A Software Suite for the Control and the Monitoring of Adaptive Robotic Ecologies

Adaptive robotic ecologies are networks of heterogeneous robotic devices (sensors, actuators, automated appliances) pervasively embedded in everyday environments, where they learn to cooperate towards the achievement of complex tasks. While their flexibility makes them an increasingly popular way to improve a system’s reliability, scalability, robustness and autonomy, their effective realisation demands integrated control and software solutions for the specification, integration and management of their highly heterogeneous and computational constrained components. In this extended abstract we briefly illustrate the characteristic requirements dictated by robotic ecologies, discuss our experience in developing adaptive robotic ecologies, and provide an overview of the specific solutions developed as part of the EU FP7 RUBICON Project

Model-based resource analysis and synthesis of service-oriented automotive software architectures

Context Automotive software architectures describe distributed functionality by an interaction of software components. One drawback of today\u27s architectures is their strong integration into the onboard communication network based on predefined dependencies at design time. The idea is to reduce this rigid integration and technological dependencies. To this end, service-oriented architecture offers a suitable methodology since network communication is dynamically established at run-time. Aim We target to provide a methodology for analysing hardware resources and synthesising automotive service-oriented architectures based on platform-independent service models. Subsequently, we focus on transforming these models into a platform-specific architecture realisation process following AUTOSAR Adaptive. Approach For the platform-independent part, we apply the concepts of design space exploration and simulation to analyse and synthesise deployment configurations, i. e., mapping services to hardware resources at an early development stage. We refine these configurations to AUTOSAR Adaptive software architecture models representing the necessary input for a subsequent implementation process for the platform-specific part. Result We present deployment configurations that are optimal for the usage of a given set of computing resources currently under consideration for our next generation of E/E architecture. We also provide simulation results that demonstrate the ability of these configurations to meet the run time requirements. Both results helped us to decide whether a particular configuration can be implemented. As a possible software toolchain for this purpose, we finally provide a prototype. Conclusion The use of models and their analysis are proper means to get there, but the quality and speed of development must also be considered

Whither PQL?

Generalized linear mixed models (GLMM) are generalized linear models with normally distributed random effects in the linear predictor. Penalized quasi-likelihood (PQL), an approximate method of inference in GLMMs, involves repeated fitting of linear mixed models with “working” dependent variables and iterative weights that depend on parameter estimates from the previous cycle of iteration. The generality of PQL, and its implementation in commercially available software, has encouraged the application of GLMMs in many scientific fields. Caution is needed, however, since PQL may sometimes yield badly biased estimates of variance components, especially with binary outcomes. Recent developments in numerical integration, including adaptive Gaussian quadrature, higher order Laplace expansions, stochastic integration and Markov chain Monte Carlo (MCMC) algorithms, provide attractive alternatives to PQL for approximate likelihood inference in GLMMs. Analyses of some well known datasets, and simulations based on these analyses, suggest that PQL still performs remarkably well in comparison with more elaborate procedures in many practical situations. Adaptive Gaussian quadrature is a viable alternative for nested designs where the numerical integration is limited to a small number of dimensions. Higher order Laplace approximations hold the promise of accurate inference more generally. MCMC is likely the method of choice for the most complex problems that involve high dimensional integrals

A Structured Approach for Modular Design: A Plug and Play Middleware for Sensory Modules, Actuation Platforms, Task Descriptions and Implementations in Robotics and Automation Environments

In this paper, the RISCWare framework is developed and described. RISCWare is a robotic middleware used for the integration of heterogeneous robotic components. RISCWare consists of three modules. The first module is the sensory module which represents sensors that collect information about the remote or local environment. The platform module defines the robotic platforms and actuation methods. The last module is the task-description module, which defines the tasks and applications that the platforms will perform; such as teleoperation, navigation, obstacle avoidance, manipulation, 3-D reconstruction, and map building. The Plug-and-Play approach is one of the key features of RISCWare, which allows auto-detection and auto-reconfiguration of the attached standardized components (hardware and software) according to current system configurations. These components can be dynamically available or unavailable. Dynamic reconfiguration provides the facility to modify a system during its execution, and can be used to apply patches and updates, to implement adaptive systems, or to support third-party modules. This automatic detection and reconfiguration of devices and driver software makes it easier and more efficient for the end users to add and use new devices and software applications. Several experiments, performed on the RISCbot II mobile manipulation platform are described and implemented to evaluate the RISCWare framework with respect to applicability and resource utilization

Algorithms and Data Structures for Multi-Adaptive Time-Stepping

Multi-adaptive Galerkin methods are extensions of the standard continuous and discontinuous Galerkin methods for the numerical solution of initial value problems for ordinary or partial differential equations. In particular, the multi-adaptive methods allow individual and adaptive time steps to be used for different components or in different regions of space. We present algorithms for efficient multi-adaptive time-stepping, including the recursive construction of time slabs and adaptive time step selection. We also present data structures for efficient storage and interpolation of the multi-adaptive solution. The efficiency of the proposed algorithms and data structures is demonstrated for a series of benchmark problems.Comment: ACM Transactions on Mathematical Software 35(3), 24 pages (2008

Adaptive development and maintenance of user-centric software systems

A software system cannot be developed without considering the various facets of its environment. Stakeholders – including the users that play a central role – have their needs, expectations, and perceptions of a system. Organisational and technical aspects of the environment are constantly changing. The ability to adapt a software system and its requirements to its environment throughout its full lifecycle is of paramount importance in a constantly changing environment. The continuous involvement of users is as important as the constant evaluation of the system and the observation of evolving environments. We present a methodology for adaptive software systems development and maintenance. We draw upon a diverse range of accepted methods including participatory design, software architecture, and evolutionary design. Our focus is on user-centred software systems

Toward a unified PNT, Part 1: Complexity and context: Key challenges of multisensor positioning

The next generation of navigation and positioning systems must provide greater accuracy and reliability in a range of challenging environments to meet the needs of a variety of mission-critical applications. No single navigation technology is robust enough to meet these requirements on its own, so a multisensor solution is required. Known environmental features, such as signs, buildings, terrain height variation, and magnetic anomalies, may or may not be available for positioning. The system could be stationary, carried by a pedestrian, or on any type of land, sea, or air vehicle. Furthermore, for many applications, the environment and host behavior are subject to change. A multi-sensor solution is thus required. The expert knowledge problem is compounded by the fact that different modules in an integrated navigation system are often supplied by different organizations, who may be reluctant to share necessary design information if this is considered to be intellectual property that must be protected