This project will create a self-learning robotic ecology, called RUBICON (for Robotic UBIquitous COgnitive Network), consisting of a network of sensors, effectors and mobile robot devices.
Enabling robots to seamlessly operate as part of these ecologies is an important challenge for robotics R&D, in order to support applications such as ambient assisted living, security, etc.
Current approaches heavily rely on models of the environment and on human configuration and supervision and lack the ability to smoothly adapt to evolving situations.
These limitations make these systems hard and costly to deploy and maintain in real world applications, as they must be tailored to the specific environment and constantly updated to suit changes in both the environments and in the applications where they are deployed.
A RUBICON ecology will be able to teach itself about its environment and learn to improve the way it carries out different tasks. The ecology will act as a persistent memory and source of intelligence for all its participants and it will exploit the mobility and the better sensing capabilities of the robots to verify and provide the feedback on its own performance.
As the nodes of a RUBICON ecology will mutually support one another’s learning, the ecology will identify, commission and fulfil tasks more effectively and efficiently.
The project builds on many years of experience across a world-leading consortium. It combines robotics, multi-agent systems, novelty detection, dynamic planning, statistical and computational neuroscience methods, efficient component & data abstraction, robot/WSN middleware and three robotic test-beds. Validation will take place using two application scenarios.
Impact: The project will reduce the amount of preparation and pre-programming that robotic and/or wireless sensor network (WSN) solutions require when they are deployed. In addition, RUBICON ecologies will reduce the need to maintain and re-configure already-deployed systems, so that changes in the requirements of such systems can be easily implemented and new components can be easily accommodated.
The relative intelligence and mobility of a robot, when compared to those of a typical wireless sensor node, means that WSN nodes embedded in a RUBICON ecology can learn about their environment and their domain application, through the ‘training’ that is provided by the robot. This means that the quality of service which is offered by WSNs can be significantly improved, without the need for extensive human involvement.