文件列表:

.travis.yml (53, 2016-06-06)
Doxyfile (105195, 2016-06-06)
examples\create_histogram_grid.c (1261, 2016-06-06)
examples\no-velocity.c (2212, 2016-06-06)
include\histogram_grid.h (1006, 2016-06-06)
include\polar_histogram.h (547, 2016-06-06)
include\vfh.h (866, 2016-06-06)
LICENSE (1062, 2016-06-06)
Makefile (616, 2016-06-06)
src\histogram_grid.c (2795, 2016-06-06)
src\polar_histogram.c (1629, 2016-06-06)
src\vfh.c (1876, 2016-06-06)
examples (0, 2017-07-25)
include (0, 2017-07-25)
src (0, 2017-07-25)

# Vector Field Histogram [](https://travis-ci.org/agarie/vector-field-histogram) This is an **incomplete** implementation of the [Vector Field Histogram](http://en.wikipedia.org/wiki/Vector_Field_Histogram) algorithm, developed by J. Borenstein and Y. Koren in 1990. Recently I got some motivation to study robotics once again and decided it was time to finally "finish" this code. I originally used this library in a project for my real-time systems class at [Embry-Riddle Aeronautical University](http://www.erau.edu) when I was studying in the US. Eventually, I'll add support to the VFH+ and VFH-star algorithms. ## How to install Building the software should be as easy as: ```bash git clone https://github.com/agarie/vector-field-histogram.git cd vector-field-histogram make ``` I'm not working on using autotools for the moment (I need to learn how to use it, to be honest...). For now, it's possible to compile a program with the object file `vfh.o`. The example `create_histogram_grid`(see Makefile) does exactly that. ## The VFH algorithm The VFH algorithm receives as inputs an array of rangefinder sensor readings and generates control signals -- the "best" direction and a damping factor for the max velocity of the robot. The sensor readings are mapped into the Histogram Grid, a large matrix in which each cell corresponds to an obstacle density in that area. Sonars and laser rangefinders return a direction and a distance, so there must be a conversion from polar to rectangular coordinates before processing them. The correspondence between real world coordinates (i.e. `(x, y)`) and grid coordinates (i.e. `(i, j)`) is given by a `resolution` parameter, which is the size of the cells, such that `(x, y)` is in cell `(i, j)` if x is in [i * resolution, (i + 1) * resolution] and j is in [j * resolution, (j + 1) * resolution]. Each reading in a cell increases that cell's density by 1. With the histogram grid in place, actual path planning can begin. A square moving window centered around the robot is picked and each of its cells is mapped to (m_ij, beta_ij); `m` is a function of the obstacle density of the cell and its distance to the robot, and `beta` is the angular position of the cell. The polar histogram separates the cells in `n = 360/alpha` sectors. Each sector k has value M_k = sum(m_ij for (i, j) in sector_k). The moving window is used to avoid computing on all cells (the histogram grid can be *huge*) and because cells too far away probably won't contribute much to local planning. A threshold is applied to the polar histogram, selecting only the sectors with obstacle density low enough for safe passage. Finally, the sector with the direction best matching the objective's is followed. The max velocity S_max is decreased depending on the density of the chosen sector and the current angular velocity. ## License Copyright (c) 2012-2016 Carlos Agarie. See LICENSE for details.

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