CEU Session #4 - Space Robotics for On-Orbit Servicing and Space Debris Removal

The next ten years will see an unprecedented increase in the number of spacecraft deployed in Earth orbit and the number of commercial ventures operating space assets. The large increase in the number of spacecraft and the large increase in the commercial value of space will lead to renewed interest in robotic on-orbit servicing (OOS) and active debris removal (ADR). The lecture will provide a brief overview over the history of crewed and robotic OOS and discuss the missions planned for the near future. It will then proceed to identify the critical enabling technologies for a future, operational OOS and ADR infrastructure, discuss the technical challenges and present promising concepts and demonstrated technologies that can make routine OOS and ADR a possibility. The focus will be on robotics technologies and spacecraft guidance, navigation and control systems

Airplane Pitch Response to Rapid Configuration Change: Flight Test and Safety Assessment

This paper examines airplane response to rapid flap extension on seven general aviation airplanes. The scenario involves a pilot flying in the traffic pattern becoming distracted, abruptly extending flaps while looking outside the airplane, and failing to notice airspeed and pitch-attitude changes. The airplanes tested reached pitch forces of up to 36 lbf, meeting FAA requirements but exceeding the capability of 55% of the population. Flight data showed a pitch-up to more than 30˚ in 5 s after flap extension, causing airspeed to drop below stall speed for four of the airplanes. At traffic pattern altitudes, stalling an airplane can be fatal. The NTSB lists over 1000 accidents caused by loss of control in the traffic pattern between 1982 and 2017. As general aviation airplanes do not carry flight data recorders, it is unknown how many of those accidents may have involved stalls caused by uncommanded response after flap extension. From the data gathered in flight, it seems possible some were. To improve safety, flight training should prepare students to anticipate rapid pitch changes during flap extension and retraction. In addition, airplane developers could interconnect flaps with the elevator, reduce horizontal tail size, or use a T-tail. The FAA should consider reducing the maximum pitch stick and wheel forces in 14 CFR §23.143 to 20 lbf or less

Real-Time Satellite Component Recognition with YOLO-V5

With the increasing risk of collisions with space debris and the growing interest in on-orbit servicing, the ability to autonomously capture non-cooperative, tumbling target objects remains an unresolved challenge. To accomplish this task, characterizing and classifying satellite components is critical to the success of the mission. This paper focuses on using machine vision by a small satellite to perform image classification based on locating and identifying satellite components such as satellite bodies, solar panels or antennas. The classification and component detection approach is based on “You Only Look Once” (YOLO) V5, which uses Neural Networks to identify the satellite components. The training dataset includes images of real and virtual satellites and additional preprocessed images to increase the effectiveness of the algorithm. The weights obtained from the algorithm are then used in a spacecraft motion dynamics and orbital lighting simulator to test classification and detection performance. Each test case entails a different approach path of the chaser satellite to the target satellite, a different attitude motion of the target satellite, and different lighting conditions to mimic that of the Sun. Initial results indicate that once trained, the YOLO V5 approach is able to effectively process an input camera feed to solve satellite classification and component detection problems in real-time within the limitations of flight computers

Resource-constrained FPGA Design for Satellite Component Feature Extraction

The effective use of computer vision and machine learning for on-orbit applications has been hampered by limited computing capabilities, and therefore limited performance. While embedded systems utilizing ARM processors have been shown to meet acceptable but low performance standards, the recent availability of larger space-grade field programmable gate arrays (FPGAs) show potential to exceed the performance of microcomputer systems. This work proposes use of neural network-based object detection algorithm that can be deployed on a comparably resource-constrained FPGA to automatically detect components of non-cooperative, satellites on orbit. Hardware-in-the-loop experiments were performed on the ORION Maneuver Kinematics Simulator at Florida Tech to compare the performance of the new model deployed on a small, resource-constrained FPGA to an equivalent algorithm on a microcomputer system. Results show the FPGA implementation increases the throughput and decreases latency while maintaining comparable accuracy. These findings suggest future missions should consider deploying computer vision algorithms on space-grade FPGAs.Comment: 9 pages, 7 figures, 4 tables, Accepted at IEEE Aerospace Conference 202

Autonomous Rendezvous with Non-cooperative Target Objects with Swarm Chasers and Observers

Space debris is on the rise due to the increasing demand for spacecraft for com-munication, navigation, and other applications. The Space Surveillance Network (SSN) tracks over 27,000 large pieces of debris and estimates the number of small, un-trackable fragments at over 1,00,000. To control the growth of debris, the for-mation of further debris must be reduced. Some solutions include deorbiting larger non-cooperative resident space objects (RSOs) or servicing satellites in or-bit. Both require rendezvous with RSOs, and the scale of the problem calls for autonomous missions. This paper introduces the Multipurpose Autonomous Ren-dezvous Vision-Integrated Navigation system (MARVIN) developed and tested at the ORION Facility at Florida Institution of Technology. MARVIN consists of two sub-systems: a machine vision-aided navigation system and an artificial po-tential field (APF) guidance algorithm which work together to command a swarm of chasers to safely rendezvous with the RSO. We present the MARVIN architec-ture and hardware-in-the-loop experiments demonstrating autonomous, collabo-rative swarm satellite operations successfully guiding three drones to rendezvous with a physical mockup of a non-cooperative satellite in motion.Comment: Presented at AAS/AIAA Spaceflight Mechanics Meeting 2023, 17 pages, 9 figures, 3 table

Performance Study of YOLOv5 and Faster R-CNN for Autonomous Navigation around Non-Cooperative Targets

Autonomous navigation and path-planning around non-cooperative space objects is an enabling technology for on-orbit 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 learning-based 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.Comment: 12 pages, 10 figures, 9 tables, IEEE Aerospace Conference 202

Evaluation of the DREAM Technique for a High-Throughput Deorphanization of Chemosensory Receptors in Drosophila

In the vinegar fly Drosophila melanogaster, the majority of olfactory receptors mediating the detection of volatile chemicals found in their natural habitat have been functionally characterized (deorphanized) in vivo. In this process, receptors have been assigned ligands leading to either excitation or inhibition in the olfactory sensory neuron where they are expressed. In other, non-drosophilid insect species, scientists have not yet been able to compile datasets about ligand–receptor interactions anywhere near as extensive as in the model organism D. melanogaster, as genetic tools necessary for receptor deorphanization are still missing. Recently, it was discovered that exposure to artificially high concentrations of odorants leads to reliable alterations in mRNA levels of interacting odorant receptors in mammals. Analyzing receptor expression after odorant exposure can, therefore, help to identify ligand–receptor interactions in vivo without the need for other genetic tools. Transfer of the same methodology from mice to a small number of receptors in D. melanogaster resulted in a similar trend, indicating that odorant exposure induced alterations in mRNA levels are generally applicable for deorphanization of interacting chemosensory receptors. Here, we evaluated the potential of the DREAM (Deorphanization of receptors based on expression alterations in mRNA levels) technique for high-throughput deorphanization of chemosensory receptors in insect species using D. melanogaster as a model. We confirmed that in some cases the exposure of a chemosensory receptor to high concentration of its best ligand leads to measureable alterations in mRNA levels. However, unlike in mammals, we found several cases where either confirmed ligands did not induce alterations in mRNA levels of the corresponding chemosensory receptors, or where gene transcript-levels were altered even though there is no evidence for a ligand–receptor interaction. Hence, there are severe limitations to the suitability of the DREAM technique for deorphanization as a general tool to characterize olfactory receptors in insects

Trajectory Energy Management Systems for eVTOL Vehicles: Modeling, Simulation and Testing

Presented at AIAA AVIATION Forum 2022The rise of electric aircraft propulsion methods, the increased use of automated and integrated flight control systems, and the envisioned use of personal Vertical Takeoff and Landing (VTOL) vehicles in urban environments lead to novel technical and regulatory challenges for aircraft manufacturers, certification authorities and operators. The combination of electric propulsion, where energy reserves and powertrain performance are highly sensitive to the environment, and VTOL, where the aircraft cannot simply glide to an emergency landing, generates the need for Trajectory Energy Management (TEM). The TEM task involves the manipulation of flight and propulsion controls to achieve a planned flight profile. The TEM system must provide the pilot or automated control system with guidance cues to achieve a planned flight profile, to maintain an energy-optimal trajectory, to avoid deviations from the flight plan causing increases in energy and power consumption, and to mitigate the risk of energy completion. As the pilot must manage both the energy source and flight dynamics energy state, the TEM system must provide sufficient information to the pilot, so that the pilot can perform the mission. This research is intended to define some requirements for energy management such that the pilot can safely accomplish an intended profile and land with enough energy reserves. These requirements must be defined based on prototype algorithm development, simulation results, and flight test data