Towards robust assembly with knowledge representation for the planning domain definition language (PDDL) Z. Kootbally a,n , C. Schlenoff b , C. Lawler a , T. Kramer c , S.K. Gupta d a Department of Mechanical Engineering, University of Maryland, College Park, MD 20740, USA b Intelligent Systems Division, National Institute of Standards and Technology, Gaithersburg, MD, USA c Department of Mechanical Engineering, Catholic University of America, Washington, DC, USA d Maryland Robotics Center, University of Maryland, College Park, MD, USA article info Article history: Received 1 May 2014 Received in revised form 31 July 2014 Accepted 11 August 2014 Available online 10 September 2014 Keywords: PDDL (Planning Domain Definition Language) Planning Replanning Agility Knowledge representation Robotics abstract The effort described in this paper attempts to integrate agility aspects in the “Agility Performance of Robotic Systems” (APRS) project, developed at the National Institute of Standards and Technology (NIST). The new technical idea for the APRS project is to develop the measurement science in the form of an integrated agility framework enabling manufacturers to assess and assure the agility performance of their robot systems. This framework includes robot agility performance metrics, information models, test methods, and protocols. This paper presents models for the Planning Domain Definition Language (PDDL), used within the APRS project. PDDL is an attempt to standardize Artificial Intelligence planning languages. The described models have been fully defined in the XML Schema Definition Language (XSDL) and in the Web Ontology Language (OWL) for kit building applications. Kit building or kitting is a process that brings parts that will be used in assembly operations together in a kit and then moves the kit to the area where the parts are used in the final assembly. Furthermore, the paper discusses a tool that is capable of automatically and dynamically generating PDDL files from the models in order to generate a plan or to replan from scratch. Finally, the ability of the tool to update a PDDL problem file from a relational database for replanning to recover from failures is presented. & 2014 Elsevier Ltd. All rights reserved. 1. Introduction The new technical idea for the “Agility Performance of Robotic Systems” (APRS) project [1] at the National Institute of Standards and Technology (NIST) is to develop the measurement science in the form of an integrated agility framework enabling manufac- turers to assess and assure the agility performance of their robot systems. This framework includes robot agility performance metrics, information models, test methods, and protocols – all of which are validated using a combined virtual and real testing environment. The information models enumerate and make expli- cit the necessary knowledge for achieving rapid re-tasking and being agile and will answer question such as “What does the robot need to know?”, “When does it need to know it?”, and “How will it get that knowledge?”. This framework will (1) allow manufac- turers to easily and rapidly reconfigure and re-task robot systems in assembly operations, (2) make robots more accessible to small and medium organizations, (3) provide large organizations greater efficiency in their assembly operations, and (4) allow the US to compete effectively in the global market. Any company that is currently deploying or planning to deploy robot systems will benefit because it will be able to accurately predict the agility performance of its robot systems and be able to quickly re-task and reconfigure its assembly operations. The increased number of new models and variants has forced manufacturing firms to meet the demands of a diversified custo- mer base by creating products in a short development cycle, yielding low cost, high quality, and sufficient quantity. Modern manufacturing enterprises have two alternatives to face the aforementioned requirements. The first one is to use manufactur- ing plants with excess capacity and stock of products in inventory to smooth fluctuations in demand. The second one is to use and increase the flexibility of their manufacturing plants to deal with the production volume and variety. While the use of flexibility generates the complexity of its implementation, it still is the preferred solution. Chryssolouris [2] identified manufacturing flexibility as an important attribute to overcome the increased Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/rcim Robotics and Computer-Integrated Manufacturing http://dx.doi.org/10.1016/j.rcim.2014.08.006 0736-5845/& 2014 Elsevier Ltd. All rights reserved. n Corresponding author. E-mail addresses: zeid.kootbally@nist.gov (Z. Kootbally), craig.schlenoff@nist.gov (C. Schlenoff), crlawler@umd.edu (C. Lawler), thomas.kramer@nist.gov (T. Kramer), skgupta@umd.edu (S.K. Gupta). Robotics and Computer-Integrated Manufacturing 33 (2015) 42–55