def analyse(environment,knowledge,movement,num_goals,
min_dist,alphas,betas,repeat,scenario):
progress = 0
spaces = environment.get_coords(grid_object.labels['empty'])
start = spaces[np.random.randint(0,len(spaces))]
#################################
start = [0,5]
goals = [start]
too_close = []
for g in range(0,num_goals):
for square in spaces:
if grid_object.p2_dist(goals[-1],square)<=min_dist:
too_close.append(square)
for square in too_close:
if square in spaces:
spaces.remove(square)
goals.append(spaces[np.random.randint(0,len(spaces))])
goals.remove(start)
####################################
goals = [[9,5]]
environment.set_goals(goals)
size = (environment.get_width(),environment.get_height())
results = []
for k in knowledge:
obstacles = environment.get_coords(grid_object.labels['obstacle'])
max_obs = environment.get_capacity()
num_obs = int(np.floor(k*max_obs))
initial_obs = []
for n in range(0,num_obs):
if len(obstacles)==0:
break
index = np.random.randint(0,len(obstacles))
initial_obs.append(obstacles[index])
obstacles.remove(obstacles[index])
for m in movement:
move_prob = [1-m,m/2,0,0,0,0,0,m/2]
for p in range(0,len(alphas)):
a = alphas[p]
b = betas[p]
costs = []
colls = []
perfect = journey.simulation(environment,environment,start,
goals,[1,0,0,0,0,0,0,0])
for r in range(0,repeat):
information = grid_object.grid(size,initial_obs,max_obs)
if scenario[0]==1:
information.set_goals(goals)
elif scenario[0]==2:
information.update_sense_range(scenario[1])
actual = journey.simulation(information,environment,start,
goals,move_prob,a,b)
if ((actual[journey.outputs['complete']] and
perfect[journey.outputs['complete']]) or scenario[0]==2):
costs.append(perfect[journey.outputs['cost']]-
actual[journey.outputs['cost']])
colls.append(actual[journey.outputs['collisions']])
if len(costs)>0:
av_cost = sum(costs)/len(costs)
av_coll = sum(colls)/len(colls)
results.append([k,m,a,b,av_cost,av_coll,start,goals,
actual[journey.outputs['record']]])
progress+=1
print(progress)
return results
@author: matth """ import grid_object import search import journey import numpy as np np.set_printoptions(threshold=np.inf) sizeA = (5,5) startA = [0,0] endA = [2,2] #obs=[0,2] ##print(obs) test1 = grid_object.grid(sizeA,[],startA,endA) test1.random_obs(3,[startA,endA]) #test1.update_risk() print(test1.get_state()) #test1.update_heuristic(endA) ##print(test1.get_heuristic()) #result1 = search.find_path(test1,startA,endA) ##print(result1) <<<<<<< HEAD sizeA = (3,3) startA = [0,0] endA = [2,2] obs=[0,2] ##print(obs) test1 = grid_object.grid(sizeA,[obs],startA,endA)
##test2 = grid_object.grid(sizeA,startA,endA,[[1,1]]) ##result2 = search.find_path(test2,startA,endA) sizeB = (15, 15) ob1 = [[3, 4], [4, 4], [3, 3], [4, 5]] ob2 = [[6, 8], [7, 8], [8, 8], [9, 8]] ob3 = [[2, 5], [3, 4], [4, 3], [5, 2]] ob4 = [[10, 12], [11, 12], [13, 12], [12, 11]] obstacles = ob1 + ob2 + ob3 startB = [0, 0] #endB = [9,9] #endC = [0,14] endD = [12, 1] endE = [12, 12] goals = [endD, endE] env = grid_object.grid(sizeB, obstacles, len(obstacles)) #env.update_heuristic(endB) inf = grid_object.grid(sizeB, [], len(obstacles)) #inf.update_heuristic(endB) move_prob1 = [1, 0, 0, 0, 0, 0, 0, 0] move_prob2 = [0.9, 0.1, 0, 0, 0, 0, 0, 0] move_prob3 = [0.8, 0.1, 0, 0, 0, 0, 0, 0.1] env.update_sense_range(3) inf.update_sense_range(3) env.set_goals(goals) inf.set_goals(goals) #env.set_goals([endD])
def get_results(knowledge, movement, results_nb, goals_nb, repeat_nb):
#create an environment...
sizeB = (10, 10)
ob1 = [[3, 4], [4, 4], [3, 3], [4, 5]]
ob2 = [[6, 8], [7, 8], [8, 8], [9, 8]]
ob3 = [[2, 5], [3, 4], [4, 3], [5, 2]]
ob4 = [[5, 6], [5, 7]]
obstacles = ob1 + ob2 + ob3 #+ob4
#
# ob1=[[3,4],[3,6],[4,4],[4,6],[5,4],[5,6],[6,4],[6,6],[7,4],[7,6]]
# obstacles=ob1
obstacles = gridStore.obs
sizeB = (30, 30)
env = grid_object.grid(sizeB, obstacles, len(obstacles))
#create parameter vectors...
num_goals = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
min_dist = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15]
'''
alpha is heuristic
beta is risk
'''
#test for cost
alphas_c = [
1.5, 1.4, 1.3, 1.2, 1.1, 1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2,
0.1, 0.0
]
betas__c = [
1.5, 1.4, 1.3, 1.2, 1.1, 1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2,
0.1, 0.0
]
#test for heuristic
alphas_h = [
0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0, 2.2, 2.4, 2.6,
2.8, 3.0
]
betas__h = [
1.5, 1.4, 1.3, 1.2, 1.1, 1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2,
0.1, 0.0
]
#test fot risk
alphas_r = [
1.5, 1.4, 1.3, 1.2, 1.1, 1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2,
0.1, 0.0
]
betas__r = [
0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0, 2.2, 2.4, 2.6,
2.8, 3.0
]
repeat = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
results = []
alphas = [alphas_r[-2]]
betas = [betas__r[-2]]
alphas = [1]
betas = [1]
for i in range(0, results_nb):
results.append(
performance.analyse(env, knowledge, movement, num_goals[goals_nb],
min_dist[5], alphas, betas, repeat[repeat_nb],
[2, 4]))
env.update_path_colour(results[0][0][-1], results[0][0][-3],
results[0][0][-2])
env.show_me()
return results
import grid_object import search import journey import performance import numpy as np import animation np.set_printoptions(threshold=np.inf) sizeA = (5, 5) startA = [0, 0] endA = [2, 2] #obs=[0,2] ##print(obs) test1 = grid_object.grid(sizeA, [], 0) test1.random_obs(3, [startA, endA]) #test1.update_risk() #print(test1.get_state()) #test1.update_heuristic(endA) ##print(test1.get_heuristic()) #result1 = search.find_path(test1,startA,endA) ##print(result1) ##test2 = grid_object.grid(sizeA,startA,endA,[[1,1]]) ##result2 = search.find_path(test2,startA,endA) sizeB = (15, 15) ob1 = [[3, 4], [4, 4], [3, 3], [4, 5]] ob2 = [[6, 8], [7, 8], [8, 8], [9, 8]] ob3 = [[2, 5], [3, 4], [4, 3], [5, 2]] ob4 = [[10, 12], [11, 12], [13, 12], [12, 11]]