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Update Jupyter notebooks
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‎notebooks/Example.ipynb‎

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@@ -27,8 +27,11 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# Workaround for: https://github.com/JuliaRobotics/RigidBodyDynamics.jl/issues/500\n",
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"using LinearAlgebra; BLAS.set_num_threads(1)"
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"using LinearAlgebra\n",
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"# Try to load something faster than OpenBLAS\n",
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"try using AppleAccelerate catch e; end\n",
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"try using MKL catch e; end\n",
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"BLAS.get_config()"
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]
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},
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{
@@ -37,11 +40,23 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"using KNITRO\n",
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"# using KNITRO\n",
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"using MeshCat\n",
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"using RigidBodyDynamics"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load the pre-compiled binaries of Coin HSL routines\n",
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"HSL_jll_path = expanduser(\"~/HSL_jll.jl-2023.5.26\")\n",
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"Pkg.develop(path=HSL_jll_path)\n",
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"import HSL_jll"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
@@ -71,8 +86,13 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"robot = TORA.create_robot_kuka_iiwa_14(vis)\n",
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"problem = TORA.Problem(robot, 301, 1/150)\n",
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"robot = TORA.create_robot_franka(\"panda_arm\", vis)\n",
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"# robot = TORA.create_robot_kinova_gen2(\"j2s6s200\", vis)\n",
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"# robot = TORA.create_robot_kinova_gen3(\"gen3_robotiq_2f_140\", vis)\n",
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"# robot = TORA.create_robot_kuka(\"iiwa14\", vis)\n",
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"# robot = TORA.create_robot_ur(\"ur10e\", vis)\n",
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"\n",
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"problem = TORA.Problem(robot, 2001, 1/1000)\n",
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"\n",
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"# Constrain initial and final joint velocities to zero\n",
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"TORA.fix_joint_velocities!(problem, robot, 1, zeros(robot.n_v))\n",
@@ -90,7 +110,8 @@
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"\n",
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" for k = 1:2:problem.num_knots\n",
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" θ = CubicTimeScaling(problem.num_knots - 1, k - 1) * 2π\n",
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" pos = [0.5, 0.2 * cos(θ), 0.8 + 0.2 * sin(θ)]\n",
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" # pos = [0.5, 0.2 * cos(θ), 0.8 + 0.2 * sin(θ)] # UR10e\n",
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" pos = [0.4, 0.2 * cos(θ), 0.7 + 0.2 * sin(θ)] # Franka Emika\n",
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" # pos = [0.5, 0.3 * sin(θ) + 0.1 * sin(8 * θ), 0.8 + 0.3 * cos(θ) + 0.1 * cos(8 * θ)]\n",
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" TORA.constrain_ee_position!(problem, k, pos)\n",
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" end\n",
@@ -105,7 +126,11 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"initial_q = [0, 0, 0, -π/2, 0, 0, 0]\n",
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"initial_q = zeros(robot.n_q)\n",
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"# initial_q = [0, 0, 0, -π/2, 0, 0, 0]\n",
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"initial_q = [0, -π/4, 0, -3π/4, 0, π/2, 0] # Franka Emika\n",
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"# initial_q = [0, -120, 120, -180, -90, 0] .|> deg2rad # UR10e\n",
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"# initial_q = x[1:7]\n",
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"\n",
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"zero!(robot.state)\n",
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"set_configuration!(robot.state, initial_q)\n",
@@ -130,6 +155,45 @@
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"use_inv_dyn = true\n",
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"minimise_τ = false\n",
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"\n",
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"user_options = Dict(\n",
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" # === Termination === # https://coin-or.github.io/Ipopt/OPTIONS.html#OPT_Termination\n",
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" # \"tol\" => 10e-2, # default: 10e-8\n",
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" # \"max_cpu_time\" => 4.0, # default: 10e20\n",
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" # \"constr_viol_tol\" => 0.1, # default: 0.0001\n",
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" # \"acceptable_tol\" => 0.1, # default: 10e-6\n",
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" # \"acceptable_constr_viol_tol\" => 0.1, # default: 0.01\n",
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"\n",
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" # === Output === # https://coin-or.github.io/Ipopt/OPTIONS.html#OPT_Output\n",
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" # \"print_level\" => 0, # [0, 12], default: 5\n",
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" \n",
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" # === NLP Scaling === # https://coin-or.github.io/Ipopt/OPTIONS.html#OPT_NLP_Scaling\n",
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" # \"nlp_scaling_method\" => \"none\", # none, user-scaling, gradient-based, equilibration-based\n",
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"\n",
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" # === Warm Start === # https://coin-or.github.io/Ipopt/OPTIONS.html#OPT_Warm_Start\n",
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" # \"warm_start_init_point\" => \"yes\",\n",
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" # \"warm_start_same_structure\" => \"yes\",\n",
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" # \"warm_start_entire_iterate\" => \"yes\",\n",
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"\n",
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" # === Barrier Parameter Update === # https://coin-or.github.io/Ipopt/OPTIONS.html#OPT_Barrier_Parameter_Update\n",
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" \"mu_strategy\" => \"adaptive\", # monotone (default), adaptive\n",
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" # \"mu_oracle\" => \"loqo\", # probing, loqo, quality-function (default)\n",
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"\n",
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" # === Linear Solver === # https://coin-or.github.io/Ipopt/OPTIONS.html#OPT_Linear_Solver\n",
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" \"linear_solver\" => \"ma57\", # ma27 (default), ma57, ma77, ma86, ma97, (...)\n",
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" # \"ma57_pre_alloc\" => 1.10, # [1, Inf), 1.05 (default)\n",
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"\n",
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" # === Step Calculation === # https://coin-or.github.io/Ipopt/OPTIONS.html#OPT_Step_Calculation\n",
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" # \"mehrotra_algorithm\" => \"yes\", # yes, no (default)\n",
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" # \"fast_step_computation\" => \"yes\", # yes, no (default)\n",
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"\n",
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" # === Hessian Approximation === # https://coin-or.github.io/Ipopt/OPTIONS.html#OPT_Hessian_Approximation\n",
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" \"hessian_approximation\" => \"limited-memory\", # exact (default), limited-memory\n",
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" # \"hessian_approximation_space\" => \"all-variables\", # nonlinear-variables (default), all-variables\n",
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"\n",
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" # === Derivative Checker === # https://coin-or.github.io/Ipopt/OPTIONS.html#OPT_Derivative_Checker\n",
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" # \"derivative_test\" => \"first-order\", # none, first-order, second-order, only-second-order\n",
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")\n",
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"\n",
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"# Choose which solver you want to use:\n",
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"solve = TORA.solve_with_ipopt # Uses Ipopt (https://github.com/coin-or/Ipopt)\n",
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"# solve = TORA.solve_with_knitro # Uses KNITRO (https://www.artelys.com/solvers/knitro/)\n",
@@ -138,7 +202,8 @@
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"cpu_time, x, solver_log = solve(problem, robot,\n",
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" initial_guess=initial_guess,\n",
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" use_inv_dyn=use_inv_dyn,\n",
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" minimise_τ=minimise_τ)"
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" minimise_τ=minimise_τ,\n",
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" user_options=user_options)"
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]
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},
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{
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"TORA.plot_log(solver_log)"
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Julia 1.8.0",
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"display_name": "Julia 1.9.1",
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"language": "julia",
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"name": "julia-1.8"
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"name": "julia-1.9"
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},
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"language_info": {
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"file_extension": ".jl",
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"mimetype": "application/julia",
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"name": "julia",
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"version": "1.8.0"
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"version": "1.9.1"
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}
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},
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"nbformat": 4,

‎notebooks/Tutorial.ipynb‎

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],
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"metadata": {
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"kernelspec": {
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"display_name": "Julia 1.8.0",
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"display_name": "Julia 1.9.1",
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"language": "julia",
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"name": "julia-1.8"
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"name": "julia-1.9"
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},
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"language_info": {
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"file_extension": ".jl",
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"mimetype": "application/julia",
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"name": "julia",
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"version": "1.8.0"
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"version": "1.9.1"
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}
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},
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"nbformat": 4,

‎src/transcription/ipopt.jl‎

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@@ -25,7 +25,7 @@ function solve_with_ipopt(problem::Problem, robot::Robot;
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initial_guess::Vector{Float64}=Float64[],
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use_inv_dyn::Bool=false,
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minimise_τ::Bool=false,
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user_options::Dict=Dict())
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user_options::Dict=Dict("hessian_approximation" => "limited-memory"))
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# # # # # # # # # # # # # # # #
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# Variables and their bounds #
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# # # # # # # # # # # # # # # #

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