def test_inputs():
horizon, theta, deadline, solver_type = get_solver_param()
g, v0_target, v0_searchers, target_motion, belief_distribution = parameters_sim()
specs = MyInputs()
specs.set_theta(theta)
specs.set_deadline(deadline)
specs.set_solver_type(solver_type)
specs.set_horizon(horizon)
specs.set_graph(0)
specs.set_start_target(v0_target)
specs.set_start_searchers(v0_searchers)
specs.set_target_motion(target_motion)
specs.set_belief_distribution(belief_distribution)
assert g["name"] == specs.graph["name"]
assert specs.horizon == horizon
assert specs.theta == theta
assert specs.start_target_random is False
assert specs.start_target_true == v0_target[0]
assert specs.start_searcher_random is False
assert specs.start_searcher_v == v0_searchers
assert specs.target_motion == target_motion
assert specs.belief_distribution == belief_distribution
def my_specs():
theta = 2
deadline = 6
horizon = 3
solver_type = 'central'
graph_file = 'G7V_test.p'
g = ext.get_graph(graph_file)
target_motion = 'random'
belief_distribution = 'uniform'
v0_target = [7]
v0_searchers = [1, 2]
specs = MyInputs()
specs.set_graph(0)
specs.set_theta(theta)
specs.set_deadline(deadline)
specs.set_solver_type(solver_type)
specs.set_horizon(horizon)
specs.set_start_target(v0_target)
specs.set_start_searchers(v0_searchers)
specs.set_target_motion(target_motion)
specs.set_belief_distribution(belief_distribution)
return specs
def my_specs():
horizon, theta, deadline, solver_type = get_solver_param()
g, v0_target, v0_searchers, target_motion, belief_distribution = parameters_sim()
specs = MyInputs()
specs.set_graph(0)
specs.set_theta(theta)
specs.set_deadline(deadline)
specs.set_solver_type(solver_type)
specs.set_horizon(horizon)
specs.set_start_target(v0_target)
specs.set_start_searchers(v0_searchers)
specs.set_target_motion(target_motion)
specs.set_belief_distribution(belief_distribution)
return specs
def call_sim_ex():
"""Example for changing specs and calling the planner """
# initialize default inputs
specs = MyInputs()
# load graph, either by number (int), iGraph object or .p file name (str)
specs.set_graph(8)
# solver parameter: central x distributed
specs.set_solver_type('distributed')
# target motion
specs.set_target_motion('static')
# searchers' detection: capture range and false negatives
m = 3
specs.set_capture_range(0)
specs.set_size_team(m)
# time-step stuff: deadline mission (tau), planning horizon (h), re-plan frequency (theta)
h = 10
specs.set_all_times(h)
specs.set_deadline(50)
specs.set_theta(1)
# solver timeout (in sec)
specs.set_timeout(120)
# belief
n = len(specs.graph.vs)
v_maybe = [8, 10, 12, 14, 15, 17]
b_0 = [0.0 for i in range(n + 1)]
for v in v_maybe:
b_0[v] = 1 / 6
specs.set_b0(b_0)
specs.set_start_target_vertex(5)
# searchers initial vertices
specs.set_start_searchers([1, 1, 1])
# run simulator
belief, target, searchers, sim_data = sf.run_simulator(specs)