Hardware-in-the-Loop Testing for Suborbital Flights of the Safe and Precise Landing Integrated Capabilities Evolution (SPLICE) Project Technologies

Segregated FLS Processing Cores for V/STOL Autonomous Landing Guidance Assistant System Using FPGA

“…In addition, Table II presents further countermeasures that have been considered for defending the ALGAS4 system against cyberattacks and making it more durable to withstand and recover from error conditions during any fault injection attacks by avoiding the requirement to interchange data with any sort of external memory while processing the input sensory data from the two sensors. This feature is achieved by storing all the required coefficients, The Safe and Precise Landing System in [22] The Safe and Precise Landing System in [23] The Safe and Precise Landing System in [24] Main algorithm(s) to support safe landing parameters, and weights for processing all the different data stages of the ALGAS4 architecture using internal soft registers within the silicon fabric of the FPGA. This also includes the FIFO-like memory storage buffers, which are used to implement the APMU.…”

“…To emphasize the main advantages of the ALGAS4 system over the state-of-the-art systems, we summarize its key aspects, as depicted in Table III. Despite the impressive technologies and implemented ideas that have been used in the References [22,24], they mainly depend on cameras to enhance safe drone landing mechanisms. This is effective only for indoor environments, but it is not suitable for most of the outdoor landing situations.…”

“…Many research contributions are using image processing and integrated computer vision to strengthen the landing safety of drones. Also, Reference [22] outlines a method and design for a system that manages multiple data streams through a multicore asymmetric processing architecture, aiming to eliminate data interruptions directed at the application processors. This design specifically supports a controlled environment for flight software within NASA's Safe and Precise Landing -Integrated Capabilities Evolution (SPLICE) project.…”

Feature-based Learning for Optimal Abort Guidance

“…Our work in [ 22 ] built a customized quadcopter to validate the computational performance of a fuel-optimal powered descent guidance algorithm. Although scale-model rocket powered vehicles have been developed for testing EDL missions [ 28 ], [ 29 ] with the purpose of increasing the technology readiness level (TRL), the complexities and costs involved in the scale-model tests make them not viable until the late stages of a mission. The testbed based on a customized quadcopter allows rapid validation of the proposed method in real-world experiments without sacrificing project budgets or safety to the degree that a scale-model would.…”