# 概率模型SSMR

**Repository Path**: kczy/probability-model---ssmr

## Basic Information

- **Project Name**: 概率模型SSMR
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-03-01
- **Last Updated**: 2024-07-15

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


## Skid-Steer Robot Modeling

### Introduction
This repository contains the code associated to our ICRA 2024 paper : A Probabilistic Motion Model for Skid-Steer Wheeled Mobile Robot Navigation on Off-Road Terrains.

Paper : https://arxiv.org/pdf/2402.18065.pdf

Video : https://www.youtube.com/watch?v=_rVy2aBp42c

We train Gaussian Process Regression models to predict future robot linear and angular velocity states for different terrains. The outputs of multiple models
are then fused online using a convex optimization formulation allowing the motion model to generalize to different/unseen terrain conditions. The resultant mean and
covariance estimates of the robot states can be used for Risk-Aware Motion Planning approaches such as Stochastic Model Predictive Control. 

### Experimental Setup
Begin by cloning this repository and setting up a Python virtual environment.

```
git clone git@github.com:RIVeR-Lab/jackal_robot_modeling.git
python3 -m venv venv
source venv/bin/activate
pip3 install -r requirments.txt
```

In order to evaluate and benchmark our proposed modeling method, we used the off-road navigation dataset released as a part of [this](https://ieeexplore.ieee.org/abstract/document/8794216) paper. 
Begin by cloning this repository and downloading the dataset as follows. If you have troubles setting up the dataset as suggested above, you can manually download it from [this](https://drive.google.com/file/d/10YAQsaLhTnNbBER5beItwMlTBYkLmqTC/view?usp=drive_link) link. 

```
sudo apt-get install unzip
cd jackal_robot_modeling/
gdown 10YAQsaLhTnNbBER5beItwMlTBYkLmqTC
unzip -qq data.zip
rm -rf data.zip
```

The training of the GP/Benchmark kinematic models and their subsequent inference on a test dataset has been assembled into a single script shown below. The plots and tables in the paper were generated via running the individual components of this script.

```
python3 src/probabilistic_dynamics.py
```

### Citation
If you find this code useful, please consider citing
```
@article{trivedi2024probabilistic,
  title={A Probabilistic Motion Model for Skid-Steer Wheeled Mobile Robot Navigation on Off-Road Terrains},
  author={Trivedi, Ananya and Zolotas, Mark and Abbas, Adeeb and Prajapati, Sarvesh and Bazzi, Salah and Pad{\i}r, Task{\i}n},
  journal={arXiv preprint arXiv:2402.18065},
  year={2024}
}
```