Taking control: Modular and adaptive robotics process control systems
Taking control: Modular and adaptive robotics process control systems
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掌控：模块化和自适应机器人过程控制系统
DOI：
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发表时间：
2012
期刊：
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通讯作者：
Wolfgang Schröder
中科院分区：
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作者：
Peter Ulbrich;Florian Franzmann;C. Harkort;Martin Hoffmann;Tobias Klaus;Anja Rebhan;Wolfgang Schröder
机器人系统通常包括复杂的传感器和致动器系统，其控制应用也同样复杂。这些系统经常修改和扩展，必须适应其环境。虽然自动化系统是为特定的生产过程量身定制的，但自动驾驶汽车必须根据环境条件自适应地切换其传感器和控制器。然而，在设计和实现过程控制系统时，传统的控制理论关注的是手头的控制问题，而没有考虑到这种可变性。因此，所得到的模型和实现工件是单片的，另外使实时系统设计复杂化。在本文中，我们提出了一个模块化的方法，机器人过程控制系统的设计，它不仅针对在设计时的可变性，但也在运行时的适应性。我们的方法是基于一个分层的控制体系结构，其中包括一个明确的接口之间的两个领域：控制工程和计算机科学。我们的架构提供了独立的构建块和数据流方面的关注点分离。例如，传感器的更换不再涉及下游过滤器和控制器的繁琐修改。同样，可以省略高级应用行为到过程控制系统的易出错映射。我们通过自动驾驶汽车用例的例子验证了我们的方法。我们的实验结果表明，易于使用，并保持与原来的单片设计的控制质量的能力。
Robotics systems usually comprise sophisticated sensor and actuator systems with no less complex control applications. These systems are subject to frequent modifications and extensions and have to adapt to their environment. While automation systems are tailored to particular production processes, autonomous vehicles must adaptively switch their sensors and controllers depending on environmental conditions. However, when designing and implementing the process control system, traditional control theory focuses on the control problem at hand without having this variability in mind. Thus, the resulting models and implementation artefacts are monolithic, additionally complicating the real-time system design. In this paper, we present a modularisation approach for the design of robotics process control systems, which not only aims for variability at design-time but also for adaptivity at run-time. Our approach is based on a layered control architecture, which includes an explicit interface between the two domains involved: control engineering and computer science. Our architecture provides separation of concerns in terms of independent building blocks and data flows. For example, the replacement of a sensor no longer involves the tedious modification of downstream filters and controllers. Likewise, the error-prone mapping of high-level application behaviour to the process control system can be omitted. We validated our approach by the example of an autonomous vehicle use case. Our experimental results demonstrate ease of use and the capability to maintain quality of control on par with the original monolithic design.