The application of robotics in manufacturing assembly is hindered by their lack of agility, their large changeover times for new tasks and new products , and their limited reusability. Removing these hindrances is technically hard due to the complexity of robot systems, the lack of understanding of robot capabilities as they pertain to assembly tasks, and the absence of measurement science and tools to assess and assure the robot’s agility. Since robots are primarily used in large companies for large lot size production, and changeover is performed infrequently and manually, manufacturers have avoided the agility issue. The key idea for this project is to develop the measurement science, in the form of an integrated agility framework, which will enable 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 – all of which will be validated using a combined virtual and real testing environment. This framework will (1) allow manufacturers 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 U.S. to compete effectively in the global market. Any company that is currently deploying or planning to deploy robot systems will benefit because they will be able to accurately predict the agility performance of their robot systems and be able to quickly re-task and reconfigure their assembly operations.
Deliver a robot agility performance metrics, information models, test methods and protocols, validated using a combined virtual and real testing environment, that will enable manufacturers to easily and rapidly reconfigure and re-task robot systems in assembly operations by 2018.
What is the new technical idea?
The new technical idea for this 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 – all of which are validated using a combined virtual and real testing environment. The information models enumerate and make explicit 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?." With this agility framework, the following will be possible:
What is the research plan?
- Using the test methods and metrics, manufacturers will be able to determine a robotic system’s ability to be agile when confronted with new assembly operations.
- Using the test methods and metrics, manufacturers will be able to quantify a robot’s performance when confronted with unexpected events or failures.
- Using the information models and protocols, manufacturers will be able to quickly and easily re-task their robots when new assembly operations are needed. Currently, it often takes much longer to program and configure a robot than it would take for the task to be completed by a human.
- Using the information models and protocols, manufacturers will be able to assess the robot’s ability to cope with part and environmental variations. Many subcomponents have component-by-component or lot-by-lot variations. Today’s high-precision robotic systems are not able to adapt automatically to such variations.
To accomplish the objective, this project will undertake four tasks, roughly in parallel:
- Test methods and metrics to measure the agility of robot assembly systems focusing on industries’ future challenges. This task will involve considerable industry interaction (via site visits) and literature reviews to develop use case scenarios to better understand industries’ expected challenges and to obtain their buy-in for the project. These use case scenarios will then be modeled as formal test methods, including testing procedures, initial and goal states, environmental conditions, and metrics.
- Formalized robot description and environment models of manufacturing assembly operations will capture relevant information about a robot’s capabilities, specifications, and configuration. Requirements for the robot model will be determined, and a thorough robot model literature review will be performed. We will leverage existing robot models, such as the Unified Robot Description Format (URDF), while adding further information, such as robot capabilities, to allow us to assess robot agility. Existing environmental models will also be elaborated to address robot assembly challenges. Formal approaches, such as ontologies, will be used to represent the information in the models, and interfaces will be created to realize them in simulation environments.
- Planning infrastructure will be comprised of a plan information model and planning protocols to allow for dynamic tasking and re-tasking of robot assembly systems. We will leverage existing planning approaches, such as the Planning Domain Definition Language (PDDL), while extending them to read from a dynamic database of world states to allow for robot agility in a changing environment. The output of task 1 will serve as the requirements for the database and planning information models.
- Integrated agility framework will be comprised of the output of the above three tasks, validated using a real and virtual environment. Starting in year one, a thorough analysis of simulation systems will be performed. An underlying framework will be built on this simulation environment, which will provide interfaces to ties in the output of the other tasks as initial results become available. This task will culminate in an environment that will be applied to a single robot (year 4) and to multiple robots and/or humans (year 5).
An iterative approach will be taken for all tasks, namely, developing initial models/systems, applying them to agility challenges, identify shortcomings, and making refinements.
October 1, 2013
Lead Organizational Unit:
Related Programs and Projects: