REMORA CubeSat for large debris rendezvous, attachment, tracking, and collision avoidance

McCormick, Ryan; Austin, Alex; Wehage, Kristopher; Backus, Spencer; Miller, Robert R.; Leith, James A.; Bradley, Ben K.; Durham, Parker; Mukherjee, Rahul

doi:10.1109/aero.2018.8396814

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…However, demonstration involved complex vision sensing for successful completion of task, however make it nearly impossible to implement on low compute power hardware. Nonetheless, In recent work, arm augmented cubesat [27] was designed by JPL to showcase hardware capability to perform autonomous In orbit assembly. Cubesat with arm uses on board passive sensing to obtain relative pose of object of interest, in this case that object is a truss.…”

Section: Ia Relative Pose Estimation For On-orbit Assemblymentioning

confidence: 99%

“…Future work will be on integrating this model with tracker to test the performance of assembly task on realworld testbed with framework as shown in fig. 1 In our previous work, [21] we demonstrated ability to perform assembly task using monocular vision based tracking and remora arm [27] designed by JPL. This work shows successful real-time decision making using vision based feedback and dynamic motion primitives on low compute platform.…”

Section: Ib Problem Statementmentioning

confidence: 99%

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Assistive Relative Pose Estimation for On-orbit Assembly using Convolutional Neural Networks

Sonawani,

Alimo,

Detry

et al. 2020

Preprint

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Accurate real-time pose estimation of spacecraft or object in space is a key capability necessary for on-orbit spacecraft servicing and assembly tasks. Pose estimation of objects in space is more challenging than for objects on Earth due to space images containing widely varying illumination conditions, high contrast, and poor resolution in addition to power and mass constraints. In this paper, a convolutional neural network is leveraged to uniquely determine the translation and rotation of an object of interest relative to the camera. The main idea of using CNN model is to assist object tracker used in on space assembly tasks where only feature based method is always not sufficient. The simulation framework designed for assembly task is used to generate dataset for training the modified CNN models and, then results of different models are compared with measure of how accurately models are predicting the pose. Unlike many current approaches for spacecraft or object in space pose estimation, the model does not rely on hand-crafted object-specific features which makes this model more robust and easier to apply to other types of spacecraft. It is shown that the model performs comparable to the current feature-selection methods and can therefore be used in conjunction with them to provide more reliable estimates.

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Section: Ia Relative Pose Estimation For On-orbit Assemblymentioning

confidence: 99%

Section: Ib Problem Statementmentioning

confidence: 99%

Assistive Relative Pose Estimation for On-orbit Assembly using Convolutional Neural Networks

Sonawani,

Alimo,

Detry

et al. 2020

Preprint

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“…Hays et al (2004) presented a similar AKM capturing method, but instead of a probe, they used a flexible cable-like probe, that can be retracted after capturing. McCormick et al (2018) conducted a feasibility study of a cubesat equipped with a robotic clamp, that could capture the engine nozzle of a large piece of space debris and change its orbit.…”

Section: Introductionmentioning

confidence: 99%

On-Orbit Robotic Grasping of a Spent Rocket Stage: Grasp Stability Analysis and Experimental Results

2021

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The increased complexity of the tasks that on-orbit robots have to undertake has led to an increased need for manipulation dexterity. Space robots can become more dexterous by adopting grasping and manipulation methodologies and algorithms from terrestrial robots. In this paper, we present a novel methodology for evaluating the stability of a robotic grasp that captures a piece of space debris, a spent rocket stage. We calculate the Intrinsic Stiffness Matrix of a 2-fingered grasp on the surface of an Apogee Kick Motor nozzle and create a stability metric that is a function of the local contact curvature, material properties, applied force, and target mass. We evaluate the efficacy of the stability metric in a simulation and two real robot experiments. The subject of all experiments is a chasing robot that needs to capture a target AKM and pull it back towards the chaser body. In the V-REP simulator, we evaluate four grasping points on three AKM models, over three pulling profiles, using three physics engines. We also use a real robotic testbed with the capability of emulating an approaching robot and a weightless AKM target to evaluate our method over 11 grasps and three pulling profiles. Finally, we perform a sensitivity analysis to demonstrate how a variation on the grasping parameters affects grasp stability. The results of all experiments suggest that the grasp can be stable under slow pulling profiles, with successful pulling for all targets. The presented work offers an alternative way of capturing orbital targets and a novel example of how terrestrial robotic grasping methodologies could be extended to orbital activities.

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“…REMORA, a proposed debris tracking mission, features a CubeSat-sized robotic arm designed specifically for debris manipulation applications. The arm has a maximum intermittent torque output of 0.75 # ⇤ < and a length of 40 2< [2]. These design specifications are used to construct the feasibility envelope of the arm for di erent CubeSat sizes.…”

mentioning

confidence: 99%

Comparative Assessment of Target Capture Techniques for Space Debris Removal with CubeSats

et al. 2021

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The use of CubeSats for space debris removal represents a possible avenue for enabling non-governmental operators to become involved in the maintenance of space. While their small size and inexpensive components reduce barriers to entry for universities and companies, certain technical challenges are magnified by CubeSats' low inertia and power limitations. One such area is target capture, in which an approaching CubeSat must establish a secure contact point with a debris object prior to beginning the detumbling or deorbiting process. This paper discusses nets, harpoons, and robotic arms as three possible strategies for target capture. Each method is examined to identify key regimes of possible feasibility for CubeSat applications. A dynamics model is introduced and utilized to simulate the relative motion of a CubeSat tethered to a debris object, a situation encountered with both harpoon and net capture. Di erences in potential operating regimes are highlighted for the three methods, and conclusions are drawn about their possible realms of e ectiveness. I. Nomenclature Abbreviations ADCSAttitude determination and control subsystem ADR Active debris removal CONOPS Concept of operations TeRBoDOT Tethered Rigid Body Dynamics Observation Tool Subscripts and Superscripts AE (.) Vector (.)| C Indicates quantity (.) at time C

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REMORA CubeSat for large debris rendezvous, attachment, tracking, and collision avoidance

Cited by 7 publications

References 5 publications

Assistive Relative Pose Estimation for On-orbit Assembly using Convolutional Neural Networks

Assistive Relative Pose Estimation for On-orbit Assembly using Convolutional Neural Networks

On-Orbit Robotic Grasping of a Spent Rocket Stage: Grasp Stability Analysis and Experimental Results

Comparative Assessment of Target Capture Techniques for Space Debris Removal with CubeSats

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