In this sprint, there is not expected to be any collaboration with a human teammate during mission execution. We anticipate the human role in the system to be limited to activities such as providing initial tasking and setting operating parameters.

It is our current expectation that ROS 1 will be sufficiently able to handle the “squad scale” operation in this program (4-10 UGVs). One technical note relating to this question is that the ARL Autonomy Stack contains components designed to support the communication between platforms in a “multi-master” configuration over a UDP-based link. Future sprints will potentially transition to ROS 2. The use of ROS2 “bridge nodes” would also be a potential approach to improve scaling.

Proposed budgets should be appropriate for the work described. ARL anticipates funding between 3 to 5 efforts in this sprint.

Current robot models in the simulator feature a variety of sensors including several different cameras and lidars. Additional sensor models of existing hardware can be added to the simulation environment as needed for the proposed effort.

This sprint is looking at a team of ground robots in “squad” scale (4-10) units. It does not include an aerial component.

The scenario which includes robot and or communications failure could be specified in a proposal but is not generally called for in this sprint topic. Including robot attrition / communications disruptions is not planned for this cycle but could be part of the performance metrics utilized by specific proposers to demonstrate robustness if desired.

Doctrine for the Army is a standard of operation which is taught to Soldiers. This includes formations (such as wedge, echelon, column, line), and maneuver tactics such as traveling or bounding overwatch.

Prior ARL programs contain a mix of industry, UARCs, and university performers. There is no target distribution for the performers in this program.

The call is for development of multi-agent tactical maneuver behaviors in a simulator environment. Each funded proposal will be working with an ARL technical POC (who can be identified during the proposal review process by ARL). This POC will facilitate follow-on transitions where appropriate.

Generalizable machine learning here should be able to operate with different initial conditions in the same simulation environment, as well as to operate (potentially with some but minimal degraded performance) in similar but unseen simulation environments (i.e., a learned controller would be trained in several “forest” simulations and could be demonstrated in an unseen test “forest” simulation, but not expected to operate well in a “village” simulation).

No limitations are specified here with respect to SWAP aside from what could conceivably fit within a vehicle platform.

Multiple scales of operation could be considered from a highly complex 50–100-meter maneuver up to a multi kilometer operation with mixed complexity.

ARL plans to develop reserved “test scenarios” which will be used to support the evaluation of proposed solutions.

Yes, the tools provided by ARL are a starting point meant to support further novel research in AI and machine learning techniques that are relevant to the Call for Proposals.

Based on the described research problem, ARL anticipates many potential solutions will have a machine learning component. However, a machine learning component is not a requirement if a proposed approach can address the described problems without use of such a component.

There are no preconceived expectations on the proportion of funded proposals from academia, industry, and UARCs.

There are many documents on this subject provided at https://armypubs.army.mil/ProductMaps/PubForm/ATP.aspx which can be referenced in proposals. ARL has provided examples from the Combined Arms Battalion (ATP_3-90.5) and Scout Platoon (ATP_3-20.98) as a starting point, but other sources can be cited.

Apart from human input potentially defining initial conditions, goals, and other parameters – this sprint is focused on the autonomous operation of ground robot teams without further human involvement.

Yes, proposals can build from research outcomes from other ARL and non-ARL programs.

No, leveraged or in-kind funding is not a technical review criterion. Additional leveraged or in-kind funds can be disclosed to justify lower cost bids.

Refer to Section F, Articles c, and d of the FOA for the required certifications.

Please follow the instruction listed in FOA to ensure all completed forms are included in the package.

https://arl.devcom.army.mil/resources/baa-forms/

References are not included in the page limits.

Prior information can be used to a degree, but any proposed solution should be able to handle incomplete or absent prior information (such as in a potential “hold-out” test scenario).

Prior information about the location of adversaries should be limited to high-level operating conditions: i.e. Beyond this phase line, contact with adversary scout units is (not/somewhat/highly) probable. Adversaries have been seen in this named area. Prior knowledge of the adversary should not be limited to precise position information. Maneuvering ground assets should not assume that all adversaries are known in advance.

Hardware experimentation is not required for this cycle in the second year. If hardware demonstrations are feasible and appropriate for a specific project, the ARL Technical POC(s) can help to facilitate the transition of research outcomes to the appropriate technical maturity suitable for this experimentation.

Proposers can cite current capabilities in IoBT systems to justify their choices / configurations in terms of capabilities; however, as there is an existing IoBT collaborative research alliance, proposed research should not have significant overlap with that program.

The slides, videos, and recordings from the opportunity webinar are posted to the TBAM webpage. Additionally, new recordings are available there for the MITRE Multi-Agent Environment (MMAE) as well as current research overviews.

Please review the webinar recordings / slides and review all provided information in the FOA and webinar slides for further information on relevant research topics/direction.

In general, there are no specific restrictions on the types of approaches which can be proposed. Each proposal will be evaluated on the criteria enumerated in the FOA.

This is a typo – the correct due date is 27 May at 1700 EDT.

The primary focus of this program is on the coordinated maneuver of ground assets in this sprint cycle. The availability of additional “prior” information (attributable to aerial assets) could be included. The coordination between UAVs and UGVs is the subject of other programs (DCIST).

No, there is no requirement of this kind.

“Squad scale” – 4 to 10 agents

It should be enough to reach goal location(s)/ areas by choosing routes that maximize cover and concealment.

Desert environments could still exhibit cover in the form of sparse foliage (brush) and elevation changes. This type of operating environment is likely to be more challenging with respect to finding cover and concealment.

Durable equipment such as computers should be listed as “equipment.”

Awards will require compliance with Bayh Dole and the appropriate implementing regulations which provides the Government with a nonexclusive, irrevocable, paid-up license to use a Subject Invention throughout the world.