Att 1 ARAM SOO.pdf

Attachment 1

Advanced Robotics and Automation for Manufacturing & Sustainment (ARAMS} Statement of Objectives

Period of Performance (PoP): 7 yr

1.0 Background

Advanced robotics and automation technology is critical for the Air Force's commercial industrial base and organic sustainment enterprise. Such technology can increase production throughput, leading to reduced acquisition times for both systems and critical components and maximizing the availability of Department of the Air Force (DAF) systems.

Robotics and automation also reduce costs, increase quality, mitigate critical workforce challenges, and facilitate data collection that enables digital twins for production assets.

Significant capability gaps exist, however, in robotics and automation for defense production and sustainment, where the low-volume, high-mix, and highly variable nature of manufacturing renders conventional solutions impractical. These capability gaps are primarily driven by scientific and technical (S&T) gaps; however, technical development to address these gaps in isolation is not enough. It is critical that technical development be accomplished through innovative teaming between academia, technology companies, robotic manufacturers, solution providers, the Air Force's industrial base, and end users so that at the end of development, commercial entities are immediately prepared to commercialize and supply that technology to the Air Force, its commercial industrial base, and the broader US manufacturing base. It is also important to disseminate robotics and automation technological development throughout the DoD production ecosystem to enable broad transition and to make selected intellectual property (IP) available to the ecosystem to spur research, development, and innovation.

2.0 Objective

The objective of this effort is to establish and maintain a consortium of organizations as described above to perform research, development, testing, and evaluation (RDT&E) to advance the state of the art in dual-use robotics and automation technologies of interest to the OAF. With the consortium established, the performer will work in close collaboration with the Air Force Research Laboratory

(AFRL) to assemble best of breed teams to develop, mature, deliver, and commercialize dual-use technical solutions for robotics and automation challenges. The performer will also work with AFRL to define and provide an appropriate level of program management for each effort. The performer will create an infrastructure to securely disseminate technical achievements and share selected IP, as negotiated for each effort, with relevant US-based S&Tcommunities. The technical scope includes broad categories of robotics and automation technology development as defined below.

3.0 Technical Scope

Agility encompasses the ability for robotics and automation systems to rapidly adapt to the changing needs of production. This includes the ability to rapidly repurpose systems for work in multiple environments, on multiple systems, and for multiple production processes. It also includes the ability to adapt to the intricacies of specific assets autonomously and rapidly, where the as-built state varies significantly from nominal design and wear, damage, and modifications in service represent significant deviations from the nominal state. Technical areas critical to agility include but are not limited to: automated toolpath and process planning; trajectory & motion optimization; manipulator accuracy; novel sensors and manipulators; novel methods for sensing and cognition; robotic assembly; flexible end of arm tooling; multipurpose, modular, & rapidly redeployable systems; task/process informed planning; methods for accommodating task, process, and workpiece variability; humanoid robotics.

3.2 Robotic Mobility

Robotic mobility is key to operational agility by providing scalable systems that do not require monumental installations. Mobile robots must, at a minimum, be able to detect and localize objects in their environment and be able to self-navigate collision free paths to the object of interest. In addition, mobile robots must accurately localize the object of interest to the degree required by the manufacturing process to be performed, which can be as accurate as 0.1 mm. This is a significant technical challenge because commercial off the shelf robots, while repeatable, are not accurate, and the methods that allow them to perform precision work in a fixed, single-purpose work cell do not transfer to mobile applications. Additionally, mobile robots must be able to perform without extensive fixed infrastructure for navigation and localization and will also need to perform reliably outdoors in highly dynamic environments, and in all weather conditions. Technical areas critical to mobility include but are not limited to: mobile manipulation; navigation in dynamic, unstructured, and/or GPS-denied environments; workpiece localization and safe approach in unstructured environments; localization and navigation outdoors; standardized architectures for mobile robots; novel mobility modes; aerial robotics; mobility on complex structures (e.g., airframes}; safety for mobile robotics, confined space robotics

3.3 Multi-Robot, Multi-Human Teaming

In OAF production and sustainment, cycle time is often the key metric, which requires multiple people working and multiple processes being performed simultaneously. For robotic and automation systems to be practical, robots must be able to seamlessly team with each other and human workers. This must be accomplished in real time and in response to the highly dynamic, variable, and unstructured environments in which they will be employed. The complex, dynamic, and highly variable nature of OAF production and sustainment will require human involvement for the foreseeable future, so robots will need to team with humans. This teaming will be both physical (humans and robot collaborating on a task} and cognitive (humans helping robots to plan a task or overcome circumstances out of the robots' experience}.

Technical areas critical to multi-robot, multi-human teaming include but are not limited to:

multi-agent dynamic teaming; swarm robotics; collaborative manipulation; safety for humans in/near the robotic work envelope; human-robot collaboration; simplified user interfaces; collaborative task planning; supervised autonomy; human augmentation (e.g., exoskeletons}; robotic path visualization; high-level task specification.

3.4 Process Modeling, Monitoring, & Control

Human experts learn to accomplish manufacturing tasks with experience over time, often developing an intuitive feel for the process and how specific actions or techniques affect it.

This expertise is time consuming and expensive to acquire and retain, which increases cost and lead time. This is especially problematic in a surge situation caused by an armed conflict.

Automated process, monitoring, and control is needed to reduce dependance on human experts. Additionally, agile robotic processing requires that robots have models of manufacturing processes, sensors for monitoring those processes, and control methods for adapting their actions to assure success. Process models must be sufficiently accurate for the task at hand, but they also must be fast enough to make processing decisions and adjustments on a human time scale or quicker. This implies novel modeling methods that integrate process physics with statistical and machine learning methods. Production processes critical to the OAF include but are not limited to: coating; coating removal;

surface preparation; forming; machining; casting; joining; composite and high temperature structure creation, inspection, and repair; assembly and disassembly; nondestructive inspection; testing and quality control. Operational sustainment processes include but are not limited to: fueling; payload loading/unloading; visual inspection; runway surface assessment and repair; towing; material handling.

3.5 Advanced Visualization

Advanced visualization is a critical tool for allowing humans to interact with robotic and automation systems, as well as with production processes and sustainment tasks directly. It allows humans to accomplish robotic task specification and understand the intent of autonomous systems quickly and intuitively. It enables the transformation from simple robotic path planning to robotic process planning and optimization. When combined with fast-acting process models and appropriate sensors, advanced visualization gives human manufacturing and sustainment technicians guidance on how to successfully complete manual tasks, thereby reducing or even eliminating training requirements and improving outcomes. Technical areas critical to advanced visualization include but are not limited to:

extended reality; data visualization & task assistance; production process visualization; real time production process data collection; human-robot interaction.

3.6 Scalability & Rapid Commissioning

To facilitate the rapid deployment of new and existing technologies for automated manufacturing, the OAF has a need for methods, approaches, and technology stacks to influence the scalability and transferability of automation-based solutions. To achieve this goal, robust digital architectures must be developed for creating virtual representations that are agile and responsive to real-time changes in the physical world. Specifically, OAF requires approaches that directly influence decision making at the edge for both human-in-the-loop scenarios as well as fully autonomous applications. This is particularly challenging when underlying training data used to develop existing algorithms do not represent emerging or unknown environments and scenarios. Areas of interest include but are not limited to: enterprise-wide digital architectures to enable federated machine learning;

ontology development for advanced knowledge-based reasoning; approaches for improving interoperability for process control especially across different mixes of environments and processes; software workflow development to ease the burden of application development; advances in algorithm development that leverage transfer learning to limit the burden of upfront data collection; developments in virtual commissioning that offer significant speed to implementation compared with traditional cell and/or task set-up.

3.7 OAF – Specific Environments

Aside from pervasive enabling technologies discussed above, the OAF has need for robotics, automation, process modeling/control, advanced visualization in OAF-specific applications and environments. Examples include but are not limited to: terrestrial manufacturing for space; in- space manufacturing and sustainment; flight line robotics;

robotics and automation in austere environments; production and sustainment of hypersonicvehicles; production and sustainment of autonomous collaborative platforms

(ACPs) and low cost/ attritable weapon systems; field assembly of ACPs; electromagnetic warfare implications for autonomous systems.

3.8 Manufacturing Process Informatics

Research and develop general and reusable technology stacks (methods, algorithms, tools, software, etc.) for collecting, managing, curating, and using manufacturing process data.

Develop data pipelines that facilitate the integration of data, digital models, and product lifecycle management tools, preferably based on current standards. Develop technology that utilizes manufacturing process data to enable adaptive process control to optimize manufacturing processes. Process optimization includes classical metrics like yield, cycle time, tolerances, and process capability, but can also include optimization of the process to maximize material property objectives. Develop tools that optimize or facilitate agile decision making for upstream and/or downstream manufacturing processes, possibly across links in the supply chain. Develop technology leverages process data to enable model-based inspection and serial number-specific workpiece quality inspection and certification. Develop technology that enables supply chain agility by allowing aerospace manufacturers to spin up production of exiting or novel systems quickly and confidently.

4.0 Consortium Requirements and Management

The consortium envisioned for this call will, within the scope defined above, focus on:

• Fostering collaboration between Air Force organizations, the Air Force's commercial industrial base, technology development organizations, academia, robot and automation equipment OEMs, technology solution providers, and other government agencies.

• Capturing technology demand signals from the Air Force's commercial industrial base and facilitating strategic planning for the consortium's technology development activities.

• Development, derisking, and pilot-scale demonstration of technology for the Air

Force's commercial and organic industrial bases.

• Archiving, curating, and securely sharing consortium developed intellectual property in a way that maximizes benefit to both the Air Force and the consortium.

4.1 No Foreign Participation

Foreign participation is prohibited except with the written consent of the government program manager backed by specific legal authority. The consortium manager will ensure that foreign owned US subsidiaries without adequate controls to prevent technology transfer to parent organizations are excluded from the consortium.

4.2 Cost Share

Cost share shall be required on individual projects/CLINs. The minimum cost share requirements per project are as follows:

10% for government-initiated projects

25% for consortium-initiated projects

The government program manager may increase cost share requirements for specific efforts based on technical risk. The government program manager may also waive the cost share requirement on specific government-initiated projects if it is in the government's best interest

(e.g., congressionally directed funding).

4.3 Funding the Consortium Manger

Minimal funding is available for standing up the consortium. Leveraging a pre-existing consortium is highly encouraged. The expectation is that the consortium manager will be funded via fees charged on each project/CUN, and any costs for administration of the consortium will be embedded in these fees. Three fee levels are desired:

1. Minimum Project Management. Under this option, the government PM will provide both technical and project management leadership. The consortium manager will administer the sub-award, disseminate results to the consortium, and archive, curate, and maintain any shared IP or data.

2. Standard Project Management. Under this option, the government PM will provide technical project leadership. The consortium manager will manage cost, schedule, and performance in addition to the services listed in the option above.

3. Technical Project Management. Under this option, the consortium manager will provide both technical and project management leadership under broad guidance from the government PM. The consortium manager will also manage cost, schedule, and performance in addition to the services listed in the option above.

4.4 IP and Data Management

The consortium manager will establish and implement procedures for:

• Identifying background IP of project performers.

• Identifying foreground IP and data to be gene rated by the project, and negotiating what may be shared amongst consortium members.

• Securing controlled unclassified information (CUI) and ensure compliance with export control laws, ITAR, and EAR.

• Securely sharing said foreground IP and data amongst the consortia in accordance with DoD distribution statements and export control laws.

• Storing, curating, maintaining, and delivering IP and data to the government and consortium members as allowed under the terms of the contract.

4.5 Deliverables

• Quarterly business reports (funds expenditure, cost share, etc.)

• All consortium-developed and government-owned intellectual property and related artifacts.

• All hardware and software purchased with government funds.

• Final report

• Additional deliverables as defined for each project/CUN.

4.6 OPSEC Requirements

All contractors shall participate in all activities associated with the disciplines of the organization's Industrial Security, Information Security, Personnel Security, Operations Security

(OPSEC}, and Antiterrorism programs, following appropriate measures in each program as required for this particular contract. Security measures are required in an effort to reduce program vulnerability from successful adversary collection, exploitation of critical information, and violations of export control requirements. The prime contractor will ensure that all subcontractors, if applicable, conform to these requirements as required by the prime contractor.

4.7 Program Protection Plan (PPP) Requirements

Any potential critical program information (CPI) generated as part of this effort will be reviewed by the government PM to determine the need for a PPP or to be included as part of an existing PPP.

4.8 Other Requirements

The consortium manager will maintain access to AFRL's Digital Lab Environment (Atlassian) for collaboration and deliverables and maintain a project management page for each project.

Project-specific requirements will be defined with the creation of each new project.

4.9 CUI Information

The contractor shall ensure that personnel associated with this effort have annual training on OPSEC as well as Information Handling, Marking, Storage, Transmission, Release, Destruction and Reporting points related to Controlled Unclassified Information (CUI).

4.10 Sharing of Information

Approval of technical and programmatic information shared external to this effort shall be approved by the government program manager.

5.0 Proposals

5.1 Proposal Expectations

The statement of work (SoW) should describe plans and procedures for how the consortium manager will:

• Convene all relevant and willing stakeholders across industry, academia and Government within a stable and neutral public-private partnership to foster a collaborative ecosystem to support the objectives outlined above.

• Facilitate coordination of the consortium and Air Force stakeholders to advance relevant technologies and impact AF acquisition and sustainment as well as a broad swath of industry.

• Provide an agile business process to address community-identified challenges and sponsor-directed requirements. This process should include the procedures that will be used to define, solicit, award, and execute projects.

• Develop and maintain guidelines that define how consortium data/information is shared as well as provide mechanisms to foster wide dissemination of consortium successes and opportunities across the AF and its supporting industrial base.

Business operations excellence is a central issue for this consortium to be successful. A financial management model that provides flexibility and speed at minimal cost is essential to develop a resilient consortium. The resultant SOW should define how the consortium manager will:

• Work with Air Force governance stakeholders to create and manage the technical portfolio of projects and activities.

• Establish a consortium governance structure capable of integrating feedback and input of consortium members and Air Force governance stakeholders.

• Establish and execute the financial management mechanisms for the consortium and its project investments.

• Allocate and track funds received from the Air Force to the consortium members according to the agreed upon budget and technical development plans.

• Provide a single point of contact to the financial officers of the consortium members and the Air Force governance stakeholders.

• Facilitate meetings and workshops.