Coalescent Multi-Robot Teaming through Automated Task Solution Synthesis

This paper describes a methodology for automatically synthesizing task solutions for heterogeneous multi-robot teams. In contrast to prior approaches that require a manual predefinition of how the robot team will accomplish its task (while perhaps automating who performs which task), our approach automates both the how and the who to generate task solution approaches that were not explicitly defined by the designer a priori. Our approach, which we call ASyMTRe, is based on mapping environmental, perceptual, and motor control schemas to the required flow of information through the multi-robot system, automatically reconfiguring the connections of schemas within and across robots to synthesize valid and efficient multi-robot behaviors for accomplishing the team objectives. We present the anytime ASyMTRe configuration algorithm, proving that the configuration algorithm is correct, and formally addressing issues of completeness and optimality. We then validate the approach by presenting the results of applying our methodology to two different teaming scenarios: multi-robot transportation, and multirobot box pushing. The advantages of this new approach are that it: (1) enables the robot team to synthesize new task solutions that use fundamentally different combinations of robot behaviors for different team compositions, and (2) provides a general mechanism for sharing sensory information across networked robots, so that more capable robots can assist less capable robots in accomplishing their objectives.