Active Exploitation of Redundancies in Reconfigurable Multi-Robot Systems

While traditional robotic systems come with a monolithic system design, reconfigurable multi-robot systems can share and shift physical resources in an on-demand fashion. Multi-robot operations can benefit from this flexibility by actively managing system redundancies depending on current tasks and having more options to respond to failure events. To support this active exploitation of redundancies in robotic systems, this paper details an organization model as basis for planning with reconfigurable multi-robot systems. The model allows to exploit redundancies when optimizing a multi-robot system's probability of survival with respect to a desired mission. The resulting planning approach trades safety against efficiency in robotic operations and thereby offers a new perspective and tool to design and improve multi-robot missions. We use a simulated multi-robot planetary exploration mission to evaluate this approach and highlight an exemplary performance landscape.

A Development Cycle for Automated Self-Exploration of Robot Behaviors

In this paper we introduce Q-Rock, a development cycle for the automated self-exploration and qualification of robotic behaviors. With Q-Rock, we suggest a novel, integrative approach to automate robot development processes. Q-Rock combines several machine learning and reasoning techniques to deal with the increasing complexity in the design of robotic systems. The Q-Rock development cycle consists of three complementary processes: (1) automated exploration of capabilities that a given robotic hardware provides, (2) classification and semantic annotation of these capabilities to generate more complex behaviors, and (3) mapping between application requirements and available behaviors. These processes are based on a graph-based representation of a robot's structure, including hardware and software components. A graph-database serves as central, scalable knowledge base to enable collaboration with robot designers including mechanical and electrical engineers, software developers and machine learning experts. In this paper we formalize Q-Rock's integrative development cycle and highlight its benefits with a proof-of-concept implementation and a use case demonstration.