The need for autonomous robot systems in both the service and the industrial domain is larger than ever. In the latter, the transition to small batches or even "batch size 1" in production created a need for robot control system architectures that can provide the required flexibility. Such architectures must not only have a sufficient knowledge integration framework. It must also support autonomous mission execution and allow for interchangeability and interoperability between different tasks and robot systems. We introduce SkiROS2, a skill-based robot control platform on top of ROS. SkiROS2 proposes a layered, hybrid control structure for automated task planning, and reactive execution, supported by a knowledge base for reasoning about the world state and entities. The scheduling formulation builds on the extended behavior tree model that merges task-level planning and execution. This allows for a high degree of modularity and a fast reaction to changes in the environment. The skill formulation based on pre-, hold- and post-conditions allows to organize robot programs and to compose diverse skills reaching from perception to low-level control and the incorporation of external tools. We relate SkiROS2 to the field and outline three example use cases that cover task planning, reasoning, multisensory input, integration in a manufacturing execution system and reinforcement learning.

翻译：无论是在服务领域还是工业领域，对自主机器人系统的需求都比以往更为迫切。在工业领域，向小批量甚至"批量规模为1"的生产模式转变，催生了能够提供所需灵活性的机器人控制系统架构。这类架构不仅需要具备足够的知识集成框架，还必须支持自主任务执行，并确保不同任务与机器人系统之间的可互换性和互操作性。我们提出了SkiROS2——一个基于ROS的技能型机器人控制平台。SkiROS2采用分层混合控制结构，通过知识库支持对世界状态和实体的推理，实现自动化任务规划与反应式执行。其调度方案基于扩展行为树模型构建，融合了任务级规划与执行，从而实现了高度的模块化以及对环境变化的快速响应。基于前条件、保持条件和后条件的技能定义框架，能够有效组织机器人程序，并组合从感知到低层控制乃至外部工具集成的多样化技能。我们将SkiROS2与相关领域进行关联，并概述了三个典型用例，涵盖任务规划、推理、多感知输入、制造执行系统集成以及强化学习。