link to notion page: https://www.notion.so/PhyVista-2ef9b87cb91380ed8148da2aa6e3343b?source=copy_link

PhyVista — GitHub Repository Documentation

What this project does:

This project builds a physics-based simulation to study how steering and control behave when the balance between propulsion and friction changes, especially under reduced-gravity conditions. Instead of treating motion as animation, the system models a vehicle’s dynamics using real equations of motion and advances them over time using numerical integration.

At its core, the simulator represents the vehicle as a dynamic system whose state evolves based on forces, torques, steering inputs, gravity, and surface traction. By adjusting parameters such as gravity level, friction coefficient, and propulsion force, the project reveals how steering authority, stability, and controllability degrade or improve in non-Earth environments.

Control algorithms—starting with PID and extendable to modern state-space methods—are applied in closed loop to maintain heading or follow trajectories. This allows direct comparison between different control strategies under identical physical conditions. The simulation is deterministic and experiment-driven, enabling repeatable parameter sweeps, stability analysis, and performance evaluation.

The project’s value lies in its role as a controlled laboratory for understanding vehicle dynamics rather than as a visual demo. It connects physics, mathematics, and computer science by translating continuous mechanical laws into discrete, testable algorithms. Practically, the framework is relevant to planetary rovers, low-gravity vehicles, autonomous systems, and any application where steering performance depends critically on traction and environmental constraints.

To Learn:

<aside> 💡

<aside> 💡

Clean, structured, point-wise. This is the complete learning map for your project — code and non-code — ordered from foundation to full maturity. Nothing extra, nothing missing.

I. Mathematics (foundation layer)

1. Calculus

   • derivatives
   • integrals
   • partial derivatives

2. Differential equations

   • first-order ODEs
   • second-order ODEs
   • discretization

3. Linear algebra

   • vectors as state
   • matrices
   • eigenvalues (intuition)

II. Physics (model layer)

4. Classical mechanics

   • kinematics
   • Newton’s laws
   • rotational motion

5. Vehicle dynamics (2D)

   • bicycle model
   • steering geometry
   • yaw dynamics

6. Friction & traction

   • static vs kinetic friction
   • slip
   • low-gravity effects

III. Control theory (intelligence layer)

7. Control fundamentals

   • feedback systems
   • stability
   • response characteristics

8. PID control

   • tuning
   • saturation
   • anti-windup

9. State-space control

   • state vectors
   • system matrices
   • LQR (conceptual)

IV. Numerical simulation (reliability layer)

10. Time discretization

    • fixed timestep
    • numerical error

11. Integration methods

    • Euler
    • semi-implicit Euler
    • Runge–Kutta (RK4)

12. Stability & error

    • divergence detection
    • timestep sensitivity

V. Programming – Core (execution layer)

13. Python fundamentals

    • clean syntax
    • functions
    • modules

14. NumPy

    • arrays
    • vectorized math
    • state handling

15. Simulation loop design

    • update order
    • deterministic behavior

VI. Programming – Architecture (scaling layer)

16. Object-oriented design

    • classes for system parts
    • composition over inheritance

17. Abstraction & interfaces

    • controller interfaces
    • physics engine separation

18. Configuration management

    • parameters
    • experiment setup

VII. Programming – Control & Algorithms

19. Control implementation

    • PID from scratch
    • discrete control logic

20. State-space simulation

    • matrix updates
    • discrete dynamics

VIII. Testing & experiments (credibility layer)

21. Unit testing

    • physics validation
    • controller stability

22. Parameter sweeps

    • batch simulations
    • sensitivity analysis

23. Logging & data handling

    • structured logs
    • reproducibility

IX. Visualization & frontend (interaction layer)

24. Scientific visualization

    • Matplotlib
    • time-series plots
    • phase plots

25. Interactive tuning

    • sliders
    • live parameter updates

26. Optional GUI

    • tkinter / PyQt
    • simulation controls

27. Optional web frontend

    • Three.js
    • backend–frontend separation

X. Software engineering discipline (professional layer)

28. Version control

    • Git
    • experiment tracking

29. Documentation

    • README
    • model explanation

30. Code quality

    • modularity
    • readability

XI. Advanced extensions (only after mastery)

31. Estimation

    • sensor noise
    • Kalman filter

32. Path planning

    • trajectories
    • constraints

33. Autonomy

    • closed-loop navigation

Core principle (do not ignore)

Every topic must change the simulator’s behavior, not just your notes.

If learning doesn’t improve realism, stability, or insight — it’s premature.

This roadmap is enough to take your project from student-level to research-grade.

</aside>

</aside>

Screenshots dated [22-01-2026]:

<aside> 💡

rest work ill do later im feeling sleepy now all of this is dated for 22-01-206 01:27 AM…

</aside>