboCup
Humanoid Soccer Teen Size League and RoboCup Flying Robots Competition.
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... The framework's architecture instantiates the Autonomic Computing Architecture defined by IBM (2005), known as MAPE-K, under a robotics perspective. This paper is a revised, extended version of Ramos et al. (2019b). The content has been modified in different ways with respect to the initial version. ...
... Initially Ramos et al. (2019b), the RoCS Framework was evaluated in a simple but usual scenario for service robots as proof of concept. In this work, we performed two additional experiments exploring the reuse capability of the framework by changing robot models and their application tasks. ...
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With the expansion of autonomous robotics and its applications (e.g. medical, competition, military), the biggest hurdle in developing mobile robots lies in endowing them with the ability to interact with the environment and to make correct decisions so that their tasks can be executed successfully. However, as the complexity of robotic systems grows, the need to organize and modularize software for their correct functioning also becomes a challenge, making the development of software for controlling robots a complex and intricate task. In the robotics domain, there is a lack of reference software architectures and, although most robot architectures available in the literature facilitate the creation process with their modularity, existing solutions do not provide development guidance on reusing existing modules. Based on the well- known IBM Autonomic Computing reference architecture (known as MAPE-K), this work defines a refined architecture following the Robotics perspective. To explore the capabilities of the proposed refinement, we implemented the RoCS (Robotics and Cognitive Systems) framework for autonomous robots. We successfully tested the framework under simulated robotics scenarios that mimic typical robotics tasks and evidence the framework reuse capability. Finally, we understand the proposed framework needs further experimental evaluation, particularly, assessments on real-world scenarios.
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