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H-OBCA

Hierarchical Optimization-Based Collision Avoidance - An algorithm for generating dynamically feasible parking trajectories

Paper describing the theory can be found here.

Short Description

H-OBCA is an optimization-based approach for autonomous parking. It builds on OBCA, which is a recent method for generating obstacle-free trajectories using optimal control.

H-OBCA is able to generate high-quality kino-dynamically feasible obstacle-free trajectories. These trajectories are smooth, and can be accurately tracked by simple low-level path following controllers. A Julia-based implementation is provided.

Examples

H-OBCA for Reverse Parking

H-OBCA for Parallel Parking

How to run the Parking code:

First steps

  1. Change to the directory

  2. Install Julia from https://julialang.org/downloads/ (code tested on version 0.5.1 and 0.6.0)

  3. Open Julia in terminal

  4. Install Julia package JuMP using Pkg.add("JuMP")

  5. Install Julia package Ipopt using Pkg.add("Ipopt")

  6. Install Julia package PyPlot using Pkg.add("PyPlot")

  7. Install Julia package NearestNeighbors using Pkg.add("NearestNeighbors")

  8. Install Julia package ControlSystems using Pkg.add("ControlSystems")

Running the parking example

  1. Start Julia in terminal

  2. Type in terminal: include("setup.jl")

  3. Type in terminal: include("main.jl")

modifying the code

  1. To play with start points, change x0 in main.jl and run the code by: include("main.jl")

  2. If you change anything in one of the collision avoidance problems, you need to activate the changes by running: include("setup.jl")

  3. If you change the size of the car in main.jl, the change also need to be made in collision_check.jl

Note

  1. this code has been tested on Julia 0.5.1 and 0.6.0, and might not run on any other Julia versions

  2. For best results, run code in Julia terminal

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Hierarchical Optimization-Based Collision Avoidance - a path planner for autonomous parking

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