Example: Reachability problem solved by Lazy abstraction.

This is a optimal reachability problem for a continuous system.

In order to study the concrete system and its symbolic abstraction in a unified framework, we will solve the problem for the sampled system with a sampling time $\tau$. For the construction of the relations in the abstraction, it is necessary to over-approximate attainable sets of a particular cell. In this example, we consider the used of a growth bound function [1, VIII.2, VIII.5] which is one of the possible methods to over-approximate attainable sets of a particular cell based on the state reach by its center.

For this reachability problem, the abstraction controller is built using a solver that lazily builds the abstraction, constructing the abstraction at the same time as the controller.

First, let us import StaticArrays and Plots.

using StaticArrays, JuMP, Plots

At this point, we import Dionysos.

using Dionysos
const DI = Dionysos
const UT = DI.Utils
const DO = DI.Domain
const ST = DI.System
const SY = DI.Symbolic
const PR = DI.Problem
const OP = DI.Optim
const AB = OP.Abstraction

include(joinpath(dirname(dirname(pathof(Dionysos))), "problems", "simple_problem.jl"))

# specific functions
function post_image(abstract_system, concrete_system, xpos, u)
    Xdom = abstract_system.Xdom
    x = DO.get_coord_by_pos(Xdom.grid, xpos)
    Fx = concrete_system.f_eval(x, u)
    r = Xdom.grid.h / 2.0 + concrete_system.measnoise
    Fr = r

    rectI = DO.get_pos_lims_outer(Xdom.grid, UT.HyperRectangle(Fx .- Fr, Fx .+ Fr))
    ypos_iter = Iterators.product(DO._ranges(rectI)...)
    over_approx = []
    allin = true
    for ypos in ypos_iter
        ypos = DO.set_in_period_pos(Xdom, ypos)
        if !(ypos in Xdom)
            allin = false
            break
        end
        target = SY.get_state_by_xpos(abstract_system, ypos)[1]
        push!(over_approx, target)
    end
    return allin ? over_approx : []
end

function pre_image(abstract_system, concrete_system, xpos, u)
    grid = abstract_system.Xdom.grid
    x = DO.get_coord_by_pos(grid, xpos)
    potential = Int[]
    x_prev = concrete_system.f_backward(x, u)
    xpos_cell = DO.get_pos_by_coord(grid, x_prev)
    n = 2
    for i in (-n):n
        for j in (-n):n
            x_n = (xpos_cell[1] + i, xpos_cell[2] + j)
            x_n = DO.set_in_period_pos(abstract_system.Xdom, x_n)
            if x_n in abstract_system.Xdom
                cell = SY.get_state_by_xpos(abstract_system, x_n)[1]
                if !(cell in potential)
                    push!(potential, cell)
                end
            end
        end
    end
    return potential
end

function compute_reachable_set(rect::UT.HyperRectangle, concrete_system, Udom)
    r = (rect.ub - rect.lb) / 2.0 + concrete_system.measnoise
    Fr = r
    x = UT.get_center(rect)
    n = UT.get_dims(rect)
    lb = fill(Inf, n)
    ub = fill(-Inf, n)
    for upos in DO.enum_pos(Udom)
        u = DO.get_coord_by_pos(Udom.grid, upos)
        Fx = concrete_system.f_eval(x, u)
        lb = min.(lb, Fx .- Fr)
        ub = max.(ub, Fx .+ Fr)
    end
    lb = SVector{n}(lb)
    ub = SVector{n}(ub)
    return UT.HyperRectangle(lb, ub)
end
minimum_transition_cost(symmodel, contsys, source, target) = 1.0

concrete_problem = SimpleProblem.problem()
concrete_system = concrete_problem.system

hx = [0.5, 0.5]
u0 = SVector(0.0, 0.0)
hu = SVector(0.5, 0.5)
Ugrid = DO.GridFree(u0, hu)
hx_heuristic = [1.0, 1.0] * 1.5
maxIter = 100

optimizer = MOI.instantiate(AB.LazyAbstraction.Optimizer)

AB.LazyAbstraction.set_optimizer!(
    optimizer,
    concrete_problem,
    maxIter,
    pre_image,
    post_image,
    compute_reachable_set,
    minimum_transition_cost,
    hx_heuristic,
    hx,
    Ugrid,
)

WARNING: method definition for system at /home/runner/work/Dionysos.jl/Dionysos.jl/problems/simple_problem.jl:28 declares type variable T but does not use it.
WARNING: method definition for system at /home/runner/work/Dionysos.jl/Dionysos.jl/problems/simple_problem.jl:28 declares type variable N but does not use it.

Build the abstraction and solve the optimal control problem using A* algorithm

using Suppressor
@suppress begin
    MOI.optimize!(optimizer)
end

Get the results

abstract_system = MOI.get(optimizer, MOI.RawOptimizerAttribute("abstract_system"))
abstract_problem = MOI.get(optimizer, MOI.RawOptimizerAttribute("abstract_problem"))
abstract_controller = MOI.get(optimizer, MOI.RawOptimizerAttribute("abstract_controller"))
concrete_controller = MOI.get(optimizer, MOI.RawOptimizerAttribute("concrete_controller"))
abstract_lyap_fun = MOI.get(optimizer, MOI.RawOptimizerAttribute("abstract_lyap_fun"))
concrete_lyap_fun = MOI.get(optimizer, MOI.RawOptimizerAttribute("concrete_lyap_fun"));

Simulation

We define the cost and stopping criteria for a simulation

cost_eval(x, u) = UT.function_value(concrete_problem.transition_cost, x, u)
reached(x) = x ∈ concrete_problem.target_set
nstep = typeof(concrete_problem.time) == PR.Infinity ? 100 : concrete_problem.time; # max num of steps

We simulate the closed loop trajectory

x0 = UT.get_center(concrete_problem.initial_set)
cost_control_trajectory = ST.get_closed_loop_trajectory(
    concrete_system.f_eval,
    concrete_controller,
    cost_eval,
    x0,
    nstep;
    stopping = reached,
    noise = false,
)

cost_bound = concrete_lyap_fun(x0)
cost_true = sum(cost_control_trajectory.costs.seq);
println("Goal set reached")
println("Guaranteed cost:\t $(cost_bound)")
println("True cost:\t\t $(cost_true)")

Goal set reached
Guaranteed cost:	 9.0
True cost:		 9.0

Display the results

Display the specifications and domains

fig = plot(; aspect_ratio = :equal);
#We display the concrete domain
plot!(concrete_system.X; color = :yellow, opacity = 0.5);

#We display the abstract domain
plot!(abstract_system.Xdom; color = :blue, opacity = 0.5);

#We display the concrete specifications
plot!(concrete_problem.initial_set; color = :green, opacity = 0.8);
plot!(concrete_problem.target_set; dims = [1, 2], color = :red, opacity = 0.8);

#We display the concrete trajectory
plot!(cost_control_trajectory; ms = 0.5)

Display the abstraction and Lyapunov-like function

fig = plot(; aspect_ratio = :equal);
plot!(
    abstract_system;
    dims = [1, 2],
    cost = true,
    lyap_fun = optimizer.lyap_fun,
    label = false,
)

Display the Bellman-like value function (heuristic)

fig = plot(; aspect_ratio = :equal)
plot!(
    optimizer.abstract_system_heuristic;
    arrowsB = false,
    dims = [1, 2],
    cost = true,
    lyap_fun = optimizer.bell_fun,
    label = false,
)

Display the results of the A* algorithm

fig = plot(; aspect_ratio = :equal)
plot!(optimizer.lazy_search_problem)
plot!(cost_control_trajectory; color = :black)

display(fig)

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