The ARM Institute, a federally funded Manufacturing Innovation Institute (MII) part of the Manufacturing USA Network, publicly released our new Technology Project Call! There are number of unique and exciting aspects to this Project Call, including: • Project teams can request up to $1M per project. A minimum 1:1 cost share is required for all submissions. The overall ARM budget for this project call will be approximately $10M. • This Project Call will follow a NEW two-step process. The first step will be Concept Papers. Project teams selected from the Concept Phase will be invited for project presentations to our panel of reviewers. • New and expanded Project Call topic areas, including an increased focus on AI for robotic manufacturing systems.

There are number of unique and exciting aspects to this Project Call, including:

• Project teams can request up to $1M per project. A minimum 1:1 cost share is required for all submissions. The overall ARM budget for this project call will be approximately $10M.
• This Project Call will follow a NEW two-step process. The first step will be Concept Papers. Project teams selected from the Concept Phase will be invited for project presentations to our panel of reviewers.
• New and expanded Project Call topic areas, including an increased focus on AI for robotic manufacturing systems.

This Project Call features new and expanded Special Topic Areas, including:
• Adaptive Real-Time Path Planning and Control
A successful solution conveys a technical approach or methodology that enables an advanced manufacturing robotic systems to adjust a path or trajectory to variations that may occur in the system or process.
• Human Action & Intention Prediction
A successful project would initiate the development of enabling robots to assist humans in an efficient manner and prevent mistakes and accidents. As an example, it is feasible to assume that this technology would permit a human to work on a very technical manufacturing task while a robot or multiple robots assist in collecting tools, cleaning or simply performing value added non-essential tasks commonly required in a manufacturing environment.
• Robot Learning
The development of models and methods to enable a robot to learn a manufacturing task from a human or another robot.
• Feature and Pose Recognition and Estimation
The development of robotic contextual awareness of a manufacturing task, component, process and perhaps other activities within a facility to enable and anticipate the next process step and be capable of supporting.
• Virtual Commissioning of Advanced Robotic Systems
There is a need to reduce the risk of commissioning new manufacturing robotic systems and accelerate the deployment of robotic automation. Part of qualifying an automation system for production is commissioning the equipment through a series of tests. When done at installation time, any defects in the system can be costly to troubleshoot and resolve. Simulation environments provide useful solutions but struggle to replicate realistic manufacturing environments that include non-ideal performance of equipment, products and sensors.
• Artificial Intelligence (AI) and Data for Advanced Manufacturing Robotic Systems
To catalyze the adoption of AI used in advanced manufacturing robotic systems the development of structures, standards, and practices for managing associated data are essential. This includes the development of benchmarks, test methods, and metrics to characterize the performance of manufacturing robotic systems in leveraging AI algorithms and associated data.
• Multi-Agent Motion Planning and Tasking for Discreet and Continuous Manufacturing Processes
The development of real-time robotic awareness of other robots and agents, capable of providing cooperative motion and tasking, and error recovery of one or more agents within a multi-agent setting.
• Virtual Masking for Advanced Coating Applications
Advanced coatings used in aerospace and on ground vehicles typically require masking. The process is labor intensive and provides inconstant results and quality depending on the application and process variability. Coating applications are typically energy intensive, produce emissions that present safety and environmental concerns, yield excessive material waste and often result in rework. A successful technology development project will eliminate or significantly reduce the necessity for masking for advanced coating applications and significantly improve the current processes.

You can learn more here and download our related documents here: https://arminstitute.org/project-calls/.

We will also host a public webinar in support of this Project Call on July 27: https://register.gotowebinar.com/register/6039294539335942412