A DDS-based middleware for quality-of-service and high-performance networked robotics

摘要

Social robots must adapt to dynamic environments, human interaction partners and challenging new stringent tasks. Their inner software is usually distributed and should be designed and deployed carefully because slight changes in the robot's requirements can have an important impact not only on the existing source code but also on the resulting performance at run-time. This paper proposes an implementation of this inner software using a new lightweight middleware for networked robotics called Nerve. The principal novelty this middleware has with respect to other state-of-the-art approaches is that it guarantees both scalability and QoS, which are key requirements for real-time robotics software. The benefits of Nerve have been proved through its use in two key components of the cognitive system of a social robot: (i) the visual attention mechanism, used to extract relevant data from perceived images; and (ii) a robot learning by imitation control architecture that allows the social robot to be taught by people using natural demonstrations (i.e. using the same communication channels that would be used to teach people). Nerve makes use of existing patterns for networked applications together with the recent Data Distribution Service specification, which is a publish/subscribe standard for real-time and distributed systems that provides a wide set of QoS policies. In this paper, these different QoS policies have been applied carefully to achieve the best performance of the target robot.