Distance between two non parallel lines

  • Context: MHB 
  • Thread starter Thread starter mathmari
  • Start date Start date
  • Tags Tags
    Lines Parallel
Click For Summary
SUMMARY

The distance between two non-parallel lines \( l_1 \) and \( l_2 \) in three-dimensional space is calculated using the formula \( d=\frac{|(\overrightarrow{v}_1 - \overrightarrow{v}_2) \cdot (\overrightarrow{a}_1 \times \overrightarrow{a}_2)|}{||\overrightarrow{a}_1 \times \overrightarrow{a}_2||} \). Here, \( \overrightarrow{v}_1 \) and \( \overrightarrow{v}_2 \) are points on lines \( l_1 \) and \( l_2 \), while \( \overrightarrow{a}_1 \) and \( \overrightarrow{a}_2 \) are their respective direction vectors. The method involves projecting the vector between the two points onto the unit normal vector of the plane formed by the direction vectors. This discussion clarifies that the formula is specifically applicable to skew lines in \( \mathbb{R}^3 \).

PREREQUISITES
  • Understanding of vector operations, including dot product and cross product.
  • Familiarity with the concept of projection in vector geometry.
  • Knowledge of three-dimensional coordinate systems and lines in \( \mathbb{R}^3 \).
  • Basic principles of geometry, particularly regarding skew lines and planes.
NEXT STEPS
  • Study vector projections and their applications in geometry.
  • Learn about the properties of skew lines in three-dimensional space.
  • Explore the geometric interpretation of the cross product in vector calculus.
  • Investigate applications of distance calculations in physics and engineering contexts.
USEFUL FOR

Students and professionals in mathematics, physics, and engineering who require a solid understanding of vector geometry, particularly in calculating distances between non-parallel lines in three-dimensional space.

mathmari
Gold Member
MHB
Messages
4,984
Reaction score
7
Hey! :o

Using vector methods show that the distance between two non parallel lines $l_1$ and $l_2$ is given by $$d=\frac{|(\overrightarrow{v}_1 - \overrightarrow{v}_2) \cdot (\overrightarrow{ a}_1 \times \overrightarrow{a}_2)|}{||\overrightarrow{a}_1 \times \overrightarrow{a}_2||}$$ where $\vec{v}_1$ and $\vec{v}_2$ are random points of $l_1$ and $l_2$ respectively, and $\vec{a}_1$ and $\vec{a}_2$ are the directions of $l_1$ and $l_2$.

HINT:
We consider the plane that contains $l_1$ and is parallel to $l_2$. Show that $\frac{\vec{a}_1 \times \vec{a}_2}{\|\vec{a}_1 \times \vec{a}_2\|}$ is unit perpendicular to that plane. Then take the projection of $\vec{v}_2-\vec{v}_1$ to that perpendicular direction.
I have done the following: We consider the plane that contains $l_1$ and is parallel to $l_2$. That means that the plane passes through the point $\overrightarrow{v}_1$ and has as parallel vector the vector $\overrightarrow{a}$.

To find the distance between the two lines, we have to find the distance between the points $\overrightarrow{v}_1$ and $\overrightarrow{v}_2$.

The vectors $\overrightarrow{a}_1$ and $\overrightarrow{a}_2$ produce the plane, so the vector $\overrightarrow{a}_1 \times \overrightarrow{a}_2$ is perpendicular to the plane.

So, the unit perpendicular vector to the plane is $\frac{\overrightarrow{a}_1 \times \overrightarrow{a}_2}{||\overrightarrow{a}_1 \times \overrightarrow{a}_2||}$.

A vector from the plane to the point $\overrightarrow{v}_2$ is $\overrightarrow{v}_2-\overrightarrow{v}_1$.

The distance that we are looking for the length of the projection of this vector onto the normal vector to the plane.

So, $$d=\frac{|(\overrightarrow{v}_1 - \overrightarrow{v}_2) \cdot (\overrightarrow{ a}_1 \times \overrightarrow{a}_2)|}{||\overrightarrow{a}_1 \times \overrightarrow{a}_2||}$$
Is this correct?? (Wondering) Could I improve something at the formulation?? (Wondering)
 
Physics news on Phys.org
The formulation of the question is incomplete at best. If the two lines lie in a plane and they are not parallel, the minimum distance is 0 (by definition). And in spherical geometry all parallel lines meet at the poles.
 
  • Like
Likes   Reactions: mathmari
I'm guessing that the intent here is to find the distance between two skew lines in ##\mathbb R^3##.
 
  • Like
Likes   Reactions: mathmari and Ackbach

Similar threads

  • · Replies 2 ·
Replies
2
Views
2K
  • · Replies 3 ·
Replies
3
Views
2K
  • · Replies 82 ·
3
Replies
82
Views
8K
  • · Replies 2 ·
Replies
2
Views
2K
Replies
2
Views
2K
Replies
2
Views
2K
  • · Replies 2 ·
Replies
2
Views
2K
  • · Replies 11 ·
Replies
11
Views
1K
Replies
3
Views
1K
Replies
2
Views
2K