Over the last decade, researchers have been proposing and investigating computing systems with advanced levels of autonomy in order to manage the ever-increasing requirements in complexity. An autonomous system is an artificial system able to perform a certain number of tasks with a high degree of autonomy. Cognitive Dynamic Systems (CDS) are one particular approach to tackle these challenges. CDS aim at building up rules of behaviour over time through learning from continuous experiential interactions with the environment. By exploiting these rules, CDS can deal with environmental dynamics and uncertainties and have therefore leveraged the automation of tasks with complex perception-action cycles including surveillance, cognitive radio, traffic control and robot-mediated industrial and domestic applications. However, autonomous systems and in particular CDS lack in adaptability to internal and external non-stationary conditions. Many real-world systems frequently experience non-stationary conditions (i.e., unknown situations) due to uncertain interactions with the environment (incl. human agents) and users, failures or structural changes.
One important area of signal processing research is to enable the robot to cope with its environment whether this is on land, underwater, in the air, underground, or in space.
A fully autonomous system can:
- Gain information about the environment
- Work for an extended period without human intervention
- Move either all or part of itself throughout its operating environment without human assistance
- Avoid situations that are harmful to people, property, or itself unless those are part of its design specifications
An autonomous system may also learn or gain new knowledge like adjusting for new methods of accomplishing its tasks or adapting to changing surroundings.
Like other machines, autonomous systems still require regular maintenance.