This repository contains a Pygame-based simulation and experimental code for evaluating path-planning and tracking control methods for planetary rovers. It implements and compares:

• Improved A* with motion primitives (rover_control_sim_fast.py) — global planner that expands constant-curvature motion primitives in $(x,y,\theta)$ space and uses a terrain-aware cost model.
• Grid-based A* (rover_control_sim_astar.py) — baseline classical 8-connected grid planner with per-cell terrain costs.
• Arc-based local controller — reactive controller that samples short circular arcs, scores them by multi-objective criteria (progress, alignment, safety), and executes the best arc.
• Utilities to generate synthetic DEMs, place procedural rocks, visualize simulation, and plot experimental results.

This project was developed as a case study for the research paper: "An improved path planning and tracking control method for planetary exploration rovers with traversable tolerance" (H. Zhang, F. Jiang, Q. Li — Biomimetic Intelligence and Robotics, 2025).

Features

• Procedural terrain generator (DEM with Gaussian bumps + noise)
• Per-cell traversability / cost model (slope, roughness, rock proximity)
• Motion-primitive-based A* planner (constant-curvature arcs)
• Baseline grid A* planner for comparisons
• Arc-based local tracking controller
• Pygame visualization with interactive controls
• Logging utilities and plotting scripts to reproduce figures (e.g., planning time histogram)
• Easy-to-tune parameters for planner, controller, and terrain

Screenshots & Figures

This section shows the main visualization assets used in the report and demo runs.

Figure: Planning time histogram

The following chart summarizes planning-time distributions across 40 trials for the two planners (Classical Avs Improved A). The chart was generated by plot_planning_time_hist.py (synthetic/demo data).
File: media/chart/planning_time_histogram.png

Figure 1 — Planning time histogram (Classical A vs Improved A*). The improved planner shows a ~30% reduction in mean planning time in our synthetic experiments.*

Screenshot gallery

Interactive simulator screenshots (captured from example runs). Click an image to view full-size.

Requirements

Tested with Python 3.8+ on Linux

Install dependencies with:

python -m venv venv
source venv/bin/activate   # or `venv\Scripts\activate` on Windows
pip install -r requirements.txt

Quick start — run the simulator

1. Clone repository:

git clone https://github.com/aditya-an1l/mars-rover-path-finder.git
cd mars-rover-path-finder

2. (Optional) Put a rover.png sprite into assets/ (if absent, a triangle will be used).

3. Run improved A* (motion primitives):

python rover_control_sim_fast.py

Or run classical grid A* baseline:

python rover_control_sim_astar.py

Controls (in-simulator)

• Left-click: set goal position
• Arrows / W A S D: drive rover manually (forward/back, rotate)
• p: plan a global path from rover to goal (runs the active planner)
• f: toggle auto-follow (rover follows planned path using arc-based controller)
• r: regenerate terrain + rocks (new random seed)
• Esc or q: quit

HUD displays: rover pose (x,y,heading), current goal, path length (nodes), auto-follow status, and instructions.

Parameters & tuning

Key parameters are defined at the top of the scripts:

• DEM: GRID_W, GRID_H, bumps, noise, amplitude, seed
• Traversability: dzmax, phimax, rnmax
• Motion primitives: lengths, radii, samples per primitive
• Planner: heuristic_weight, heading_bins, max_time, max_iterations
• Controller: arc sampling (v and omega ranges), time-step for arc sim, weights for evaluation

For reproducible experiments, fix the random seeds in the DEM and rock-place functions.

File an issue / contribute

Contributions and bug reports are welcome. Suggested workflow:

1. Fork the repo
2. Create a feature branch: git checkout -b feat/my-feature
3. Commit and push, then open a Pull Request

Please include:

• Short description of the change
• Test/logs illustrating behavior
• Any parameter defaults changed

License

This repository is released under the MIT License. See LICENSE.

Citation

If you use this code or results in research, please cite the project and the underlying paper:

BibTeX entry (see CITATION.bib):

CITATION.bib

@article{zhang2025improved,
  title={An improved path planning and tracking control method for planetary exploration rovers with traversable tolerance},
  author={Zhang, Haojie and Jiang, Feng and Li, Qing},
  journal={Biomimetic Intelligence and Robotics},
  volume={5},
  number={2},
  pages={100219},
  year={2025},
  doi={10.1016/j.birob.2025.100219}
}