In this paper we present our ongoing work on Robotics Run-time Adaptation (RRA). RRA is a model-driven approach that addresses robotics runtime adaptation by modeling and resolving run-time variability of robotic applications.

Robotics applications are characterized by a huge amount of variability. Their design requires the developers to choose between several variants, which relate to both functionalities and hardware. Some of these choices can be taken at deployment-time, however others should be taken at run-time, when more information about the context is known. To make this possible, a software system needs to be able to reason about its current state and to adapt its architecture to provide the configuration that best suites the context. This paper presents a model-based approach for run-time adaptation of robotic systems. It defines a set of orthogonal models that represent the system architecture, its variability, and the state of the context. Additionally it introduces a set of algorithms that reason about the knowledge represented in our models to resolve the run-time variability and to adapt the system architecture. The paper discusses and evaluates the approach by means of two case studies.

The computational requirements of the increasingly sophisticated algorithms used in today's robotics software applications have outpaced the onboard processors of the average robot. Furthermore, the development and configuration of these applications are difficult tasks that require expertise in diverse domains, including software engineering, control engineering, and computer vision. As a solution to these problems, this paper extends and integrates our previous works, which are based on two promising techniques: Cloud Robotics and Software Product Lines. Cloud Robotics provides a powerful and scalable environment to offload the computationally expensive algorithms resulting in low-cost processors and light-weight robots. Software Product Lines allow the end user to deploy and configure complex robotics applications without dealing with low-level problems such as configuring algorithms and designing architectures. This paper discusses the proposed method in depth, and demonstrates its advantages with a case study.

During the last years robotic researchers have been able of developing extremely complex applications. The complexity of these applications is reflected by the variety of functionalities involved, which are provided by a significant number of components. Although the reuse of software components is becoming a best-practice, the reuse of reference architectures, which model sub-systems providing functionalities common to a great number of applications, is still uncommon. This paper provides two contributions to this topic: (a) a development process that defines how reference architectures can be exploited for building robotic applications, (b) the HyperFlex software toolchain, which has been developed for supporting the design and the reuse of reference architectures. The idea presented in this paper is simple yet powerful: instead of building complex applications by reusing single components, even more complex applications can be developed by reusing reference architectures of mature sub-systems.

Deploying a complex robot software architecture on real robot systems and getting it to run reliably is a challenging task. We argue that software deployment decisions should be separated as much as possible from the core development of software functionalities. This will make the developed software more independent of a particular hardware architecture (and thus more reusable) and allow it to be deployed more flexibly on a wider variety of robot platforms. This paper presents a domain-specific language (DSL) which supports this idea and demonstrates how the DSL is used in a model-driven engineering-based development process. A practical example of applying the DSL to the development of an application for the KUKA youBot platform is given.

Most of the current state of the art motion planning software libraries are not easily interchangeable, because core concepts are represented with different data structures, application programming interfaces (API) are not compatible, or algorithms are encapsulated in modules organized in mutually exclusive abstraction hierarchies. These problems limit the possibility to reuse different libraries in the same application interchangeably and to compare their quality attributes (performance, completeness, etc.). An approach to overcome these shortcomings is refactoring, a technique that aims to restructure a set of existing software libraries without affecting their external behavior in order to harmonize their architecture, data structures, and APIs. This paper presents a component-based refactoring approach that has allowed the transition from motion planning libraries, taking the object-oriented framework CoPP as basis, to a component-based system. In particular we describe a four-step application of well-known architecture refactoring patterns that redistributes the responsibilities among the classes, harmonizes the common data structures and reduces the coupling degree. The obtained system represents a composition of reusable components that are easy to customize and offer different algorithms to resolve the same problem. In this way the user could quickly compile a new motion planning application by simply choosing which algorithm to use for each functionality.