rust-gnc 0.2.0

A no_std GNC (Guidance, Navigation, and Control) library for autonomous systems in Rust.
Documentation

Rust GNC (Guidance, Navigation, and Control)

A high-assurance, no_std library for flight control systems. Designed for performance, safety, and readability.

Features

  • Type-Safe Physics: Custom units for Radians, Position, and Velocity to prevent unit-mismatch bugs.
  • Modular Control: Generic AxisProcessor allowing any Filter implementation (Complementary, Kalman, etc.).
  • Safety-First: Built-in ArmingState to prevent integral windup and accidental motor spins.
  • Shortest-Path Yaw: Automatic angle normalization for circular navigation.

Architecture

The library is organized by physical responsibility:

  • units/: Core physical primitives and operator overloading.
  • filters/: Signal processing (Sensor Fusion).
  • control/: PID loops and Motor Mixing.

🛠 Supported Configurations

  • Airframe: Quadcopter (X-Configuration).
  • Mixing: Standard 4-motor thrust distribution.
  • Coordinate System: NED (North-East-Down) frame.

Quick Start

let mut stabilizer = Stabilizer::new(...);
stabilizer.set_armed(ArmingState::Armed);
let motors = stabilizer.tick(roll_in, pitch_in, yaw_in, target, throttle, dt);

🚀 Roadmap

The development of Rust GNC (Guidance, Navigation, and Control) is divided into four strategic phases, moving from basic stability to autonomous intelligence.

Phase 1: Mission Safety & Reliability

  • [ ] Failsafe System: State-machine logic for Signal Loss (RX), Battery Low, and IMU Failure.
  • [ ] Emergency Protocols: Automated "Land-in-Place" and "Return-to-Home" (RTH) routines.
  • [ ] Telemetry Logging: High-speed, no_std compatible binary logging for post-flight black-box analysis.

Phase 2: Advanced Control & Navigation

  • [ ] Acro (Rate) Mode: A high-performance controller mapping inputs to angular velocity targets ($deg/s$).
  • [ ] Position Hold: Implementation of a cascaded outer-loop for GPS-based coordinate maintenance.
  • [ ] Vertical Control: Altitude hold using Barometer and Z-axis Accelerometer fusion.

Phase 3: Signal Processing Suite

  • [ ] Extended Kalman Filter (EKF): Sophisticated 6-DOF/9-DOF fusion to replace simple complementary filters.
  • [ ] Dynamic Notch Filters: FFT-based vibration suppression to isolate motor noise from control signals.
  • [ ] Alternative Controllers: Support for LQR (Linear Quadratic Regulator) and Sliding Mode Control.

Phase 4: Intelligent Flight (AI/ML)

  • [ ] Neural Attitude Controller: Augmenting PID loops with Reinforcement Learning (RL) agents for non-linear flight regimes.
  • [ ] System Identification: Automated "Auto-Tune" utility to calculate vehicle moments of inertia and optimal gains.
  • [ ] Obstacle Avoidance: Integration of LiDAR/ToF sensor arrays for autonomous path planning.