def optimum_block(self, robot, env):
block_list = env.blocks
if len(block_list) == 1:
if robot.robot_id == 0 and env.red_dropped_off < env.blue_dropped_off and env.blue_dropped_off > 2:
return (block_list[0][0], block_list[0][1])
elif robot.robot_id == 1 and env.red_dropped_off > env.blue_dropped_off and env.red_dropped_off > 2:
return (block_list[0][0], block_list[0][1])
elif env.red_dropped_off == env.blue_dropped_off:
pass
else:
return None
new_block_list = []
cur_pos = robot.current_position()
for block in block_list:
p = (block[0], block[1])
block_position = (block[0], block[1])
if robot.robot_id == 0:
other_target = self.blue_current_target
else:
other_target = self.red_current_target
if h(other_target, p) < 20:
continue
if not (robot.path_possible([int(block[0]), int(block[1])])):
continue
else:
distance = h(p, cur_pos)
other_robot_distance = h(
convert_to_grid_coords(
robot.robot_data[(robot.robot_id + 1) % 2][1:3]), p)
danger_coord, danger = env.optimum_heading(
[block[0], block[1]],
convert_to_grid_coords(
robot.robot_data[(robot.robot_id + 1) % 2][1:3]))
if danger >= 6:
if not (robot.path_possible(
[int(danger_coord[0]),
int(danger_coord[1])])):
print('No danger path')
continue
combined_heuristic = distance * 2 + -other_robot_distance * 1 + danger * 1 - h(
other_target, p)
print(combined_heuristic)
if robot.robot_id + block[2] == 2:
new_block_list.append(
[block[0], block[1], (combined_heuristic) * 0.3])
elif block[2] == 0:
new_block_list.append(
[block[0], block[1], combined_heuristic])
if new_block_list == []:
return None
optblock = min(new_block_list, key=lambda x: x[2])
return (optblock[0], optblock[1])
def choose_block(self, block_list, robot, factor=0.5, env=None):
cur_pos = robot.current_position()
new_block_list = []
for block in block_list:
p = (block[0], block[1])
dis = h(p, cur_pos)
if env:
_, danger = env.optimum_heading(
[block[0], block[1]],
convert_to_grid_coords(
robot.robot_data[(robot.robot_id + 1) % 2][1:3]))
else:
danger = 0
print('dis: ' + str(dis))
print('danger: ' + str(danger))
if robot.robot_id == 0:
other_target = self.blue_current_target
else:
other_target = self.red_current_target
if h(other_target, p) < 20:
continue
if robot.robot_id + block[2] == 2:
new_block_list.append(
[block[0], block[1], (dis + danger * 2) * factor])
elif block[2] == 0:
new_block_list.append([block[0], block[1], dis + danger * 2])
if new_block_list == []:
return None
optblock = min(new_block_list, key=lambda x: x[2])
return (optblock[0], optblock[1])
def optimum_block(self, robot, env): block_list = env.blocks new_block_list = [] cur_pos = robot.current_position() for block in block_list: p = (block[0],block[1]) block_position = (block[0], block[1]) if robot.robot_id ==0: other_target = self.blue_current_target else: other_target = self.red_current_target if h(other_target, p) <20: continue if not(robot.path_possible([int(block[0]),int(block[1])])): continue else: distance = h(p, cur_pos) other_robot_distance = h(convert_to_grid_coords(robot.robot_data[(robot.robot_id+1)%2][1:3]), p) _, danger = env.optimum_heading([block[0],block[1]], convert_to_grid_coords(robot.robot_data[(robot.robot_id+1)%2][1:3])) combined_heuristic = distance*1 + -other_robot_distance*1 + danger*2 - h(other_target, p) if robot.robot_id + block[2] ==2: new_block_list.append([block[0],block[1],(combined_heuristic)*0.5]) elif block[2] ==0: new_block_list.append([block[0], block[1], combined_heuristic]) if new_block_list == []: return None optblock = min(new_block_list, key=lambda x: x[2]) return (optblock[0],optblock[1])
def choose_block(block_list, robot, factor=0.5):
cur_pos = robot.current_position()
new_block_list = []
for block in block_list:
p = (block[0], block[1])
dis = h(p, cur_pos)
if robot.robot_id == 0:
other_target = blue_current_target
else:
other_target = red_current_target
if h(other_target, p) < 15:
continue
if robot.robot_id + block[2] == 2:
new_block_list.append([block[0], block[1], dis * factor])
elif block[2] == 0:
new_block_list.append([block[0], block[1], dis])
if new_block_list == []:
return None
optblock = min(new_block_list, key=lambda x: x[2])
return (optblock[0], optblock[1])
def choose_block(block_list, robot, factor):
cur_pos = robot.current_position()
new_block_list = []
for block in block_list:
p = (block[0], block[1])
dis = h(p, cur_pos)
if robot.robot_id + block[2] == 2:
new_block_list.append([block[0], block[1], dis * factor])
elif block[2] == 0:
new_block_list.append([block[0], block[1], dis])
if new_block_list == []:
if robot.robot_id == 0:
return (7, 73)
else:
return (7, 7)
optblock = min(new_block_list, key=lambda x: x[2])
return (optblock[0], optblock[1])