基于Metaplan的自主机器人运行时架构与任务计划协同自适应
Runtime Architecture and Task Plan Co-Adaptation for Autonomous Robots with Metaplan
AI总结：
针对真实世界中自主机器人的不确定性应对需求，提出可跨域复用的Metaplan框架，结合机器人与环境模型及PDDL规划器实现架构与任务计划的运行时协同自适应，并通过两类无人载具验证其有效性与可复用性。
摘要：
自主机器人部署于真实世界时需具备应对不确定性的能力。为在这类环境中稳健运行，机器人需同时适配自身架构与任务计划。架构自适应与任务计划自适应相互依赖，因此要求系统实现运行时架构与任务计划协同自适应。本研究提出Metaplan框架，该框架利用机器人及其环境的模型，结合PDDL（规划域定义语言）规划器实现运行时架构与任务计划协同自适应，且设计为可跨不同领域轻松复用。实验以自适应无人水下航行器为示例，验证Metaplan可成功执行运行时架构与任务计划协同自适应，又通过将其应用于无人地面车辆，证明了其可复用性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
