Overview of the Autonomous Systems Area

Autonomous artefacts, such as self-driving cars, unmanned aerial and marine vehicles, and smart robots, are rapidly entering mainstream focus from scientific, societal, technological and industrial perspectives. Additionally, large-scale systems and systems of system, e.g., infrastructure systems, are increasingly automated and self-organizing, with the possibility to make intelligent decisions on the basis of continuous, heterogeneous, multi-source data.

We use the term Autonomous Systems for autonomous artefacts and large-scale self-managing systems consisting of physical infrastructure and software that, together with humans, provide increased functionality, sustainability, and efficiency for society, e.g., self-driving cars, service robots, smart transportation systems, smart power grids and cloud infrastructures.

Autonomous systems must be capable of planning and executing complex functions as intended, with limited human intervention, operating in uncertain and unstructured physical and/or information environments, and managing unexpected external or internal events, e.g., faults. This distinguishes them from mere automated systems, which also are able to execute complex functions, but which mostly assume structured environments, have limited capacity to learn and adapt to unexpected events.

In the most tangible instantiations of autonomy, such as robotics and automated driving, the main focus for autonomy is to replace or complement the human’s capacity to manage complexity, namely, to analyse and make decisions based on vast amounts of, possibly uncertain, data and information in varying forms, under real-time constraints. For both autonomous vehicles and autonomous systems of systems, computations are to an increasing extent performed in the cloud, i.e., in virtualized software and servers located in data centres or in the access network infrastructure. Here a third instantiation of autonomy – autonomic computing – is found, i.e., self-managing data centres which use control, machine learning, and data analytics techniques to master architectural and operational complexity, by deciding dynamically how applications should be mapped to servers, how capacity should be scaled, and, if possible, how to adjust the application’s resource requirements.

The scientific development and evolution of autonomous systems can no longer be separated from the informational environments in which they are embedded. Consequently, scientific and technological advances in the areas of Data Analytics, the Internet of Things, Cyber-Physical Systems, Systems of Systems, and advances in sensor technologies, are essential to the science and development of autonomous systems.  Autonomous systems will also need to cooperate with humans and each other to solve complex tasks, and thus we see collaboration and interaction as another major trend in the science of autonomous systems.

 

Here we are today

There are now numerous industrial examples showing the tremendous potential and positive impact of technologies arising from the use of autonomous systems and their integration in informational environments. In the area of autonomous driving, collaborations between Uber and CMU and between Nissan and NASA Ames focus on autonomous taxis; Volvo Car’s Drive Me program and Scania’s recent demonstrators of platooning and self-driving heavy trucks are prominent Swedish examples.  In the area of smart homes we have seen Google’s notable acquisition of the smart thermostat company Nest providing the potential for autonomous self-learning HVAC systems, and Apple’s HomeKit technology that enables users to remotely manage their smart homes. Most of these technologies are still in their infancy but are believed to have potentially huge impact in the near future.