Category Archives: Robotic Architectures

Designing robotic architectures by coordinating different modules in a data-flow graphical paradigm

Sebastian Buck, Andreas Zell, CS::APEX: A Framework for Algorithm Prototyping and Experimentation with Robotic Systems. Modeling Perception and High Level Robot Control with Activity Flow Graphs, Journal of Intelligent & Robotic Systems (2019) 94:371–387, DOI: 10.1007/s10846-018-0831-7.

Robotic systems differ drastically in their sensory capabilities, their computational power and their designated tasks. For
efficient algorithm development, however, we need to have a common modeling framework that enables us to generalize and
re-use existing solutions. A modular approach, which is coherent across different platforms, also allows faster prototyping
of new systems, given that existing functionality can be reused from already implemented modules. In this paper we develop
a modeling framework based on data flow graphs that achieves the following goal: We first merge synchronous data flow
and reactive programming into hybrid flow graphs, where we explicitly model synchronous and asynchronous data flow.
Then we transfer concepts from finite-state machines to achieve a coherent framework which we call Activity Flow Graphs.
The flow of activity enables us to model high level states directly in the data flow graph. The result is a single computation
graph that can express both perception and high level control aspects of any robotic system. This theoretical foundation is
the core of our open-source software framework CS::APEX, which allows the creation, manipulation and evaluation of
Activity Flow Graphs and enables rapid prototyping and experimentation and can be used with any robot supporting the
Robot Operating System (ROS). We then demonstrate the framework with two high level models for a fetch-and-delivery
robot and a person following robot.

On the need of integrating emotions in robotic architectures

Luiz Pessoa, Do Intelligent Robots Need Emotion?,Trends in Cognitive Sciences, Volume 21, Issue 11, 2017, Pages 817-819, DOI: 10.1016/j.tics.2017.06.010.

What is the place of emotion in intelligent robots? Researchers have advocated the inclusion of some emotion-related components in the information-processing architecture of autonomous agents. It is argued here that emotion needs to be merged with all aspects of the architecture: cognitive–emotional integration should be a key design principle.

A new robotic middleware that exposes “resources” to the network instead of functionality

Marcus V. D. VelosoJosé Tarcísio C. FilhoGuilherme A. Barreto, SOM4R: a Middleware for Robotic Applications Based on the Resource-Oriented Architecture, Journal of Intelligent & Robotic Systems, Volume 87, Issue 3–4, pp 487–506, DOI: 10.1007/s10846-017-0504-y.

This paper relies on the resource-oriented architecture (ROA) to propose a middleware that shares resources (sensors, actuators and services) of one or more robots through the TCP/IP network, providing greater efficiency in the development of software applications for robotics. The proposed middleware consists of a set of web services that provides access to representational state of resources through simple and high-level interfaces to implement a software architecture for autonomous robots. The benefits of the proposed approach are manifold: i) full abstraction of complexity and heterogeneity of robotic devices through web services and uniform interfaces, ii) scalability and independence of the operating system and programming language, iii) secure control of resources for local or remote applications through the TCP/IP network, iv) the adoption of the Resource Description Framework (RDF), XML language and HTTP protocol, and v) dynamic configuration of the connections between services at runtime. The middleware was developed using the Linux operating system (Ubuntu), with some applications built as proofs of concept for the Android operating system. The architecture specification and the open source implementation of the proposed middleware are detailed in this article, as well as applications for robot remote control via wireless networks, voice command functionality, and obstacle detection and avoidance.

How “behaviour trees” generalize the subsumption architecture and some other control architecture frameworks

M. Colledanchise and P. Ögren, “How Behavior Trees Modularize Hybrid Control Systems and Generalize Sequential Behavior Compositions, the Subsumption Architecture, and Decision Trees,” in IEEE Transactions on Robotics, vol. 33, no. 2, pp. 372-389, April 2017.DOI: 10.1109/TRO.2016.2633567.

Behavior trees (BTs) are a way of organizing the switching structure of a hybrid dynamical system (HDS), which was originally introduced in the computer game programming community. In this paper, we analyze how the BT representation increases the modularity of an HDS and how key system properties are preserved over compositions of such systems, in terms of combining two BTs into a larger one. We also show how BTs can be seen as a generalization of sequential behavior compositions, the subsumption architecture, and decisions trees. These three tools are powerful but quite different, and the fact that they are unified in a natural way in BTs might be a reason for their popularity in the gaming community. We conclude the paper by giving a set of examples illustrating how the proposed analysis tools can be applied to robot control BTs.

Model checking for the verification of the correct functionality in the presence of sensor failures of a network of behaviours included in a robotic architecture

Lisa Kiekbusch, Christopher Armbrust, Karsten Berns, Formal verification of behaviour networks including sensor failures, Robotics and Autonomous Systems, Volume 74, Part B, December 2015, Pages 331-339, ISSN 0921-8890, DOI: 10.1016/j.robot.2015.08.002.

The paper deals with the problem of verifying behaviour-based control systems. Although failures in sensor hardware and software can have strong influences on the robot’s operation, they are often neglected in the verification process. Instead, perfect sensing is assumed. Therefore, this paper provides an approach for modelling the sensor chain in a formal way and connecting it to the formal model of the control system. The resulting model can be verified using model checking techniques, which is shown on the examples of the control systems of an autonomous indoor robot and an autonomous off-road robot.