def __init__(self, q = None, s = None, v = None, theta = None, v1 = None, v2 = None):
if not q is None:
# copy
self.s = q.s
self.v = q.v
if not s is None:
# components
self.s = s
self.v = np.array(v)
elif (not v1 is None) and (not v2 is None):
# rotation between two vectors
v1 = vec.normalize(v1)
v2 = vec.normalize(v2)
self.v = np.cross(v1,v2)
self.s = np.dot(v1,v2)
self = self.normalize()
elif not theta is None:
# axis-angle
self.s = math.cos(theta/2.0)
self.v = np.array(v) * math.sin(theta/2.0)
else:
# zero
self.s = 1.0
self.v = np.zeros(3)
if self.isnan():
raise ValueError('Quat nan')
def calcAction(self):
self.action.direction[0] = self.inputDevice.getX()
self.action.direction[1] = self.inputDevice.getY()
if vec.isAlmostZero(self.action.direction):
vec.zeroize(self.action.direction) # TODO: necessary?
self.action.speed = 0.0
else:
self.action.speed = min(1.0, vec.length(self.action.direction))
vec.normalize(self.action.direction, self.action.direction)
def calcAction(self):
self.action.direction[0] = self.inputDevice.getX()
self.action.direction[1] = self.inputDevice.getY()
if vec.isAlmostZero(self.action.direction):
vec.zeroize(self.action.direction) # TODO: necessary?
self.action.speed = 0.0
else:
self.action.speed = min(1.0, vec.length(self.action.direction))
vec.normalize(self.action.direction, self.action.direction)
def draw_circle(bot, center: Vec3, normal: Vec3, radius: float, pieces: int,
color_func):
# Construct the arm that will be rotated
arm = normalize(cross(normal, center)) * radius
angle = 2 * math.pi / pieces
rotation_mat = axis_to_rotation(angle * normalize(normal))
points = [center + arm]
for i in range(pieces):
arm = dot(rotation_mat, arm)
points.append(center + arm)
bot.renderer.draw_polyline_3d(points, color_func())
def predict_hit_pos(self, from_pos):
speed = SUPER_SPEED if self.next_is_super else NORMAL_SPEED
ball_prediction = self.get_ball_prediction_struct()
if ball_prediction is not None:
TIME_PER_SLICES = 1 / 60.0
SLICES = 360
# Iterate to find the first location which can be hit
for i in range(0, 360):
time = i * TIME_PER_SLICES
rlpos = ball_prediction.slices[i].physics.location
pos = Vec3(rlpos.x, rlpos.y, rlpos.z)
dist = norm(from_pos - pos)
travel_time = dist / speed
if time >= travel_time:
# Add small bias for aiming
tsign = -1 if self.team == 0 else 1
enemy_goal = Vec3(0, tsign * -5030, 300)
bias_direction = normalize(pos - enemy_goal)
pos = pos + bias_direction * GOAL_AIM_BIAS_AMOUNT
return pos, travel_time
# Use last
rlpos = ball_prediction.slices[SLICES - 1].physics.location
return Vec3(rlpos.x, rlpos.y, rlpos.z), 6
return Vec3(0, 0, 0), 5
def calcAction(self):
v = self.percepts.myPosition() - self.targetPositionPercept(self.percepts)
# TODO: consider predicating on v.length() e.g. inversely proportional so
# that speed increases as tagged character gets closer
# max(0.0, min(1.0, 1.0 - (0.25*tagDist)/tagFar))
self.action.setSpeed(1.0)
self.action.setDirection(vec.normalize(v, self.tmp))
def setVelocity(self, velocity):
s = vec.length(velocity)
if util.isAlmostZero(s):
self.setSpeed(0)
# Don't change the orientation if the speed is zero.
else:
self.setSpeed(s)
self.shape.setOrientation(vec.normalize(velocity, self.velocity))
def setVelocity(self, velocity):
s = vec.length(velocity)
if util.isAlmostZero(s):
self.setSpeed(0)
# Don't change the orientation if the speed is zero.
else:
self.setSpeed(s)
self.shape.setOrientation(vec.normalize(velocity, self.velocity))
def calcAction(self):
v = self.percepts.myPosition() - self.targetPositionPercept(self.percepts)
# TODO: consider predicating on v.length() e.g. inversely proportional so
# that speed increases as tagged character gets closer
# max(0.0, min(1.0, 1.0 - (0.25*tagDist)/tagFar))
self.action.setSpeed(1.0)
self.action.setDirection(vec.normalize(v, self.tmp))
def to_omega(self, dt): m = vec.mag(self.v) #print self.s,s if m > 0.0: omega = 2.0 * math.asin(m) / dt * vec.normalize(self.v) else: omega = np.zeros(3) ver = False if ver: print 'dt ',dt print 'm ',m print 'r ',self.s,self.v print 'omega',omega return omega
def do_aiming_state(self):
self.expected_hit_pos, travel_time = self.predict_hit_pos(
self.info.my_car.aim_pos)
self.expected_hit_time = self.info.time + travel_time
self.direction = d = normalize(self.expected_hit_pos -
self.info.my_car.aim_pos)
rotation = Rotator(math.asin(d.z), math.atan2(d.y, d.x), 0)
car_state = CarState(
Physics(location=to_fb(self.info.my_car.aim_pos),
velocity=Vector3(0, 0, 0),
rotation=rotation,
angular_velocity=Vector3(0, 0, 0)))
game_state = GameState(cars={self.index: car_state})
self.set_game_state(game_state)
if self.next_flight_start_time < self.info.time:
self.state = self.FLYING
if self.next_is_super:
self.doing_super = True
self.next_is_super = False
else:
self.doing_super = False
def calcAction(self):
# TODO: make this settable
soonThreshold = 50.0
# TODO: consider re-factoring using BrainConditional (or something like it)
# TODO: this is hardwired to only avoid static obstacles
if soonThreshold < self.percepts.myTimeToCollision() or (self.percepts.myNextCollider() and Inf != self.percepts.nextCollider.mass):
# No collision danger
time = self.percepts.getTime()
# How many milliseconds to wait after a potential collision was detected before
# resuming with the default controller. TODO: consider making a settable class variable.
delay = 0.5
if self.timeLastCollisionDetected < 0 or delay < time - self.timeLastCollisionDetected:
self.defaultBrain.calcAction()
self.action = self.defaultBrain.action
else:
# Just continue with last action.
# TODO: consider some time discounted blend of default controller and
# avoidance vector.
pass
return
self.timeLastCollisionDetected = self.percepts.getTime()
# Collision danger present so need to take evasive action.
self.rp = vec.normalize(self.percepts.myNextCollisionPoint() - self.percepts.myPosition(), self.rp)
self.tmp = util2D.perpendicularTo(self.rp, self.percepts.myNextCollider().normalTo(self.percepts.getMe(), self.tmp), self.tmp)[0]
assert util.isAlmostZero(vec.dot(self.rp, self.tmp))
self.action.setDirection(self.tmp)
# TODO: modulate the speed based on time until collision and whatever the defaultControler
# set it to.
self.action.setSpeed(1.0)
def calcAction(self):
# TODO: make this settable
soonThreshold = 50.0
# TODO: consider re-factoring using BrainConditional (or something like it)
# TODO: this is hardwired to only avoid static obstacles
if soonThreshold < self.percepts.myTimeToCollision() or (self.percepts.myNextCollider() and Inf != self.percepts.nextCollider.mass):
# No collision danger
time = self.percepts.getTime()
# How many milliseconds to wait after a potential collision was detected before
# resuming with the default controller. TODO: consider making a settable class variable.
delay = 0.5
if self.timeLastCollisionDetected < 0 or delay < time - self.timeLastCollisionDetected:
self.defaultBrain.calcAction()
self.action = self.defaultBrain.action
else:
# Just continue with last action.
# TODO: consider some time discounted blend of default controller and
# avoidance vector.
pass
return
self.timeLastCollisionDetected = self.percepts.getTime()
# Collision danger present so need to take evasive action.
self.rp = vec.normalize(self.percepts.myNextCollisionPoint() - self.percepts.myPosition(), self.rp)
self.tmp = util2D.perpendicularTo(self.rp, self.percepts.myNextCollider().normalTo(self.percepts.getMe(), self.tmp), self.tmp)[0]
assert util.isAlmostZero(vec.dot(self.rp, self.tmp))
self.action.setDirection(self.tmp)
# TODO: modulate the speed based on time until collision and whatever the defaultControler
# set it to.
self.action.setSpeed(1.0)
def normalTo(c0, c1, n):
return vec.normalize(c1.position - c0.position, n)
def calcAction(self):
v = self.targetPositionPercept(self.percepts) - self.percepts.myPosition()
# TODO: consider predicating speed on v.length()
self.action.setSpeed(1.0)
self.action.setDirection(vec.normalize(v, self.tmp))
def calcAction(self):
v = self.targetPositionPercept(self.percepts) - self.percepts.myPosition()
# TODO: consider predicating speed on v.length()
self.action.setSpeed(1.0)
self.action.setDirection(vec.normalize(v, self.tmp))
def calcAction(self):
vec.normalize(vec.randomVec(self.action.direction), self.action.direction)
self.action.speed = util.clamp(util.uniform(), 0.25, 1.0)
def normalTo(c0, c1, n):
return vec.normalize(c1.position - c0.position, n)
def calcAction(self):
vec.normalize(vec.randomVec(self.action.direction), self.action.direction)
self.action.speed = util.clamp(util.uniform(), 0.25, 1.0)
