Within Air Force depots for aircraft maintenance and sustainment, there are many workloads that present significant challenges for human personnel due to reasons including high physical demand, repetitive tasks, and unfriendly environmental conditions. In the endeavor of optimizing manufacturing and sustainment operations, robots have increasingly been integrated, serving to separate human personnel from potentially hazardous activities and creating opportunities for process improvement. The robots that have been determined to be most versatile in industrial settings as support to human personnel are those designed to operate similarly, in a humanoid manner.As the potential of humanoid robots for widespread deployment in various applications is growing in accordance with their increasing sophistication and capability, so is the cruciality of managing and coordinating large fleets of these robots effectively for the maximization of their utility and the assurance to complete tasks successfully. While implementing a fleet management system allows for the dynamic allocation of robots to specific maintenance tasks based on real-time demand and priority, resource utilization optimization, and aircraft downtime minimization, current robot management systems often lack the scalability, flexibility, and robustness required for complex, real-world deployments involving humanoid robots beyond material handling operational scenarios. Current research emphasizes the integration of advanced sensing capabilities, such as computer vision and tactile feedback, into humanoid robots to enable them to perform intricate maintenance tasks, such as component inspection and repair, with increasing autonomy and precision to support a shift in robot fleet management towards decentralized control architectures that allow humanoid robots to adapt to changing task priorities and collaborate more effectively with human personnel. This project seeks to develop a humanoid fleet management system that provides a centralized platform for monitoring, controlling, and coordinating the activities of multiple robots across a variety of manufacturing and sustainment operations. The system should enable autonomous task allocation based on robot capabilities and availability, optimize resource utilization, allow real-time monitoring and control, facilitate seamless communication between robots and human operators, and adapt to dynamic changes in the operational environment. The system should be scalable to fleets of varying sizes and adaptable to different humanoids, other robot platforms, and operational scenarios.
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