engineering

Vector Field Histogram

Vector Field Histogram is a engineering topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Vector Field Histogram rather than just read about it. In short: In robotics, Vector Field Histogram (VFH) is a real time motion planning algorithm proposed by Johann Borenstein and Yoram Koren in 1991. The VFH utilizes a statistical representation of the robot's environment through the so-called histogram grid, and therefore places great emphasis on dealing with uncertainty from sensor and modeling errors.

Key takeaways

Vector Field Histogram belongs to engineering; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Vector Field Histogram to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Vector Field Histogram from memory before moving on to harder problems.

Reference excerpt

In robotics, Vector Field Histogram (VFH) is a real time motion planning algorithm proposed by Johann Borenstein and Yoram Koren in 1991. The VFH utilizes a statistical representation of the robot's environment through the so-called histogram grid, and therefore places great emphasis on dealing with uncertainty from sensor and modeling errors. Unlike other obstacle avoidance algorithms, VFH takes into account the dynamics and shape of the robot, and returns steering commands specific to the platform. While considered a local path planner, i.e., not designed for global path optimality, the VFH has been shown to produce near optimal paths. The original VFH algorithm was based on previous work on Virtual Force Field, a local path-planning algorithm. VFH was updated in 1998 by Iwan Ulrich and Johann Borenstein, and renamed VFH+ (unofficially "Enhanced VFH"). The approach was updated again in 2000 by Ulrich and Borenstein, and was renamed VFH*. VFH is currently one of the most popular local planners used in mobile robotics, competing with the later developed dynamic window approach. Many robotic development tools and simulation environments contain built-in support for the VFH, such as in the Player Project.

VFH The Vector Field Histogram was developed with aims of being computationally efficient, robust, and insensitive to misreadings. In practice, the VFH algorithm has proven to be fast and reliable, especially when traversing densely populated obstacle courses. At the center of the VFH algorithm is the use of statistical representation of obstacles, through histogram grids (see also occupancy grid). Such representation is well suited for inaccurate sensor data, and accommodates fusion of multiple sensor readings. The VFH algorithm contains three major components:

Cartesian histogram grid: a two-dimensional Cartesian histogram grid is constructed with the robot's range sensors, such as a sonar or a laser rangefinder. The grid is continuously updated in real time. Polar histogram: a one-dimensional polar histogram is constructed by reducing the Cartesian histogram around the momentary location of the robot. Candidate valley: consecutive sectors with a polar obstacle density below threshold, known as candidate valleys, is selected based on the proximity to the target direction. Once the center of the selected candidate direction is determined, orientation of the robot is steered to match. The speed of the robot is reduced when approaching obstacles head-on.

VFH+ The VFH+ algorithm improvements include:

Threshold hysteresis: a hysteresis increases the smoothness of the planned trajectory. Robot body size: robots of different sizes are taken into account, eliminating the need to manually adjust parameters via low-pass filters. Obstacle look-ahead: sectors that are blocked by obstacles are masked in VFH+, so that the steer angle is not directed into an obstacle. Cost function: a cost function was added to better characterize the performance of the algorithm, and also gives the possibility of switching between behaviours by changing the cost function or its parameters.

VFH* In August 2000, Iwan Ulrich and Johann Borenstein published a paper describing VFH*, claiming improvement upon the original VFH algorithms by explicitly dealing with the shortcomings of a local planning algorithm, in that global optimality is not ensured. In VFH*, the algorithm verifies the steering command produced by using the A* search algorithm to minimize the cost and heuristic functions. While simple in practice, it has been shown in experimental results that this look-ahead verification can successfully deal with problematic situations that the original VFH and VFH+ cannot handle (the resulting trajectory is fast and smooth, with no significant slow down in presence of obstacles).

See also Motion planning Dynamic window approach

References

Worked examples

Example 1 — a first encounter with Vector Field Histogram

Start with the simplest possible case. Write down what Vector Field Histogram claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In engineering, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Vector Field Histogram before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Vector Field Histogram ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Vector Field Histogram

In research: Vector Field Histogram appears in engineering research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Vector Field Histogram in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Vector Field Histogram is common in secondary-school and first-year university syllabi. It links to neighbouring topics Robot control, so understanding it makes those chapters shorter.
In everyday life: Look for Vector Field Histogram outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Vector Field Histogram in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Vector Field Histogram means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Vector Field Histogram out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Vector Field Histogram in simple terms?

Why does Vector Field Histogram matter?

Because it connects several engineering ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Vector Field Histogram?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Vector Field Histogram.