Search

Autonomous robots operating in everyday environments, such as hospitals, private houses, and public roads, are context-aware self-adaptive systems, i.e. they exploit knowledge about their resources and the environment to trigger runtime adaptation, so that they exhibit a behavior adequate to the current context. For these systems, context-aware self-adaptation requires to design the robot control application as a dynamically reconfigurable software architecture and to specify the adaptation logic for reconfiguring its variable aspects (e.g. the modules that implement various obstacle detection algorithms or control different distance sensors) according to specific criteria (e.g. enhancing robustness against variable illumination conditions). Despite self-adaptation is an intrinsic capability of autonomous robots, ad-hoc approaches are used in practice to design reconfigurable robot architectures. In order to enhance system maintainability, the control logic and the adaptation logic should be loosely coupled. For this purpose, the adaptation logic should be defined against an explicit representation of software variability in the robot control architecture. In this paper we propose a modeling approach, which consists in explicitly representing robot software variability with the MARTE::ARM-Variability metamodel, which has been designed as an extension of the UML MARTE profile. We evaluate the applicability of the proposed approach by exemplifying the software architecture design of a robot navigation framework and by analyzing the support provided by the ROS infrastructure for runtime reconfiguration of its variable aspects. • Dynamic reconfiguration of robotic systems is an emerging topic in robotics. • Maintainability requires to separate the application logic and the adaptation logic. • We propose a UML MARTE profile for modeling software variability in robotics. • We exemplify the proposed approach with a navigation case study. • We analyze the support to variability management of ROS and ROS2. [ABSTRACT FROM AUTHOR]

This paper concerns the systematic testing of robotic control software based on state-based models. We focus on cyclic systems that typically receive inputs (values from sensors), perform computations, produce outputs (sent to actuators) and possibly change state. We provide a testing theory for such cyclic systems where time can be represented and probabilities are used to quantify non-deterministic choices, making it possible to model probabilistic algorithms. In addition, refusals , the inability of a system to perform a set of actions, are taken into account. We consider several possible testing scenarios. For example, a tester might only be able to passively observe a sequence of events and so cannot check probabilities, while in another scenario a tester might be able to repeatedly apply a test case and so estimate the probabilities of sequences of events. These different testing scenarios lead to a range of implementation relations (notions of correctness). As a consequence, this paper provides formal definitions of implementation relations that can form the basis of sound automated testing in a range of testing scenarios. We also validate the implementation relations by showing how observers can be used to provide an alternative but equivalent characterisation. • Systematic testing of robotic control software based on state-based models. • Motivated by the RoboChart and RoboSim notations for modelling robotic software. • Testing theory for cyclic systems with discrete time, probabilities and refusals. • Defining 15 implementation relations and analysing the relations among them. • Alternative characterisations of these implementation relations using observers. [ABSTRACT FROM AUTHOR]

Validation and Verification (V&V) of autonomous robotic system software is becoming a critical issue. Among the V&V techniques at our disposal, formal approaches are among the most rigorous and trustworthy ones. Yet, the level of skills and knowledge required to use and deploy formal methods is usually quite high and rare. In this paper, we describe an approach that starts from a regular, but rigorous, framework to specify and deploy robotic software components, which can also automatically synthesize a formal model of these components. We describe how we can execute the resulting formal model, in place of a traditional implementation, and show how this provides the opportunity to add powerful monitoring and runtime verification capabilities to a system, e.g., to prevent collisions, or trigger an emergency landing. Since the runtime used to execute formal models is specifically designed to be faithful to their semantics, every execution (in the implementation) can be mapped to a trace in the specification. As a result, we can also prove many interesting properties offline, using model-checking techniques. We give several examples, such as properties about schedulability, worst-case traversal time, or mutual exclusion. We believe that having a consistent workflow, from an initial specification of our system, down to a formal, executable specification is a major advance in robotics and opens the way for verification of functional components of autonomous robots and beyond. We illustrate this claim by describing a complete example based on a genuine drone flight controller. • Programming and verifying the functional components of robotic systems. • Functional components are fully specified with a DSL by robot programmers. • Components implementation automatically synthesized from high-level specifications. • Formal model automatically synthesized from the same specifications. • Formal model is consistent with the semantics of the component implementation. • The synthesized model is used offline for verification with model-checking. • The formal model is used online for runtime verification and properties enforcement. [ABSTRACT FROM AUTHOR]

Abstract: This article presents a newly developed knowledge methodology/model that was designed to support the IEEE Robotics and Automation Society’s Ontologies for Robotics and Automation Working Group. This methodology/model allows for the creation of systems that demonstrate flexibility, agility, and the ability to be rapidly re-tasked. The methodology/model will be illustrated through a case study in the area of robotic kit building. Through this case study, the knowledge model will be presented, and automatic tools for optimizing the knowledge representation for planning systems and execution systems will be discussed. [Copyright &y& Elsevier]

Abstract: Robotics is an area of research in which the paradigm of Multi-Agent Systems (MAS) can prove to be highly useful. Multi-Agent Systems come in the form of cooperative robots in a team, sensor networks based on mobile robots, and robots in Intelligent Environments, to name but a few. However, the development of Multi-Agent Robotic Systems (MARS) still presents major challenges. Over the past decade, a high number of Robotics Software Frameworks (RSFs) have appeared which propose some solutions to the most recurrent problems in robotics. Some of these frameworks, such as ROS, YARP, OROCOS, ORCA, Open-RTM, and Open-RDK, possess certain characteristics and provide the basic infrastructure necessary for the development of MARS. The contribution of this work is the identification of such characteristics as well as the analysis of these frameworks in comparison with the general-purpose Multi-Agent System Frameworks (MASFs), such as JADE and Mobile-C. [Copyright &y& Elsevier]

Presents a study that demonstrates the viability and utility of robotic visual servoing (RVS) in less than optimal hardware environment. Components of the Intelligent Soft Arm Control (ISAC) service robot's software system; Three principal realms of the RVS problem; Servo-control loop; Image/robot jacobian; Visual servoing algorithm; Arm position set-point updating.