Robots are becoming more and more commonplace in many industry settings. This successful adoption can be partly attributed to (1) their increasingly affordable cost and (2) the possibility of developing intelligent, software-driven robots. Unfortunately, robotics software consumes significant amounts of energy. Moreover, robots are often battery-driven, meaning that even a small energy improvement can help reduce its energy footprint and increase its autonomy and user experience.In this paper, we study the Robot Operating System (ROS) ecosystem, the de-facto standard for developing and prototyping robotics software. We analyze 527 energy-related data points (including commits, pull-requests and issues on ROS-related repositories, ROS-related questions on StackOverflow, ROS Discourse, ROS Answers and the official ROS Wiki).Our results include a quantification of the interest of roboticists on software energy efficiency, 10 recurrent causes and 14 solutions of energy-related issues, and their implied trade-offs with respect to other quality attributes. Those contributions support roboticists and researchers towards having energy-efficient software in future robotics projects.

In today's world, reducing energy consumption should be the goal for any organization and any system, including robotics software systems. However, state of the practice in robotics software development focuses primarily on achieving functionality and performance, with minimal recognition of energy-efficiency as a driving software quality. The goal of this paper is to identify, synthesize, and empirically evaluate architectural tactics for energy-efficiency applied by practitioners in real robotics projects. Four tactics were identified by mining software repository techniques applied to the ROS ecosystem. The tactics were evaluated via experimentation on a real, commodity robotics system. Results show that the application of green architectural tactics tends to largely improve the energy-efficiency of the robot (7.9% energy savings when all tactics are applied) and that the movement strategy and the physical environment where the robot operates strongly influence how energy is consumed by the robot.

Clustering of users underlies many of the personalisation algorithms that are in use nowadays. Such clustering is mostly performed in an offline fashion. For a health and wellbeing setting, offline clustering might however not be suitable, as limited data is often available and patient states can also quickly evolve over time. Existing online clustering algorithms are not suitable for the health domain due to the type of data that involves multiple time series evolving over time. In this paper we propose a new online clustering algorithm called CluStream-GT that is suitable for health applications. By using both artificial and real datasets, we show that the approach is far more efficient compared to regular clustering, with an average speedup of 93%, while only losing 12% in the accuracy of the clustering with artificial data and 3% with real data.

Knowledge management plays an important role in the software architecting process. Recently, this role has become more apparent by a paradigm shift that views a software architecture as the set of architectural design decisions it embodies. This shift has sparked the discussion in both research and practice on how to best facilitate sharing of so-called architectural knowledge, and how tools can best be employed. In order to design successful tool support for architectural knowledge sharing it is important to take into account what software architecting really entails. In this paper, we define the main characteristics of architecting, based on observations in a large software development organization, and state-of-the-art literature in software architecture. Based on the defined characteristics, we determine how best practices known from knowledge management could be used to improve architectural knowledge sharing. This results in the definition of a set of desired properties of architectural knowledge sharing tools. Finally, we highlight the design and implementation of EAGLE, an architectural knowledge sharing portal that implements those properties.