Architecture and Task Plan Co-Adaptation with Metaplan for Unmanned Underwater Vehicles

Abstract

Unmanned Underwater Vehicles (UUVs) operate in complex environments and need to be able to adapt to sudden failures, or changes in the environment. To achieve autonomous operation, UUVs must have the ability to self-adapt in such cases. To effectively handle component failures and unexpected events, self-adaptation must be applied to both the architecture and task plan of the UUV. This allows the UUV to modify its architecture to accommodate component failures and adjust its task plan in response to unforeseen events that may render the current plan infeasible. The mutual dependencies between architectural adaptation and task planning pose a significant challenge when determining how to apply adaptation. As a result, the task planner must take into account the implications of architectural adaptation when generating a plan. This paper proposes Metaplan, a modular ROS2-based framework for applying architectural and task plan co-adaptation in a reusable way. Metaplan extends Metacontrol, an architectural self-adaptation framework, with a task planner based on Planning Domain Definition Language (PDDL). The effectiveness of Metaplan is demonstrated by evaluating it on SUAVE, an exemplar for evaluating self-adaptation frameworks for UUVs. Metaplan is shown to outperform a baseline which only makes use of a task planner. Architecture and task plan co-adaptation is demonstrated by presenting the UUV with a sudden drop in battery level, requiring the UUV to adapt both its architecture and its task plan. Furthermore, the reusability of Metaplan is showcased by applying it to a mobile manipulator scenario.