# OPNet

**Repository Path**: leewlz/opnet

## Basic Information

- **Project Name**: OPNet
- **Description**: runnable version of planning with opnet
branch 'trt' is built upon Nvidia TensorRT
branch 'torch' is built upon Pytorch
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-01-26
- **Last Updated**: 2021-01-27

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<<<<<<< HEAD
c++ realization of the paper: Kinodynamic RRT*: Asymptotically Optimal Motion Planning for Robots with Linear Dynamics

Building:
The depth_sensor_simulator package in uav_simulator is alternative to build with GPU or CPU to render the depth sensor measurement. By default, it is set to build with GPU in CMakeLists:

Dependencies:

1. ROS (I am using Ubuntu 16.04 and ROS Kinetic, other versions maybe also usable)

2. CUDA (I am using 10.2)

3. for branch "master" (inference using NVIDIA TensorRT):
    * TensorRT 7.0.0+cuda10.2 (with TensorRT ONNX  libraries)
     
4. for branch "torch" (inference using a Python node with Pytorch ):
    * numpy
    * Pytorch (I am using 1.3.0, later versions are also usable)


# set(ENABLE_CUDA false)
set(ENABLE_CUDA true)

Remember to change the 'arch' and 'code' flags according to your graphics card devices. 
Remember to change the file paths such as "/home/wlz/op_ws/src/..." in related launch files
for branch "torch":
* Remember to change the model path in net_node.py -- init_param function

Run Simulation:
1. roslaunch state_machine rviz.launch  (to open rviz for visualization)
2. roslaunch state_machine bench_with_pred.launch    (generate environment, start simulator)
3. roslaunch state_machine bench_with_pred.launch  or  bench_aggres.launch    or    bench_safe.launch    (test)


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# krrt-planner-with-prediction