Autonomous robots need to be able to handle uncertainties when deployed in the real world. For the robot to be able to robustly work in such an environment, it needs to be able to adapt both its architecture as well as its task plan. Architecture adaptation and task plan adaptation are mutually dependent, and therefore require the system to apply runtime architecture and task plan co-adaptation. This work presents Metaplan, which makes use of models of the robot and its environment, together with a PDDL planner to apply runtime architecture and task plan co-adaptation. Metaplan is designed to be easily reusable across different domains. Metaplan is shown to successfully perform runtime architecture and task plan co-adaptation with a self-adaptive unmanned underwater vehicle exemplar, and its reusability is demonstrated by applying it to an unmanned ground vehicle.

翻译：自主机器人在真实环境中运行时需要能够处理各种不确定性。为了确保机器人在此类环境中稳健工作，它需要能够同时调整其架构和任务计划。架构调整与任务计划调整相互依赖，因此要求系统实现运行时架构与任务计划的协同自适应。本文提出的Metaplan方法利用机器人及其环境的模型，结合PDDL规划器，实现运行时架构与任务计划的协同自适应。Metaplan设计上具有跨领域的高度可复用性。通过一个自适应无人水下航行器示例，Metaplan成功展示了运行时架构与任务计划的协同自适应能力，并将其应用于无人地面车辆，验证了其可复用性。