State estimation, smoothing and parameter estimation using Kalman and particle filters.

A practical astrodynamics for research and engineering applications

Framework for simulating and evaluating autonomous ship collision avoidance (COLAV) control strategies. Developed as part of the Autoship Centre for Research-based Innovation (SFI). Open sourced in the autumn of 2025.

A high-performance, extensible aircraft GNC framework for Julia

Rtabmap SLAM with realsense D4XX depth camera with ROS2 Humble

Python TERCOM (Terrain Contour Matching) Demo for GPS-Denied Navigation

Python DSMAC (Digital Scene Matching Area Correlator) Demo for GPS-Denied Navigation

Naturalis is a Python framework for orbital simulation and Guidance, Navigation, and Control (GNC) algorithm development.

Access NavAbility(TM) Accelerator features from JuliaLang.

Powered-descent guidance in dependency-free C++17: a from-scratch interior-point SOCP solver driving lossless convexification (3-DoF) and 6-DoF successive convexification, with a rigid-body sim and Monte Carlo dispersion.

Real-time quaternion-based Multiplicative Extended Kalman Filter (MEKF) for IMU orientation estimation with gyroscope bias compensation and 3D visualization.

A Rust Web Server to plan and execute Kerbal Space Program missions using kRPC and kOS

Access NavAbility(TM) Accelerator features from JavaScript.

Reproducible aerocapture + EDL braking trade studies with audit-grade telemetry and verification—compare baselines, enforce q/g/heating constraints, and rerun results from a single config.

Design and Simulation of a Nonlinear Dynamic Inversion–Based Controller for a 6-DOF Air-to-Air Missile Guidance System

Hybrid AI-Powered Satellite Attitude Determination and Control System (ADCS) integrating MEKF, quaternion dynamics, LQR/PD control, and neural network-based attitude control for spacecraft GNC applications.

SCvx 3-DoF powered-descent trajectory solver — flight-grade Rust (no_std, bounded-WCET, C-FFI)

Access NavAbility(TM) Accelerator features from C/C++.

A lightweight Python toolkit for early-stage parafoil design, sizing, and trajectory simulation using low-fidelity aerodynamic models.

SpaceX Starship belly-flop → flip → landing 6-DOF simulation with SCvx successive convexification guidance. 100% Monte Carlo success rate (20/20)! Features: 14-state dynamics, 4-flap differential control allocation, Bouc-Wen hysteresis, 15-state MEKF, 3× Raptor engines. 42/42 tests passed. Python + C++ | SpaceX星舰腹部翻转回收六自由度仿真，SCvx凸优化制导，蒙特卡洛100%成功率