Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2501.07616

The Ingenuity Mars Helicopter Specified and Analyzed with the Real-time Mode-aware Dataflow Model

Guillaume Roumage·Selma Azaiez·Cyril Faure·Stéphane Louise
View original source ↗Read source document ↗Export metadata ↓

Abstract

Ingenuity is an autonomous Cyber-Pysical System (CPS) that has successfully completed more than 70 flights over Mars between 2021 and 2024. Ensuring the safety of its mission is paramount, as any failure could result in catastrophic economic damage and significant financial losses. Dataflow Models of Computation and Communication (DF MoCCs) serve as a formal framework for specifying and analyzing the timing behavior of such CPSs. In particular, the Real-time Mode-aware Dataflow (RMDF) model is highly suitable to specify and analyze real-time and mode-dependent Cyber-Physical Systems (CPSs) like Ingenuity. This paper showcases the application of RMDF for the specification and analysis of Ingenuity. We propose a dataflow specification of Ingenuity, analyze its timing behavior, and provide a feasibility test. Finally, we proposed a plausible explanation of the timing anomaly that occurred during the sixth flight of Ingenuity.

Explore related subjects

eess.SYcs.SY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Guillaume Roumage, Selma Azaiez, Cyril Faure, Stéphane Louise. 2025-01-13. The Ingenuity Mars Helicopter Specified and Analyzed with the Real-time Mode-aware Dataflow Model. https://arxiv.org/abs/2501.07616

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Ensuring Stability of Non-Minimal Modes in Input-Output Data-Driven Representation

Many recent data-driven control approaches for linear time-invariant systems are based on output trajectory prediction using input-output data matrices. The system dynamics described by this predictor, which we refer to as the input-output data-driven representation, yields non-unique autoregressive with exogenous inputs (ARX) models having possibly unstable non-minimal modes. In this note, we show that the stability of these non-minimal modes is ensured by a certain choice of ARX model, which coincides with the minimum-norm least-squares predictor using the Moore-Penrose inverse of the data matrix. This stability guarantee holds regardless of the underlying system's stability. Moreover, the stability persists under sufficiently small noise in data when a suitably truncated Moore-Penrose inverse is used. Consequently, the ARX model need not be reduced to the true system order in order to avoid unstable additional modes.

eess.SY↗
arXiv2026

Optimization-Based Formation Flight on Libration Point Orbits

A model predictive control (MPC) framework is developed for station-keeping in spacecraft formation flight along libration point orbits. At each control period, the MPC policy solves a multi-vehicle optimal control problem (MVOCP) that tracks a reference trajectory, while enforcing path constraints on the relative motion of the formation. The control policy makes use of a limited set of control nodes consistent with operational constraints that allow only a small number of maneuver opportunities per revolution. To promote recursive feasibility, path constraints are progressively tightened across the prediction horizon. An isoperimetric reformulation of the constraints is used to prevent inter-sample violations. The resulting MVOCP is a nonconvex program, which is solved via sequential convex programming. The proposed approach is evaluated in a high-fidelity ephemeris model under uncertainties for a formation along the near-rectilinear halo orbit (NRHO), and subject to path constraints on inter-spacecraft separation and relative Sun phase angle. The results demonstrate maintenance of a spacecraft formation that satisfies the path constraints with realistic cumulative propellant consumption.

eess.SY↗
arXiv2026

Certificates Synthesis for A Class of Observational Properties in Stochastic Systems: A Unified Approach

In this paper, we investigate the probabilistic formal verification of stochastic dynamical systems over continuous state spaces. Motivated by problems in state estimation and information-flow security, we introduce the notion of observational properties, which characterize the inferences an external observer can draw from system outputs. These properties are formulated as probabilistic hyperproperties based on HyperLTL over finite traces, yielding a unified framework that subsumes several existing notions studied separately in the literature. We reduce the verification problem to reachability analysis over an augmented structure that integrates the system dynamics with an automaton representation of the specification. Building on this construction, we develop stochastic barrier certificates that provide probabilistic guarantees for property satisfaction while avoiding explicit state-space discretization. The effectiveness of the proposed framework is demonstrated through a case study.

eess.SY↗