Description: Cislune Inc., in collaboration with the University of Central Florida (UCF), proposes the development of an analytical framework designed to enhance the safety and efficacy of lunar missions by optimizing the interaction dynamics between astronauts and Autonomous Systems (AS). This initiative aims to refine decision-making processes through the advanced integration of state-of-the-art machine learning algorithms, data analytics, and uncertainty quantification (UQ) techniques, specifically focusing on improving trust dynamics in Cyber-Physical-Human (CPH) teams. The intended use of funding is to prototype and validate this framework, aiming for a 10% reduction in crew cognitive workload by enhancing the quality and representation of mission-critical data, such as breathable oxygen levels, propellant stores, and rover range. Utilizing immersive technologies like OpenMCT for data visualization and Cislune's SimMoon for scenario simulations, the project targets the development of a proof of concept documented in a comprehensive Phase I final report. This framework is not only pivotal for the Artemis 3 mission, aiming to ensure astronaut safety and mission success through reliable and efficient decision-making but also sets the groundwork for broader applications in future lunar and deep space exploration missions. The target markets encompass NASA's lunar exploration initiatives and commercial space entities focused on establishing a sustained human presence on the Moon, offering a significant leap in the operational capabilities of CPH teams in space exploration contexts.

Benefits: Our analytical framework is strategically designed to support NASA's mission directives by enhancing astronaut safety and mission efficacy through improved trust dynamics between humans and Autonomous Systems (AS) in lunar and deep space exploration. By leveraging machine learning algorithms, data analytics, and uncertainty quantification techniques, our technology optimizes decision-making in Cyber-Physical-Human (CPH) teams. This framework directly contributes to the Artemis program, particularly the Artemis 3 mission, by ensuring astronauts can rely on AS for critical data regarding oxygen supplies, propellant levels, and rover range, essential for the success of lunar surface operations. Beyond Artemis, our technology has broader applications in future missions, including Mars exploration, by facilitating efficient human-AS collaboration, reducing crew cognitive workload, and improving operational decision-making. This aligns with NASA's goals of establishing a sustainable human presence on the Moon, paving the way for human exploration of Mars.The commercialization opportunities for our analytical framework extend beyond NASA applications, offering significant potential in various sectors where autonomous systems (AS) play a crucial role. Key markets include commercial space operations, where companies engaged in lunar exploration, satellite maintenance, and deep space missions can leverage our technology to enhance the safety and efficiency of their operations through improved human-AS collaboration. Additionally, the framework has applications in terrestrial sectors such as unmanned aerial vehicles (UAVs), driving, autonomous maritime operations, and remote exploration, where decision-making under uncertainty and trust in autonomous systems are critical for mission success. The technology's adaptability to different operational environments and its focus on optimizing trust dynamics make it a valuable tool for industries aiming to integrate AS more effectively into their operations, ensuring both safety and operational efficacy. This broad applicability underscores the technology's potential for significant impact across both space and earth-bound industries, promising a wide range of commercialization opportunities.