Robotic software architecture in artificial intelligence

Download a PDF of the paper titled A Reference Software Architecture for Social Robots, by Luigi Asprino and 4 other authors Abstract: Social Robotics poses tough challenges to software designers who are required to take care of difficult architectural drivers like acceptability, trust of robots as well as to guarantee that robots establish a personalised interaction with their users. Moreover, in this context recurrent software design issues such as ensuring interoperability, improving reusability and customizability of software components also arise. Designing and implementing social robotic software architectures is a time-intensive activity requiring multi-disciplinary expertise: this makes difficult to rapidly develop, customise, and personalise robotic solutions. These challenges may be mitigated at design time by choosing certain architectural styles, implementing specific architectural patterns and using particular technologies. Leveraging on our experience in the MARIO project, in this paper we propose a series of principles that social robots may benefit from. These principles lay also the foundations for the design of a reference software architecture for Social Robots. The ultimate goal of this work is to establish a common ground based on a reference software architecture to allow to easily reuse robotic software components in order to rapidly develop, implement, and personalise Social Robots. arXivLabs: experimental projects with community collaborators arXiv...

Results and Conclusions We have identified eight themes that support operations, (ii) evolution and (iii) development specific activities of robotic software. The research in this area has progressed from object-oriented to component-based and now to service-driven robotics representing different cloud robotics that exploits the foundations of service-driven architectures to support an interconnected web of robots. The results of this SMS facilitate knowledge transfer – benefiting researchers and practitioners – focused on exploiting software architecture to model, develop and evolve robotic systems. Introduction Robotic systems are increasingly being integrated in various aspects of everyday life. The robotic applications range from mission critical (Parker, 1998) to infotainment and home service tasks (Mäenpää et al., 2004, Kwak et al., 2006). Robotic systems are expected to assist or replace their human counterparts for efficient and effective performance of all sorts of tasks such as industrial operations (Angerer et al., 2009) or surgical procedures (Kazanzides et al., 1992, Buzurovic et al., 2010). A robotic system is a combination of various components – hardware for system assembling and software for system operations – that must be seamlessly integrated to enable a robotic system's function as expected. To support the vision of a robotic-driven world 1, academic research (Parker, 1998, Hu et al., 2012, Brugali et al., 2007), industrial (Jackson and Coll, 2008, Kat...

Robot software systems tend to be complex. This complexity is due, in large part, to the need to control diverse sensors and actuators in real time, in the face of significant uncertainty and noise. Robot systems must work to achieve tasks while monitoring for, and reacting to, unexpected situations. Doing all this concurrently and asynchronously adds immensely to system complexity. The use of a well-conceived architecture, together with programming tools that support the architecture, can often help to manage that complexity. Currently, there is no single architecture that is best for all applications – different architectures have different advantages and disadvantages. It is important to understand those strengths and weaknesses when choosing an architectural approach for a given application. This chapter presents various approaches to architecting robotic systems. It starts by defining terms and setting the context, including a recounting of the historical developments in the area of robot architectures. The chapter then discusses in more depth the major types of architectural components in use today – behavioral control (Chap. 38), executives, and task planners (Chap. 9) – along with commonly used techniques for interconnecting connecting those components. Throughout, emphasis will be placed on programming tools and environments that support these architectures. A case study is then presented, followed by a brief discussion of further reading. Keywords • Robot System ...

Ovidiu Vermesan 1 *, Roy Bahr 1, Marco Ottella 2, Martin Serrano 3, Tore Karlsen 4, Terje Wahlstrøm 4, Hans Erik Sand 4, Meghashyam Ashwathnarayan 5 and Micaela Troglia Gamba 6 • 1SINTEF Digital AS, Oslo, Norway • 2Infineon Technologies Austria AG, Villach, Austria • 3Insight Centre for Data Analytics, National University of Ireland Galway, Galway, Ireland • 4NxTech AS, Fredrikstad, Norway • 5Infineon Technologies India Pvt. Ltd., Bangalore, India • 6CISC Semiconductors GmbH, Klagenfurt am Woertersee, Austria The Internet of Things (IoT) and Industrial IoT (IIoT) have developed rapidly in the past few years, as both the Internet and “things” have evolved significantly. “Things” now range from simple Radio Frequency Identification (RFID) devices to smart wireless sensors, intelligent wireless sensors and actuators, robotic things, and autonomous vehicles operating in consumer, business, and industrial environments. The emergence of “intelligent things” (static or mobile) in collaborative autonomous fleets requires new architectures, connectivity paradigms, trustworthiness frameworks, and platforms for the integration of applications across different business and industrial domains. These new applications accelerate the development of autonomous system design paradigms and the proliferation of the Internet of Robotic Things (IoRT). In IoRT, collaborative robotic things can communicate with other things, learn autonomously, interact safely with the environment, humans and oth...