README.md

Automated Valet Parking

1. Introduction

This repo provides an algorithm which uses hybrid a star for the initial path and the optimization based method to generate the trajectory. The pipeline of this algorithm is:

Hybrid A star -> Path optimization -> Cubic interpolation -> Velocity plan -> Solve optimization problem (use IPOPT)

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1.1 File Structure

.
├── animation
│   ├── animation.py
│   └── record_solution.py
├── collision_check
│   ├── collision_check.py
├── config
│   ├── config.yaml
│   └── read_config.py
├── interpolation
│   └── path_interpolation.py
├── main.py
├── map
│   ├── costmap.py
├── optimization
│   ├── ocp_optimization.py
│   └── path_optimazition.py
├── path_plan
│   ├── compute_h.py
│   ├── hybrid_a_star.py
│   ├── path_planner.py
│   └── rs_curve.py
├── util_math
│   ├── coordinate_transform.py
│   └── spline.py
└── velocity_plan
    └── velocity_planner.py

1.2 Requirement

Python version == 3.8 and Only support Ubuntu system (tested in 20.04, but I think 18.04 is suitable as well)

Not support in windows 64bit because the IPOPT could be not executable.

pip install -r requirements.txt

conda install -c conda-forge ipopt

1.3 Data Structure

The Case1.csv is provided by https://www.tpcap.net/#/benchmarks, and the details of this file are presented by the following:

The first six rows of the vector record the initial and goal poses of the to-be-parked vehicle. Suppose $V$ is the data vector.

• $x_{0}$ = $V$[ 1 ], $y_{0}$ = $V$[ 2 ], $\theta_{0}$ = $V$[ 3 ]
• $x_{f}$ = $V$[ 4 ], $y_f$ = $V$[ 5 ], $\theta_f$ = $V$[ 6 ].
• $V$[ 7 ] records the total number of obstacles in the parking scenario.
• $V$[ 7+$i$ ] presents the number of vertexes in the $i$-th obstacle, where the index $i$ ranges from 1 to $V$[7].
• After that, the vertexes of each obstacle are presented by their 2D coordinate values in the $x$ and $y$ axes.

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Note: you can build your own parking map based on the above rules and store the .csv file in the BenchmarkCase folder.

2. Usage

run the main.py to solve the scenario and show the animation process. There are two modes, mode 0 is to solve the scenario, and mode 1 is to plot the speed or accelariot curve.

python main.py

The solution of the trajectory is stored as a .csv file and its column name is [x,y,theta,v,a,sigma,omega,t]

The aniamation pictures including gif and png is stored in the pictures folder.

3. Todo List

• more spine function
• more velocity plan function