Planning for mechatronics systems—Architecture, methods and case study

Klöpper, Benjamin; Aufenanger, Mark; Adelt, Philipp

doi:10.1016/j.engappai.2011.08.004

Cited by 6 publications

(10 citation statements)

References 23 publications

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“…Using the weighted sum of the objectives, the optimal sequence of operation modes can be determined (cf. [81]). During runtime in a non-deterministic environment with continuous processes, behavior planning has to include methods for handling resulting problems.…”

Section: Descriptionmentioning

confidence: 93%

“…To manage planning under uncertain conditions, different techniques can be combined in a hybrid planning architecture (cf. [81]). Figure 3.26 shows the hybrid planning architecture with the corresponding components for planning, execution and monitoring of plans.…”

Section: Descriptionmentioning

confidence: 93%

“…In case the of behavior planning, the executed partial function solutions are called operation modes. Thus, a planning problem for mechatronic systems can be formulated as follows (adapted from [81]):…”

Section: Prerequisites and Inputmentioning

confidence: 99%

“…[80][81][82]), the main challenge is to map the partial function solutions onto actions within the framework of PDDL (Planning Domain Definition Language, cf. [53]).…”

Section: Prerequisites and Inputmentioning

confidence: 99%

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Methods of Improving the Dependability of Self-optimizing Systems

Dorociak

Gausemeier

2014

Lecture Notes in Mechanical Engineering

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Abstract. Various methods have been developed in the Collaborative ResearchCenter 614 which can be used to improve the dependability of self-optimizing systems. These methods are presented in this chapter. They are sorted into two categories with regard to the development process of self-optimizing systems. On one hand, there are methods which can be applied during the Conceptual Design Phase. On the other hand, there are methods that are applicable during Design and Development.There are domain-spanning methods as well as methods that have been specifically developed for particular domains, e.g., software engineering or control engineering. The methods address different attributes of dependability, such as reliability, availability or safety.Each section is prefaced with a short overview of the classification of the described method regarding the corresponding domain(s), as well as its dependability attributes, to provide the reader with a brief outline of the methods' areas of application. Information about independently applicable methods or existing relationships and interactions with other methods or third-party literature is also provided.The development process for self-optimizing mechatronic systems which was introduced in Chap. 2 consists of two main phases: Conceptual Design and Design and Development. The main result of the Conceptual Design is the Principle Solution, which includes all information required for the concrete development during the second phase. Conceptual Design PhaseEven as early as during the specification of the Principle Solution, the dependability of self-optimizing mechatronic systems can be evaluated and improved by employing appropriate methods. The result is an improved Principle Solution, as such methods take the whole system into account before splitting it up into domain-specific tasks for the following development phase.

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Section: Descriptionmentioning

confidence: 93%

Section: Descriptionmentioning

confidence: 93%

Section: Prerequisites and Inputmentioning

confidence: 99%

“…[80][81][82]), the main challenge is to map the partial function solutions onto actions within the framework of PDDL (Planning Domain Definition Language, cf. [53]).…”

Section: Prerequisites and Inputmentioning

confidence: 99%

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Methods of Improving the Dependability of Self-optimizing Systems

Dorociak

Gausemeier

2014

Lecture Notes in Mechanical Engineering

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“…Automated planning and scheduling includes domain-independent planners that can solve large planning problems that contain continuous, non-linear effects (Klöpper et al, 2012). However, due to the complexity of the dynamics of such models, they are restricted to solving small scale problem instances with the implementation of a domain-specific heuristic (Fox et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Automated planning to minimise uncertainty of machine tool calibration

Parkinson

Longstaff

Fletcher

2014

Engineering Applications of Artificial Intelligence

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When calibrating a machine tool, multiple measurement tasks will be performed, each of which has an associated uncertainty of measurement. International Standards and best-practice guides are available to aid with estimating uncertainty of measurement for individual tasks, but there is little consideration for the temporal influence on the uncertainty when considering interrelated measurements. Additionally, there is an absence of any intelligent method capable of optimising (reducing) the estimated uncertainty of the calibration plan as a whole. In this work, the uncertainty of measurement reduction problem is described and modelled in a suitable language to allow state-of-the-art artificial intelligence planning tools to produce optimal calibration plans. The paper describes how the continuous, non-linear temperature aspects are discretized and modelled to make them easier for the planner to solve. In addition, detail is provided as how the complex uncertainty equations are modelled in a restrictive language where its syntax heavily influences the encoding. An example is shown for a three-axis machine, where the produced plan exhibits intelligent behaviour in terms of scheduling measurements against temperature deviation and the propagation of error uncertainties. In this example, a reduction of 58% in the estimated uncertainty of measurement due to intelligently scheduling a calibration plan is observed. This reduction in the estimated uncertainty of measurement will result an increased conformance zone, thus reducing false acceptance and rejection of work-pieces. u DEV ICE SQR Uncertainty due to the mechanical square in micrometers (µm) u E,DEV ICE P OST Uncertainty due to thermal expansion of the mounting post in micrometers (µm)

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