def vertical_path(x, y, start_h, end_h):
"""生成垂直路径"""
if start_h < end_h:
return [Coordinate(x, y, i)
for i in range(start_h, end_h + 1)]
else:
return [Coordinate(x, y, i)
for i in reversed(range(end_h, start_h + 1))]
def lab():
coordinates = [
Coordinate(33.643653, -117.841143),
Coordinate(33.649985, -117.831387),
Coordinate(33.687698, -117.771220)
]
coordinates_json = [coordinate.getJSON() for coordinate in coordinates]
json = {"status": 200, "coordinates": coordinates_json}
return jsonify(json)
def loc_path_to_coordinate_path(path: [libjpsp.XYLoc], height) -> [Coordinate]:
res = []
for loc in path:
res.append(Coordinate(loc.x, loc.y, height))
return res
def read_csv():
with open('example.csv', 'rb') as csvfile:
spamreader = csv.reader(csvfile)
count = 0
for row in spamreader:
if count == 0:
count += 1
pass
else:
print row #array
#should loop through the row and push elements of the array into the DB
location = row[0]
date = row[1]
notes = row[2]
lattitude = row[3]
lattitude_direction = row[4]
longitude = row[5]
print longitude
longitude_direction = row[6]
coordinate = Coordinate(
location=location,
date=date,
notes=notes,
lattitude=lattitude,
lattitude_direction=lattitude_direction,
longitude=longitude,
longitude_direction=longitude_direction)
db.session.add(coordinate)
db.session.commit()
count += 1
def findRoutes():
start_time = time()
body = None
body = request.get_json(force=True)
temp = request.args.get('use_heuristic')
flag = False
if temp != "false":
flag = True
print(body)
lista = list(body)
if len(lista) < 2:
return []
destinations = None
destinations = RouteController()
for i in lista:
destinations.add_cord(Coordinate(i['lat'], i['lng'], 'A'))
#TODO
sa = None
sa = SimulatedAnnealing(destinations,
initial_temperature=1000,
cooling_rate=0.0015,
use_heuristic=flag)
sa.run()
response = []
for i in sa.best:
response.append({'lat': i.lat, 'lng': i.long})
elapsed_time = time() - start_time
print("Elapsed time: %0.10f seconds." % elapsed_time)
return jsonify(response)
def gen_random_points(num_points, map_info: MapInfo):
"""
生成当前地图中的合法的随机点，把空闲无人机分散到各个角落
:param num_points: 所需生成的坐标点数量
:param map_info: 当前地图信息
:return:
"""
low, high = 0, map_info.map_range.x
xy = np.random.randint(low=low, high=high, size=(2, num_points)) # 生成随机坐标
points = [
Coordinate(x=x, y=y, z=map_info.h_low)
for x, y in zip(xy[0, :], xy[1, :])
]
# 对于不合法的点(和障碍物重叠)，重新生成
for i in range(len(points)):
while points[i].is_overlap(map_info.buildings):
x, y = np.random.randint(low=0, high=map_info.map_range.x, size=2)
points[i] = Coordinate(x, y, map_info.h_low)
return points
def _search(self, start, end, search_height, obstacles=None):
"""调用搜索算法进行路径搜索。"""
if self.map_info.map_range.x > 0 and env.jpsp_finders \
and search_height in env.jpsp_finders:
# 大地图并且存在合法的JPSPlus对象，使用JPS算法搜索
# print("UAV %d, JPS Plus search, height %d, " % (self.uav.no, search_height), end='')
# jpsp_start_time = time.time()
# jpsp_path = env.jpsp_finders[search_height].get_path(
# xy_to_loc(start.x, start.y), xy_to_loc(end.x, end.y))
# print("time %s" % (time.time() - jpsp_start_time))
print("UAV %d, JPSPlus search, height %d, " % (self.uav.no, search_height), end='')
jpsp_start_time = time.time()
jpsp_path = env.jpsp_finders[search_height].get_path(
bridge.XYLoc(start.x, start.y), bridge.XYLoc(end.x, end.y))
print("time %s" % (time.time() - jpsp_start_time))
trans_start = time.time()
search_result = bridge.to_coord_path(path=jpsp_path, height=search_height)
print("Transform time %s" % (time.time() - trans_start))
else:
print("UAV %d, A-Star search, height %d, " % (self.uav.no, search_height), end='')
self.problem.set_config(start=Coordinate(start.x, start.y, search_height),
end=Coordinate(end.x, end.y, search_height),
h_low=self.map_info.h_low,
h_high=self.map_info.h_high,
obstacles=obstacles,
map_range=self.map_info.map_range)
a_star_start_time = time.time()
search_result = astar(self.problem, graph_search=True)
print("time %s" % (time.time() - a_star_start_time))
search_result = [c for _, c in search_result.path()]
return search_result if search_result else []
def main():
destinations = RouteController()
ciudad1 = Coordinate(-12.095266834590417, -77.0542065585971, 'Lima')
destinations.add_cord(ciudad1)
ciudad1 = Coordinate(-12.08264894268049, -77.04563370491826, 'Argentina')
destinations.add_cord(ciudad1)
ciudad2 = Coordinate(-12.092396932744766, -77.03341079647996, 'Brasil')
destinations.add_cord(ciudad2)
ciudad3 = Coordinate(-12.071923319293719, -77.0513069176675, 'Bolivia')
destinations.add_cord(ciudad3)
ciudad4 = Coordinate(-12.064787751282571, -77.03733650569475, 'Paraguay')
destinations.add_cord(ciudad4)
sa = SimulatedAnnealing(destinations,
initial_temperature=1000,
cooling_rate=0.0000003,
use_heuristic=False)
print(sa.route.show())
print(sa.route.get_distance(sa.dictionary))
sa.run()
print(sa.route.show())
print(sa.route.get_distance(sa.dictionary))
print("Termino el algoritmo con %i vueltas" % sa.iterations)
def findRoutes():
start_time = time()
body = None
body = request.get_json(force=True)
lista = list(body)
if len(lista) < 2:
return []
destinations = []
for i in lista:
destinations.append(Coordinate(i['lat'], i['lng'], 'A'))
#TODO
ga = None
ga = GeneticAlgorithm.GeneticAlgorithm(destinations, 0.5, 10)
ga.generations(2000)
best = ga.getBestIndividuo()
response = []
for i in best:
response.append({'lat': i.lat, 'lng': i.long})
elapsed_time = time() - start_time
print("Elapsed time: %0.10f seconds." % elapsed_time)
return jsonify(response)
self.plan(self.uav.loc, self.goods.end, task_type=TaskType.TO_GOODS_END)
self.next_step = self.uav.loc
# res = self.path[self.index]
# self.index += 1
elif self.uav.loc == self.goods.end:
# 货物送到目的地
self.reset() # 此时无人机算空闲, 直接重置状态
# 飞机垂直上升到(x, y, h_low)
self.path = self.vertical_path(
self.uav.loc.x, self.uav.loc.y, self.uav.loc.z, self.map_info.h_low)
self.next_step = self.path[self.index]
self.index += 1
else:
# 有可能出错了，暂时原地不动
self.next_step = self.uav.loc
if __name__ == '__main__':
_a = Coordinate(1, 0, 0)
_b = Coordinate(0, 0, 0)
for di in HORIZONTAL_DIRECTIONS:
_next_a = _a + di
for dj in HORIZONTAL_DIRECTIONS:
_next_b = _b + dj
res = is_encounter(_a, _next_a, _b, _next_b)
if res:
print(_next_a, _next_b, res)
def xy_loc_to_coord(loc: XYLoc, height: int):
return Coordinate(loc.x, loc.y, height)
def to_coordinate_path(path: [Point], height) -> [Coordinate]:
coordinate_path = []
for point in path:
x, y = point_to_xy(point)
coordinate_path.append(Coordinate(x, y, height))
return coordinate_path
def _earlier(our_loc, enemy_loc, end):
x, y = end.x, end.y
h_low = map_info.h_low
return manhattan_dis_3d(our_loc, Coordinate(x, y, h_low), h_low) < \
manhattan_dis_3d(enemy_loc, Coordinate(x, y, 0), h_low)
def attack_enemy(map_info: MapInfo, step_info: StepInfo):
"""
主动攻击敌方无人机，主动攻击的己方无人机需要满足两个如下条件：
1.无任务在身。2.价值最小或者比攻击的敌方无人机价值小
:param map_info: 地图信息：障碍物，地图大小，停机坪位置。。。
:param step_info: 当前赛场信息，包括己方无人机信息，敌方无人机信息，货物信息等等。
:return: 不需要返回，直接对Agent进行操作
"""
print('attack enemy')
_threshold = manhattan_distance(Coordinate(0, 0, 0),
map_info.map_range) / 2
print('threshold', _threshold)
def _near(coord1, coord2, threshold):
""" simplify, decide if attacker is near end """
return manhattan_distance(coord1, coord2) <= threshold
def _done(enemy_id): # 需要考虑雾区
uav = step_info.uav_enemy[enemy_id]
return uav.status == UAVStatus.CRASHED or uav.goods_no == -1
def _valuable(enemy_id):
# enemy should carry good, and good lifetime should be valid
uav = step_info.uav_enemy[enemy_id]
if uav.goods_no == -1:
return False
# return True
good = step_info.goods[uav.goods_no]
return good.left_time > manhattan_dis_3d(uav.loc, good.end,
map_info.h_low)
def _earlier(our_loc, enemy_loc, end):
x, y = end.x, end.y
h_low = map_info.h_low
return manhattan_dis_3d(our_loc, Coordinate(x, y, h_low), h_low) < \
manhattan_dis_3d(enemy_loc, Coordinate(x, y, 0), h_low)
# attack ends -> idle
for agent in env.agents.values():
if agent.task_type == TaskType.ATTACK_ENEMY:
enemy_id = env.attackerToEnemy.get(agent.uav.no)
if enemy_id and _done(enemy_id):
env.attackerToEnemy.pop(agent.uav.no)
agent.task_type = TaskType.NO_TASK
print('ATTACK -> NO_TASK')
# idle -> attack do assign & plan
for agent in env.agents.values():
if agent.task_type == TaskType.NO_TASK or agent.task_type == TaskType.TO_RANDOM_POINT:
for enemy_uav in step_info.uav_enemy.values():
_enemy_id = enemy_uav.no
if _enemy_id not in env.attackerToEnemy.values() \
and enemy_uav.status == UAVStatus.NORMAL \
and _valuable(_enemy_id): # enemy 没有被assign && 可见 && 有价值
print('In here')
goods_no = step_info.uav_enemy[_enemy_id].goods_no
x, y = step_info.goods[goods_no].end.x, step_info.goods[
goods_no].end.y
_end = Coordinate(x, y, map_info.h_low)
# if _near(agent.uav.loc, _end, threshold):
if _near(agent.uav.loc, _end, _threshold) and _earlier(
agent.uav.loc, enemy_uav.loc, _end):
print('NO_TASK->ATTACK')
env.attackerToEnemy[agent.uav.no] = _enemy_id
agent.plan(agent.uav.loc,
_end,
task_type=TaskType.ATTACK_ENEMY)