def v15_plan(problem, agent):
problem.a_star = True
problem.rand = True # Only difference from v14
if not hasattr(problem, 'verbose'):
problem.verbose = 0
elif problem.verbose == 1:
print("Problem State: ")
pyhop.print_state(problem)
solutions = pyhop.seek_plan_v13(problem,problem.goals[agent],[],[],0, verbose=problem.verbose)
# If there is no solution
if solutions[0] == False:
return solutions
return Planner.make_sol_obj(solutions, problem, agent)
def drop(state, agent, store): if state.is_agent[agent] \ and (agent in state.stores) \ and (store == state.stores[agent] \ and (not state.empty[store])): cur_loc = state.at[agent] obj = state.store_has[store] if(obj == None): pyhop.print_state(state) assert(obj != None) state.at[obj] = cur_loc state.store_has.pop(store) state.empty[store] = True return state else: return False
def v17_plan(problem, agent):
problem.a_star = True
problem.rand = True # Only difference from v14
if not hasattr(problem, 'verbose'):
problem.verbose = 0
elif problem.verbose == 1:
print("Problem State: ")
pyhop.print_state(problem)
root = pyhop.seek_bb(problem, problem.goals[agent], verbose=problem.verbose, all_plans=True)
# Even though we get the root, this planner imitates the result of a linear planner.
solutions = [root.get_plan(rand=True)]
if solutions[0] == False:
return solutions
return Planner.make_sol_obj(solutions, problem, agent)
def on_new_plan_req(agents):
pyhop.reset_agents_tasks()
pyhop.set_state("robot", state1_r)
pyhop.set_state("human", state1_h)
for ag, tasks in agents.items():
pyhop.add_tasks(ag, [(t[0], *t[1]) for t in tasks])
pyhop.print_state(pyhop.agents["robot"].state)
print(pyhop.agents["robot"].tasks)
pyhop.print_methods("robot")
pyhop.print_methods("human")
sol = []
plans = pyhop.seek_plan_robot(pyhop.agents, "robot", sol)
print(sol)
rosnode.send_plan(sol)
def simulate_plan_execute(result, state, wait_for_user=False):
if wait_for_user:
print "\n\nSimulating result, type q to quit\n"
tick = 0
data = {}
record_state_to_data(state, data, tick)
for tuple in result:
print tuple
operator = pyhop.operators[tuple[0]]
state = operator(state, *tuple[1:])
pyhop.print_state(state)
record_state_to_data(state, data, tick)
tick += 1
if wait_for_user:
s = raw_input("")
if s == "q":
exit(0)
print "data: ", data
return data
state1_h.isCarrying = {"human": None, "robot": None}
state1_h.onStack = []
state1_r = deepcopy(state1_h)
goal1_h = pyhop.Goal("goal1_h")
goal1_h.isOnStack = {"cube4": True, "cube1": True, "cube6": True}
goal1_h.onStack = ["cube4", "cube1", "cube6"]
goal1_r = deepcopy(goal1_h)
pyhop.set_state("human", state1_h)
pyhop.add_tasks("human", [('stack', goal1_h)])
pyhop.set_state("robot", state1_r)
pyhop.add_tasks("robot", [('stack', goal1_r)])
pyhop.print_state(pyhop.agents["human"].state)
sol = []
plans = pyhop.seek_plan_robot(pyhop.agents, "robot", sol)
rosnode = None
def on_new_plan_req(agents):
pyhop.reset_agents_tasks()
pyhop.set_state("robot", state1_r)
pyhop.set_state("human", state1_h)
for ag, tasks in agents.items():
pyhop.add_tasks(ag, [(t[0], *t[1]) for t in tasks])
pyhop.print_state(pyhop.agents["robot"].state)
def print_state(state): pyhop.print_state(state)
def print_state(state):
pyhop.print_state(state)
