ConvexBodyProximityQueries

ConvexBodyProximityQueries.jl implements the Gilber-Johnson-Keerthi (GJK) Algorithm from their seminal paper on fast collision detection. The following query types are available for two convex objects:

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

Usage

The package (by default) allows you to work with polytopes defined as an array of vertices, for example:

julia> using StaticArrays
julia> polyA = @SMatrix rand(2, 8)
2×8 SArray{Tuple{2,8},Float64,2,16}:
0.732619   0.327745   0.0390878  0.477455  0.627223  0.502666  0.0529193  0.0523722
0.0513408  0.0634308  0.892253   0.88009   0.100901  0.564782  0.789238   0.307813

julia> polyB = @SMatrix(rand(2, 8)) .+ 1.5
2×8 SArray{Tuple{2,8},Float64,2,16}:
2.18993  1.75404  1.51373  1.60674  1.67257  2.14208  1.97779  2.24657
2.32708  1.92212  2.32769  1.69457  1.85003  1.57441  1.93884  2.45361

julia> dir = @SVector(rand(2)) .- 0.5
2-element SArray{Tuple{2},Float64,1,2}:
-0.4673435693835293
0.4242237214159814


Or if you prefer to use LazySets to represent bounded convex sets, then simply import it into your workspace.

julia> using LazySets
julia> vp = rand(VPolytope, dim=3, num_vertices=5)
VPolytope{Float64}(Array{Float64,1}[[0.10865726649792662, 0.5724776430968089, -0.4410130831362367], [-0.8567759573657892, 0.07322903371223476, 0.34838985370789005], [0.03333527704052754, -1.974401966811797, 0.6174108419158255], [-0.4904624889544439, -0.3210835102598013, -1.1688696283212616], [-0.4369808677028199, -1.3570945645627628, -0.7506142537189342]])

julia> e = Ellipsoid([1.,1.,1.], diagm([1.0, 2.0, 3.0]))
Ellipsoid{Float64}([1.0, 1.0, 1.0], [1.0 0.0 0.0; 0.0 2.0 0.0; 0.0 0.0 3.0])


All the proximity queries can be performed simply by providing the polytope information and an initial searchdirection. In addition, tolerance_verfication requires an argument specifying the minimum tolerance of speration between two objects. :

julia> using BenchmarkTools
julia> @btime closest_points($polyA,$polyB, $dir) 172.901 ns (0 allocations: 0 bytes) ([0.477455, 0.88009], [1.60674, 1.69457]) julia> @btime minimum_distance($polyA, $polyB,$dir)
165.554 ns (0 allocations: 0 bytes)
1.3923553706117722

julia> @btime tolerance_verification($polyA,$polyB, $dir,$1.0)
99.324 ns (0 allocations: 0 bytes)
true

julia> @btime collision_detection($polyA,$polyB, $dir) 96.386 ns (0 allocations: 0 bytes) false  If you want to use your custom convex objects, you can do so by extending the support function as: import ConvexBodyProximityQueries.support function ConvexBodyProximityQueries.support(obj::MyFancyShape, dir::SVector{N}) where {N} # do something return supporting_point::SVector{N} end  Note: This is how I intended the package to be used, the vanilla support function is quite naive and only works for a StaticArray of vertices. Here are some examples for some geometries found in GeometryBasics.jl: import ConvexBodyProximityQueries.support using GeometryBasics: HyperSphere, HyperRectangle function ConvexBodyProximityQueries.support(sphere::HyperSphere{N, T}, dir::AbstractVector) where {N, T} SVector{N}(sphere.center + sphere.r*normalize(dir, 2)) end @generated function ConvexBodyProximityQueries.support(rect::HyperRectangle{N, T}, dir::AbstractVector) where {N, T} exprs = Array{Expr}(undef, (N,)) for i = 1:N exprs[i] = :(rect.widths[$i]*(dir[$i] ≥ 0.0 ? 1.0 : -1.0)/2.0 + rect.origin[$i])
end

return quote
Base.@_inline_meta
@inbounds elements = tuple(\$(exprs...))
@inbounds return SVector{N, T}(elements)
end
end


Obstacle Types

Here are some additional types that are constructed for convenience:

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]])


Speed

As the core routines use StaticArrays, they are very well optimized and run quickly with no memory allocations. However, it is upto to the user to provide efficient code for the support and a good init_dir vector to squeeze the best performance from the functions.

Examples

Minimum distance computation in 2D:

Minimum distance computation in 3D:

EnhancedGJK.jl

References

Gilbert, E. G., D. W. Johnson, and S. S. Keerthi. “A Fast Procedure for Computing the Distance between Complex Objects in Three-Dimensional Space.” IEEE Journal on Robotics and Automation 4, no. 2 (April 1988): 193–203. https://doi.org/10.1109/56.2083.