Tactical Behaviors for Autonomous Maneuver (TBAM) CRA
The objective of the Tactical Behaviors for Autonomous Maneuver (TBAM) Collaborative Research Alliance (CRA) is to develop coordinated behaviors for small groups of autonomous agents to perform doctrinal as well as novel tactical maneuver in realistic simulations of complex military-relevant environments.
Performers develop novel techniques to learn, as well as demonstrate, coordinated models of maneuver in realistic simulations of complex terrain such as forest/jungle, undulating desert/grassland, watershed/wet-gap, and rural settings (fields with boundary fences, sparse road networks, intermittent watershed and forested areas). The coordinated behaviors exhibited by the ground-robot team should find military-relevant routes that maximize cover and concealment and maneuver as if adversary contact were imminent. In areas where environmental cover is absent, but traversal is required by the mission, elements of the team should provide cover for their advancing teammates.
The TBAM CRA is a 6.1 basic research program. It consists of a series of two-year sprint efforts executed with annual program reviews. Each two-year sprint topic is focused on addressing a different set of scientific areas, which will support higher technology readiness level (TRL) research with internal DEVCOM ARL subject-matter experts. The first two-year sprint topic is “coordinated and adversarial tactical maneuver in complex terrains,” with an operational scenario entitled “Movement to Contact.” In this scenario, contact with adversarial positions is a constant concern – in some situations this contact should be avoided through use of terrain features and cover; in other missions the adversary positions should be met with a posture of tactical overmatch through coordinated maneuver – the synchronized actions of a distributed system.
|Opportunity released||22 April 2022|
|Opportunity webinar||29 April 2022, 1500 EDT|
|Deadline for questions on funding opportunity||16 May 2022|
|Proposals due for Cycle 1||27 May 2022, 1700 EDT|
|Notification to recipients||1-14 July 2022|
|Cycle 1 awards||Sept 2022|
Relevant links and documentation
- Funding Opportunity Announcement (FOA) on grants.gov
- Questions and Answers
- Video recording on the MITRE Multi-Agent Environment and current research from opportunity webinar
- Video animation conceptual scenario: Two squads of robotic agents are maneuvering to a position on a hilltop to monitor four adversary units operating in the valley below. This video demonstrates some tactical maneuvers such as leveraging formations, as well as bounded overwatch both within one squad, as well as the second squad providing overwatch for the first squad. Assets in this video utilize terrain for quick movement in cover, and use overwatch when they must make maneuvers out in the open.
- Video of Robotics Collaborative Technology Alliance capstone demonstration illustrating learned navigation behaviors
- Video recording of a simulated polaris MRZR in the ARL Unity robot simulator
- Video 1 showing the learned controller for the combined search task in the MITRE Multi-Agent Environment (MMAE)
- Video 2 showing the learned controller for the combined search task in the MITRE Multi-Agent Environment (MMAE)