The Vector Field Histogram algorithm

I’m taking a course on real-time systems at my college. In one of the projects, we have to build a system that uses “concurrent programming” design approaches, with various threads acting separately and communicating through queues.

For this, I remembered some papers I’ve read in Brazil about path planning algorithms and one of them – the Vector Field Histogram (VFH) – sounded sufficiently simple to be implemented in two weeks. Of course, I had to implement the whole system: communication with sensors, some kind of output (in my case, via UART), the interthread communication mechanism, etc.

Well, my background with Ruby made me design it so the VFH algorithm could be substituted by a stub, making the system easily testable. So I did it. However, I had a surprise, a very strange one for someone accostumed with high-level languages and an OS: there’s no heap on the microcontroller, so I can’t use malloc(). Damn.

Well, the professor like my coding style and accepted the project as is, after talking about implementing a memory allocation function myself. Anyway, I wrote this post to explain how VFH works.

What is path planning?

Suppose you have a robot wandering in a room with some sonar sensors attached to it. It’s “seeing” lots of distance measures from the surroundings, but how can it find and avoid obstacles?

Rangefinding

That’s where a path planning algorithm is used. Of course, sonars (and, more generally, rangefinders) are one way to do it; cameras and computer vision algorithms are another one.

There are two types of path planning: local and global. The former deals with more immediate concerns such as “avoid a crash”, while the latter is concerned with how to get to the objective, usually in an optimal way.

The VFH is an example of local path planning.

Data structures used in the algorithm

There are two main data structures used in VFH: the Certainty Grid, which is a kind of long-term memory about the place the robot is in, and the Polar Histogram, with bins representing the “obstacle density” in each direction.

Certainty grid

In my (very ugly) drawing, you can see what the grid looks like. The robot is the ball in position (4, 4) and the obstacle is comprised of the points {(7, 4), (7, 5), (7, 6)}.

Polar histogram

In the polar histogram above, it’s obvious that the obstacle density of bin 0 is greater than the others. There are 8 bins, so each one corresponds to 360/8 = 45 [degrees], which is the reason only bin 0 perceives the obstacle.

Steps of the algorithm

The algorithm is pretty straightforward. I won’t go into much detail about why the calculations are like that, even the authors (in [1] and [2]) don’t do that. It’s mostly good engineering design and heuristics.

I’ll explain it in terms of the functions from my implementation (see below). You can find an example of it in the examples/ folder of the repository.

After a certainty grid and a polar histogram have been allocated through grid_init() and hist_init(), and you have some measurements from the rangefinder sensors (infrared, laser, sonar), the following steps are taken:

  1. The Certainty Grid is updated with grid_update().
    Each measure is transformed from (distance, direction) coordinates to the (x, y) system of the grid. Them, the corresponding cell has its obstacle density incremented by 1.

  2. A moving window centered around the robot is generated with get_moving_window().
    It’s a smaller grid centered around the robot. The obstacle density values of each cell is simply a copy of the corresponding one in the certainty grid.

  3. A polar histogram of the obstacle densities of the moving window is created with hist_update().
    The histogram has (360/a) bins, where a is chosen so (360/a) is an integer. For example, a = 5 => bins = (360/5) = 72.
    Each cell (i, j) has a corresponding angle b = atan[(j – y0) / (i – x0)], where (x0, y0) is the position of the robot. Also, its magnitude is m = c^2 (A – Br), where c is the obstacle density and r is the distance between the robot and the cell. A and B are constants that can be adjusted through experiments.
    Finally, a cell (i, j) is contained in bin k (where k = 1, …, n) if k = floor(b / a). The magnitude of bin k is simply the sum of the magnitudes of all the cells contained in it.

  4. The control signals are generated from the polar histogram with calculate_direction().
    A search is made on the histogram to find “valleys” (sequential bins with magnitude less than some threshold, which is problem-specific). Also, the algorithm must know the direction in which the objective lies.
    With these in hand, the direction the robot must travel to in this instant is the center of the valley nearest to the objective direction.

And the VFH is basically this. There’s room for improvement, indeed: in 1998 a better version was published[3], named VFH+. And in 2000, a version that combines global path planning (A* search) and the VFH+ was released[4], named VFH*.

My implementation

I made my implementation in pure C, using only stdlib.h and math.h. Of course, it’s open source and available on Github. Any tip of how to improve the API, the documentation, the code, etc is welcome!

There are some things that I didn’t do (yet): the smoothing function for the histogram, which I haven’t even talked about here, some form of automatically calculating constants A and B and the finished implementation of the moving window.

I’ll probably write Ruby bindings someday, as it would be a relatively simple and interesting project: controlling robots with it sounds fun. :)

References

  1. BORENSTEIN, J.; KOREN, Y. The Vector Field Histogram – Fast Obstacle Avoidance for Mobile Robots. IEEE Journal of Robotics and Automation Vol 7, No 3, June 1991, pp. 278-288.
  2. BORENSTEIN, J.; KOREN, Y. Real-Time Obstacle Avoidance for Fast Mobile Robots. IEEE Transactions on Systems, Man and Cybernetics, Vol. 19, No. 5, Sept./Oct. 1989, pp. 1179-1187.
  3. ULRICH, I.; BORENSTEIN, J. VFH+: Reliable Obstacle Avoidance for Fast Mobile Robots. IEEE International Conference on Robotics and Automation. May 1998, pp. 1572-1577.
  4. ULRICH, I.; BORENSTEIN, J. VFH*: Local Obstacle Avoidance with Look-Ahead Verification. IEEE International Conference on Robotics and Automation. April 2000, pp. 2505-2511.

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