Adaptive Coordination to Complete Mission Goals

Abstract

Autonomous systems have become incredibly common, with autonomous vehicles and drones dictating major research trends. Coordination of autonomous vehicles is one of these trends. With multiple different, likely proprietary, systems all needing to communicate and accomplish a task as a unit, there is a need for each individual autonomous system to be capable of entering or leaving the unit, either because of a failure or the need to perform a different task. Thus, each device has a local goal it is trying to complete and a global goal that needs to be completed as part of the unit. Given environmental changes, the systems must adapt by determining how they can satisfy their local goals and self-integrate into the unit's goal when needed or when it is consistent with a local goal. In this paper, we examine self-integrating policies as part of satisfying a global goal when local goals also reside in an autonomous system. We use a Partial-Order, Causal-Link representation of a simple mission to discover potential flaws, or inconsistencies, present between two autonomous devices that affect the global mission. We use these flaws as triggers for self-integration. Assurance cases provide the medium to specify and validate the global and local mission constraints initially and upon adaptation. We demonstrate our solution using multiple Anki Cozmo robots to complete a multi-cube retrieval mission.