Event-Driven Simulation for Rapid Iterative Development of Distributed Space Flight Software
- URL: http://arxiv.org/abs/2505.12502v1
- Date: Sun, 18 May 2025 17:32:40 GMT
- Title: Event-Driven Simulation for Rapid Iterative Development of Distributed Space Flight Software
- Authors: Toby Bell, Simone D'Amico,
- Abstract summary: This paper presents the design, development, and application of a novel space simulation environment.<n>The environment combines the flexibility, determinism, and observability of software-only simulation with the fidelity and depth normally attained only by real-time hardware-in-the-loop testing.
- Score: 4.14360329494344
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper presents the design, development, and application of a novel space simulation environment for rapidly prototyping and testing flight software for distributed space systems. The environment combines the flexibility, determinism, and observability of software-only simulation with the fidelity and depth normally attained only by real-time hardware-in-the-loop testing. Ultimately, this work enables an engineering process in which flight software is continuously improved and delivered in its final, flight-ready form, and which reduces the cost of design changes and software revisions with respect to a traditional linear development process. Three key methods not found in existing tools enable this environment's novel capabilities: first, a hybrid event-driven simulation architecture that combines continuous-time and discrete-event simulation paradigms; second, a lightweight application-layer software virtualization design that allows executing compiled flight software binaries while modeling process scheduling, input/output, and memory use; and third, high-fidelity models for the multi-spacecraft space environment, including for wireless communication, relative sensing such as differential GPS and cameras, and flight computer health metrics like heap exhaustion and fragmentation. The simulation environment's capabilities are applied to the iterative development and testing of two flight-ready software packages: the guidance, navigation, and control software for the VISORS mission, and the Stanford Space Rendezvous Laboratory software kit for rendezvous and proximity operations. Results from 33 months of flight software development demonstrate the use of this simulation environment to rapidly and reliably identify and resolve defects, characterize navigation and control performance, and scrutinize implementation details like memory allocation and inter-spacecraft network protocols.
Related papers
- A Unified Experimental Architecture for Informative Path Planning: from Simulation to Deployment with GuadalPlanner [69.43049144653882]
This paper introduces a unified architecture that decouples high-level decision-making from vehicle-specific control.<n>The proposed architecture is realized through GuadalPlanner, which defines standardized interfaces between planning, sensing, and vehicle execution.
arXiv Detail & Related papers (2026-02-11T10:02:31Z) - ECLIPSE: An Evolutionary Computation Library for Instrumentation Prototyping in Scientific Engineering [1.6736150071247582]
We present ECLIPSE, an evolutionary framework built to interface directly with complex, domain-specific simulation tools.<n>We demonstrate ECLIPSE across several active space-science applications, including evolved 3D antennas and spacecraft optimized for drag reduction in very low Earth orbit.
arXiv Detail & Related papers (2026-01-08T16:45:11Z) - Neural surrogates for designing gravitational wave detectors [21.601009915564344]
We show how neural surrogate models can significantly reduce reliance on traditional, CPU-based simulators.<n>We train a neural network to surrogate the gravitational wave physics simulator Finesse, which was developed by the LIGO community.<n>Our algorithm loops between training the surrogate, inverse designing new experiments, and verifying their properties with the slow simulator for further training.
arXiv Detail & Related papers (2025-11-24T17:58:59Z) - Trajectory Design for UAV-Based Low-Altitude Wireless Networks in Unknown Environments: A Digital Twin-Assisted TD3 Approach [62.11847362756054]
Unmanned aerial vehicles (UAVs) are emerging as key enablers for low-altitude wireless network (LAWN)<n>We propose a digital twin (DT)-assisted training and deployment framework.<n>In this framework, the UAV transmits integrated sensing and communication signals to provide communication services to ground users, while simultaneously collecting echoes that are uploaded to the DT server to progressively construct virtual environments (VEs)<n>These VEs accelerate model training and are continuously updated with real-time UAV sensing data during deployment, supporting decision-making and enhancing flight safety.
arXiv Detail & Related papers (2025-10-28T10:05:53Z) - Hybrid Neural-MPM for Interactive Fluid Simulations in Real-Time [57.30651532625017]
We present a novel hybrid method that integrates numerical simulation, neural physics, and generative control.<n>Our system demonstrates robust performance across diverse 2D/3D scenarios, material types, and obstacle interactions.<n>We promise to release both models and data upon acceptance.
arXiv Detail & Related papers (2025-05-25T01:27:18Z) - Bridging the Gap: Physical PCI Device Integration Into SystemC-TLM Virtual Platforms [0.16492989697868893]
Virtual Platforms (VPs) serve as a platform to execute and debug the unmodified target software at an early design stage.<n>VPs need to provide high simulation speed to ensure the target software executes within a reasonable time.<n>This paper introduces a novel approach for integrating real Peripheral Component Interconnect ( PCI) devices into SystemC-TLM-2.0-based VPs.
arXiv Detail & Related papers (2025-05-21T14:46:41Z) - FlightForge: Advancing UAV Research with Procedural Generation of High-Fidelity Simulation and Integrated Autonomy [2.6003704171754416]
We propose the novel FlightForge UAV open-source simulator.<n>It offers advanced rendering capabilities, diverse control modalities, and, foremost, procedural generation of environments.<n>The simulator is already integrated with a fully autonomous UAV system capable of long-range flights in cluttered unknown environments.
arXiv Detail & Related papers (2025-02-07T16:05:17Z) - Open-Source High-Speed Flight Surrogate Modeling Framework [0.0]
High-speed flight vehicles, which travel much faster than the speed of sound, are crucial for national defense and space exploration.
accurately predicting their behavior under numerous, varied flight conditions is a challenge and often expensive.
The proposed approach involves creating smarter, more efficient machine learning models.
arXiv Detail & Related papers (2024-11-06T01:34:06Z) - A Realistic Simulation Framework for Analog/Digital Neuromorphic Architectures [73.65190161312555]
ARCANA is a software spiking neural network simulator designed to account for the properties of mixed-signal neuromorphic circuits.<n>We show how the results obtained provide a reliable estimate of the behavior of the spiking neural network trained in software, once deployed in hardware.
arXiv Detail & Related papers (2024-09-23T11:16:46Z) - Gaussian Splatting to Real World Flight Navigation Transfer with Liquid Networks [93.38375271826202]
We present a method to improve generalization and robustness to distribution shifts in sim-to-real visual quadrotor navigation tasks.
We first build a simulator by integrating Gaussian splatting with quadrotor flight dynamics, and then, train robust navigation policies using Liquid neural networks.
In this way, we obtain a full-stack imitation learning protocol that combines advances in 3D Gaussian splatting radiance field rendering, programming of expert demonstration training data, and the task understanding capabilities of Liquid networks.
arXiv Detail & Related papers (2024-06-21T13:48:37Z) - Learning to Fly in Seconds [7.259696592534715]
We show how curriculum learning and a highly optimized simulator enhance sample complexity and lead to fast training times.
Our framework enables Simulation-to-Reality (Sim2Real) transfer for direct control after only 18 seconds of training on a consumer-grade laptop.
arXiv Detail & Related papers (2023-11-22T01:06:45Z) - Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous
Driving Research [76.93956925360638]
Waymax is a new data-driven simulator for autonomous driving in multi-agent scenes.
It runs entirely on hardware accelerators such as TPUs/GPUs and supports in-graph simulation for training.
We benchmark a suite of popular imitation and reinforcement learning algorithms with ablation studies on different design decisions.
arXiv Detail & Related papers (2023-10-12T20:49:15Z) - Continual learning autoencoder training for a particle-in-cell
simulation via streaming [52.77024349608834]
upcoming exascale era will provide a new generation of physics simulations with high resolution.
These simulations will have a high resolution, which will impact the training of machine learning models since storing a high amount of simulation data on disk is nearly impossible.
This work presents an approach that trains a neural network concurrently to a running simulation without data on a disk.
arXiv Detail & Related papers (2022-11-09T09:55:14Z) - Functional Simulation of Real-Time Quantum Control Software [1.005130974691351]
We show that our simulation infrastructure simulates kernels 6.9 times faster on average compared to execution on hardware.
The position of the timeline cursor is simulated with an average accuracy of 97.9% when choosing the appropriate configuration.
arXiv Detail & Related papers (2022-10-25T22:11:32Z) - Data-Driven Offline Optimization For Architecting Hardware Accelerators [89.68870139177785]
We develop a data-driven offline optimization method for designing hardware accelerators, dubbed PRIME.
PRIME improves performance upon state-of-the-art simulation-driven methods by about 1.54x and 1.20x, while considerably reducing the required total simulation time by 93% and 99%, respectively.
In addition, PRIME also architects effective accelerators for unseen applications in a zero-shot setting, outperforming simulation-based methods by 1.26x.
arXiv Detail & Related papers (2021-10-20T17:06:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.