Autonomous navigation and path-planning around non-cooperative space objects is an enabling technology for onorbit servicing and space debris removal systems. The navigation task includes the determination of target object motion, the identification of target object features suitable for grasping, and the identification of collision hazards and other keep-out zones. Given this knowledge, chaser spacecraft can be guided towards capture locations without damaging the target object or without unduly the operations of a servicing target by covering up solar arrays or communication antennas. One way to autonomously achieve target identification, characterization and feature recognition is by use of artificial intelligence algorithms. This paper discusses how the combination of cameras and machine learning algorithms can achieve the relative navigation task. The performance of two deep learningbased object detection algorithms, Faster Region-based Convolutional Neural Networks (R-CNN) and You Only Look Once (YOLOv5), is tested using experimental data obtained in formation flight simulations in the ORION Lab at Florida Institute of Technology. The simulation scenarios vary the yaw motion of the target object, the chaser approach trajectory, and the lighting conditions in order to test the algorithms in a wide range of realistic and performance limiting situations. The data analyzed include the mean average precision metrics in order to compare the performance of the object detectors. The paper discusses the path to implementing the feature recognition algorithms and towards integrating them into the spacecraft Guidance Navigation and Control system.TABLE OF CONTENTS 1. INTRODUCTION .
Several methods have been used to optimize performance of magnetic elastomers by controlling the microstructure, such as magnetic annealing. Another way to introduce anisotropy is Fused Deposition Modeling (FDM), which has been shown to manipulate the magnetic anisotropy of rigid printed parts. However, the use of flexible composite materials has not yet been explored due to additional processing challenges. The primary goal of this study is to demonstrate tunable anisotropy of these materials via 3D printed structures without post-processing as a viable means to tune the performance of magnetic elastomer materials. Here, FDM structures were printed with thermoplastic polyurethane (TPU) polymer and either iron, carbonyl iron, or magnetite particulate. In order to determine the relative effect of different parameters on the magnetic properties, a series of samples were printed combining each material type with different aspect ratios, infill percentages, and infill orientations. A Vibrating Sample Magnetometer (VSM) was used to obtain magnetic hysteresis loops in order to compare the magnetic susceptibility between samples. Results demonstrated that FDM provides a method of achieving the directional signature of magnetic annealing without requiring any post-processing; instead, this manifests through the anisotropy of the part’s internal structure. As such, this concept is referred to as infill magnetic annealing (IMA). These variables were found to form a continuum of tunable magnetic responses. Additionally, the chosen particulate transfers its magnetic signature to the composite material. Overall, the highly customizable and nuanced characteristics of 3D-printed magnetic elastomer structures will allow for its application in a broad range of emerging magneto-mechanical applications such as magnetic actuation and soft robotics.
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