Optimization of On-Orbit Robotic Assembly of Small Satellites
Author(s)
Uzo-Okoro, Ezinne; Erkel, Daniel; Manandhar, Prakash; Dahl, Mary; Kiley, Emily; Cahoy, Kerri; de Weck, Olivier L; ... Show more Show less
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On-orbit assembly missions typically involve humans-in-the-loop and use large custom-built robotic arms designed to service existing modules. A proposed concept of on-orbit robotic assembly of modularized CubeSat components within a spacecraft locker eliminates the need for humans-in-the loop. The spacecraft locker supports use cases such as rapidly placing failed nodes within a constellation of satellites and providing sensing and propulsion capabilities in Low Earth Orbit. Despite the recent proliferation of small satellites, there are few planned demonstrations of on-orbit assembly and few demonstrations of on-orbit servicing. Key gaps challenges of in-space assembly of small satellites are (1) the lack of standardization of electromechanical CubeSat components for compatibility with commercial robotic assembly hardware, and (2) testing and modifying commercial robotic assembly hardware. In this work, we focus on testing and modifying: we develop an optimization process for a robotic assembly model to integrate small satellites in space. Our process focus is on the optimization of the on-orbit assembly time of small satellites. We use Commercial-Off-The-Shelf (COTS) robot arms to snap together components in a spacecraft, while minimizing humans-in-the-loop. Assembly time is the selected performance metric as it is critical to the assertion that building small satellites on-orbit results in reduced budget and satellite development time on Earth. We minimize on-orbit small satellite assembly time by optimizing assembly time with the Genetic Algorithm, which use dexterous robotic arms to assemble components, without any negative effects on the attitude and control system. We implement a robot arm assembly model in Python, using Inverse Kinematics. We use a Genetic Algorithm-based optimization scheme, with time as the objective function, and three constraints: robot assembly volume, power consumption, and peak power. Design variables such as joint damping, motor force (torque), position gain and velocity gain are used to model grasping a component and moving the component to the satellite assembly area of the spacecraft. The robot arms are required to be within a tolerance defined based on the 300 mm x 300 mm x 500 mm assembly area. In simulation, we observe that using a given baseline servo motor (7 V) at high proportional gains results in optimal assembly time of approximately 10-20 seconds per component assembly, compared to roughly double this time per component for a 1U CubeSat weighing 2 kg. However, we expect this improvement to result in 25% higher power consumption. Using a high gain value with a lower voltage (5 V) motor results in oscillations and additional time required to dampen out to within the given tolerance, and results in increased assembly time. The benchmarked small satellite assembly time with a human-in-the-loop requires 50 weeks to 90 months of component assembly and integration time on Earth. We anticipate that on-orbit assembly capability optimized for a 1 U functional CubeSat with 30 W of total power, would reduce the assembly time by an order of magnitude. With robotic arm models, for a 1 U CubeSat assembly, we show up to 42% saving benefit in robotic assembly time.
Date issued
2020-11Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics; Sloan School of Management; Massachusetts Institute of Technology. School of Engineering; Massachusetts Institute of Technology. Department of Mechanical Engineering; Massachusetts Institute of Technology. Institute for Data, Systems, and SocietyJournal
Accelerating Space Commerce, Exploration, and New Discovery Conference
Publisher
American Institute of Aeronautics and Astronautics
Citation
Uzo-Okoro, Ezinne et al. "Optimization of On-Orbit Robotic Assembly of Small Satellites." Accelerating Space Commerce, Exploration, and New Discovery Conference, November 2020, virtual event, American Institute of Aeronautics and Astronautics, November 2020. © 2020 Massachusetts Institute of Technology
Version: Author's final manuscript
ISBN
9781624106088