Orbit Logic has teamed with the University of Colorado Boulder’s Research and Engineering Center for Unmanned Vehicles (RECUV) on a Phase I Small Business Technology Transfer (STTR) contract sponsored by the Office of Naval Research (ONR) to develop a hierarchical autonomous mission planning and execution capability for Autonomous Underwater Vehicles (AUVs) that will address many of the challenges associated with long duration operations.
Orbit Logic and CU Boulder have teamed on this STTR to research solutions that could significantly advance the state-of-the-art in the composition, deployment, and real-time adaptability of Autonomous Underwater Vehicle collaborative sorties. The company is leveraging prior work in the Unmanned Aircraft Systems (UAS) and Satellite Onboard Autonomy research areas to address the challenges of this maritime-related topic.
The research will innovate approaches to the exchange of information between collaborating vehicles that enables onboard decision-making to perform effectively despite the communication limitations imposed by the underwater marine environment. This will be accomplished through the leveraging and integration of several key technologies.
The first is the employment of a novel decentralized data fusion approach to maintain the collective state awareness of a group of federated collaborating assets. This capability intelligently exchanges data (or strategically withholds data exchange) to minimize communications while maximizing distributed knowledge.
Secondly, the company will employ “resource ferrying” strategies to optimize collaborative operations for energy consumption. This technique, applicable to homogeneous or heterogeneous systems, has promise to significantly improve the overall effectiveness of long duration missions by intelligently distributing data, processing, and stored electrical energy. Additionally, autonomous decision logic strategies leveraged from synergistic satellite autonomy research will overlay the data architecture.
Research will determine the most compact representation of asset information necessary to achieve robust, real-time adaptive mission performance. We will investigate the application of these technologies to “Pods” of cooperating heterogeneous AUV assets, assessing the mission effectiveness and resilience that might be gained.