# CurveProximityQueries

Curve - Convex Polygon | Curve - Curve |
---|---|

CurveProximityQueries implements methods to compute the

- Closest Points
- Minimum Distance
- Tolerance Verification
- Collision Detection

between an absolutely continuous parametric curve and another object, or between two curves. If you find this package/work useful in your research please cite our paper:

```
@INPROCEEDINGS{Hovakimyan-RSS-19,
AUTHOR = {Arun Lakshmanan AND Andrew Patterson AND Venanzio Cichella AND Naira Hovakimyan},
TITLE = {Proximity Queries for Absolutely Continuous Parametric Curves},
BOOKTITLE = {Proceedings of Robotics: Science and Systems},
YEAR = {2019},
ADDRESS = {Freiburg im Breisgau, Germany},
MONTH = {June},
DOI = {10.15607/RSS.2019.XV.042}
}
```

## Usage

### Installation

julia> ] add CurveProximityQueries

### Curve Types

Methods are available to create and manipulate Bernstein polynomials. A Bernstein polynomial with uniformly randomly sampled control points can be created with:

julia> rand(Bernstein{3, 6})
a 5th order Bernstein polynomial with control points at:
([0.345747, 0.149338, 0.609345], [0.86539, 0.736102, 0.31424], [0.20149, 0.167441, 0.662126], [0.975667, 0.468056, 0.505278], [0.371533, 0.477992, 0.83668], [0.322821, 0.973494, 0.93129])
with an arclength of 1.463398157000094

which constructs a 3D 5th order Bernstein polynomial. Control points can be directly fed to the constructor as well:

julia> cpts = [[0.0, 0.0], [1.0, 2.0], [2.0, 0.0], [3.0, 0.0]];
julia> c = Bernstein(cpts)
a 3rd order Bernstein polynomial with control points at:
([0.0, 0.0], [1.0, 2.0], [2.0, 0.0], [3.0, 0.0])
with an arclength of 3.714835124201342

Generally Bernstein polynomials are evaluated between $[0, 1]$, but custom limits can be provided using `Interval`

from the `IntervalArithmetic`

package:

julia> c = Bernstein(cpts, limits=Interval(0.5, 2.5))
a 3rd order Bernstein polynomial with control points at:
([0.0, 0.0], [1.0, 2.0], [2.0, 0.0], [3.0, 0.0])
with an arclength of 3.714835124201342

The `Bernstein`

types are functors that evaluate the polynomial at some value.

julia> c(1.5)
2-element SArray{Tuple{2},Float64,1,2}:
1.5
0.75

Note, that the arclength is not a true arclength but an upper bound that is used by the proximity methods. This upper bound can be evaluated at an interval or from the beginning of the limit using:

julia> arclength(c, Interval(0.9, 1.4))
0.7839413641976036
julia> arclength(c, 1.0) # evaluates from 0.5 to 1.0
1.1826380733343569

Several algebraic operations over Bernstein polynomials are available for convenience: addition, subtraction, product with reals, differentiation.

### Obstacle Types

Several obstacle types are provided with convenience macros from ConvexBodyProximityQueries.jl:

julia> @point rand(3)
ConvexPolygon{3,1,Float64}(SArray{Tuple{3},Float64,1,3}[[0.135678, 0.840508, 0.140532]])
julia> @line [0.,1.,1.], [1.,2.,1.] # point A, point B
ConvexPolygon{3,2,Float64}(SArray{Tuple{3},Float64,1,3}[[0.0, 1.0, 1.0], [1.0, 2.0, 1.0]])
julia> @rect [0.,0.], rand(2) # center, widths
ConvexPolygon{2,4,Float64}(SArray{Tuple{2},Float64,1,2}[[0.395191, 0.174093], [-0.395191, 0.174093], [-0.395191, -0.174093], [0.395191, -0.174093]])
julia> @square ones(3), 1.0 # center, width
ConvexPolygon{3,8,Float64}(SArray{Tuple{3},Float64,1,3}[[1.5, 1.5, 1.5], [0.5, 1.5, 1.5], [0.5, 0.5, 1.5], [1.5, 0.5, 1.5], [1.5, 1.5, 0.5], [0.5, 1.5, 0.5], [0.5, 0.5, 0.5], [1.5, 0.5, 0.5]])

Random convex polygons can be constructed for 2D:

julia> obs = randpoly([1., 2.], 0.5; scale=1.0, n=20) # center, rotation; scale, number of vertices
ConvexPolygon{2,20,Float64}(SArray{Tuple{2},Float64,1,2}[[0.642686, 2.36248], [0.622121, 2.34973], [0.42866, 2.06399], [0.412454, 2.0344], [0.454968, 1.98069], [0.499506, 1.92797], [0.599317, 1.82251], [0.62982, 1.79366], [0.659987, 1.76526], [0.733777, 1.71118], [0.87861, 1.63702], [1.07313, 1.54129], [1.46142, 1.68951], [1.46817, 1.72673], [1.48588, 1.85669], [1.46772, 2.06245], [1.3987, 2.23026], [1.30631, 2.4218], [1.20662, 2.61294], [0.88346, 2.47282]])

### Proximity Queries

The formal definitions for each of the proximity queries can be found in the paper.

#### Minimum Distance

julia> minimum_distance(c, obs)
0.6542938586889715

#### Tolerance Verification

julia> tolerance_verification(c, obs, 0.5)
true
julia> tolerance_verification(c, obs, 1.0)
false

#### Collision Detection

julia> collision_detection(c, obs)
false

#### Closest Points

julia> pts = closest_points(c, obs)
([1.07313, 1.54129], [1.03968, 0.887853])

All the above tests can be performed when `obs`

is replaced with another parametric curve.

### Plotting

Plotting recipes are provided for the curves. For example to plot the closest points between `obs`

and `c`

, one can simply:

julia> plot(c); plot!(obs); plot!(pts)

### Custom Types

Parametric curves can be user defined. For example, a monomial basis type can be created as a subtype of `Curve{D, T}`

where `D`

is the dimension of the curve and `T`

is the eltype:

struct Monomial{D, N, T} <: Curve{D, T}
coeff::SVector{N, SVector{D, T}}
limits::Interval{T}
end

Methods to compute the evaluation (as a functor), and arclength upper bound has to be provided (see paper for details).

Similarly, custom types for obstacles can be created. If the obstacles are convex, then only a support mapping for the custom type is required. However, if the obstacle is not convex then methods to compute the `minimum_distance`

, `tolerance_verification`

, and `collision_detection`

between a point in space and the object must be provided.