Readme file added.

README.md

@@ -1,3 +1,181 @@
 ---
 license: apache-2.0
+library_name: pytorch
+tags:
+- robotics
+- point-cloud
+- terrain-analysis
+- autonomous-navigation
+- 3d-perception
+- deep-learning
+- PointNet
 ---
+
+# TerrainFreeSpaceNet
+
+TerrainFreeSpaceNet is a deep learning model designed to predict **terrain free-space from 3D point cloud data**.  
+It enables robots to estimate terrain traversability in uneven environments using raw point cloud observations.
+
+This model is based on a **PointNet-style neural network** that processes unordered 3D points and outputs a normalized free-space score.
+
+---
+
+# Overview
+
+Autonomous ground robots operating in outdoor environments often encounter:
+
+- uneven terrain
+- vegetation
+- slopes and depressions
+- irregular obstacles
+
+Traditional geometric free-space detection methods may struggle in these environments.
+
+TerrainFreeSpaceNet learns to **estimate terrain traversability directly from 3D point clouds**, making it suitable for:
+
+- outdoor robotics
+- off-road navigation
+- agricultural robots
+- exploration robots
+
+---
+
+## Model Architecture
+
+The model uses a **PointNet-style architecture** consisting of:
+
+1. Shared MLP layers implemented using Conv1D
+2. Batch normalization and ReLU activation
+3. Global max pooling
+4. Fully connected regression layers
+5. Sigmoid output for normalized free-space score
+
+Input shape:
+
+    [B, 3, N]
+
+Where:
+
+- `B` = batch size  
+- `3` = (x,y,z) coordinates  
+- `N` = number of sampled points  
+
+Output:
+
+    [B,1]
+
+Free-space score in range:
+
+- 0 → non-traversable
+- 1 → highly traversable
+
+
+---
+
+## Input Format
+
+The model expects **CSV point cloud input**.
+
+Example:
+
+    x,y,z
+    0.12,0.31,0.02
+    0.15,0.34,0.05
+    0.18,0.29,0.04
+
+
+---
+
+## Example Inference
+
+Example usage using the provided inference script.
+
+```python
+from inference import run_inference
+
+score = run_inference("sample_input.csv", checkpoint="model.pt")
+
+print("Free space score:", score)
+```
+
+Example output:
+
+```
+Free space score: 0.84
+```
+
+## Training
+
+The model was trained using frame-level 3D point cloud samples.
+
+Each frame contains:
+
+```
+frame_id,x,y,z,free_space
+```
+Where:
+
+- `frame_id` identifies the point cloud frame
+
+- `free_space` represents terrain traversability score
+
+## Applications
+
+TerrainFreeSpaceNet can be used for:
+
+- terrain-aware robot navigation
+
+- autonomous ground vehicles
+
+- off-road robotics
+
+- agricultural robots
+
+- exploration robots
+
+- rough terrain mobility analysis
+
+## Limitations
+
+- The model currently assumes XYZ coordinates only
+
+- Performance depends on training dataset diversity
+
+- Large terrain variations may require retraining
+
+- Real-time deployment requires optimized inference
+
+## Related Research
+
+This model was developed as part of research on:
+
+**Autonomous robot navigation in uneven terrain using 3D perception**.
+
+It is designed to integrate with the **Agoraphilic-3D Navigation Framework**.
+
+Repository
+
+Full project code available here:
+
+https://github.com/dinusharg/TerrainFreeSpaceNet
+
+
+
+## Citation
+
+If you use this model in research, please cite:
+
+```bibtex
+@article{10.1007/s12555-025-0624-2,
+   author = {Gunathilaka, W. M. Dinusha and Kahandawa, Gayan and Ibrahim, M. Yousef and Hewawasam, H. S. and Nguyen, Linh},
+   title = {Agoraphilic-3D Net: A Deep Learning Method for Attractive Force Estimation in Mapless Path Planning for Unstructured Terrain},
+   journal = {International Journal of Control, Automation and Systems},
+   volume = {23},
+   number = {12},
+   pages = {3790-3802},
+   ISSN = {2005-4092},
+   DOI = {10.1007/s12555-025-0624-2},
+   url = {https://doi.org/10.1007/s12555-025-0624-2},
+   year = {2025},
+   type = {Journal Article}
+}