def best_intercept(self, cars: List[Car]) -> Intercept:
if not cars:
return Intercept(Car(), self.info.ball_predictions)
intercepts = [
Intercept(car, self.info.ball_predictions) for car in cars
]
return min(intercepts, key=lambda intercept: intercept.time)
def simulate_landing(self):
dummy = Car(self.car)
self.trajectory = [vec3(dummy.position)]
self.landing = False
collision_normal: Optional[vec3] = None
dt = 1 / 60
simulation_duration = 0.8
for i in range(int(simulation_duration / dt)):
dummy.step(Input(), dt)
self.trajectory.append(vec3(dummy.position))
collision_sphere = sphere(dummy.position, 50)
collision_ray = Field.collide(collision_sphere)
collision_normal = collision_ray.direction
if (norm(collision_normal) > 0.0
or dummy.position[2] < 0) and i > 20:
self.landing = True
self.landing_pos = dummy.position
break
if self.landing:
u = collision_normal
f = normalize(dummy.velocity - dot(dummy.velocity, u) * u)
l = normalize(cross(u, f))
self.aerial_turn.target = mat3(f[0], l[0], u[0], f[1], l[1], u[1],
f[2], l[2], u[2])
else:
target_direction = normalize(
normalize(self.car.velocity) - vec3(0, 0, 3))
self.aerial_turn.target = look_at(target_direction, vec3(0, 0, 1))
def simulate(self):
ball_prediction = self.get_ball_prediction_struct()
duration_estimate = math.floor(
get_time_at_height(self.game.ball.location[2], 0.2) * 10) / 10
for i in range(6):
car = Car(self.game.my_car)
ball = Ball(self.game.ball)
batmobile = obb()
batmobile.half_width = vec3(64.4098892211914, 42.335182189941406,
14.697200775146484)
batmobile.center = car.location + dot(car.rotation,
vec3(9.01, 0, 12.09))
batmobile.orientation = car.rotation
dodge = Dodge(car)
dodge.duration = duration_estimate + i / 60
dodge.target = ball.location
for j in range(round(60 * dodge.duration)):
dodge.target = ball.location
dodge.step(1 / 60)
car.step(dodge.controls, 1 / 60)
prediction_slice = ball_prediction.slices[j]
physics = prediction_slice.physics
ball_location = vec3(physics.location.x, physics.location.y,
physics.location.z)
dodge.target = ball_location
batmobile.center = car.location + dot(car.rotation,
vec3(9.01, 0, 12.09))
batmobile.orientation = car.rotation
if intersect(sphere(ball_location, 93.15), batmobile) and abs(
ball_location[2] - car.location[2]
) < 25 and car.location[2] < ball_location[2]:
return True, j / 60, ball_location
return False, None, None
def __init__(self, car: Car, ball_predictions, predicate: callable = None):
self.ball: Ball = None
self.is_viable = True
#find the first reachable ball slice that also meets the predicate
test_car = Car(car)
test_aerial = Aerial(car)
for ball in ball_predictions:
test_aerial.target = ball.position
test_aerial.arrival_time = ball.time
# fake our car state :D
dir_to_target = ground_direction(test_car.position, test_aerial.target)
test_car.velocity = dir_to_target * max(norm(test_car.velocity), 1200)
test_car.orientation = look_at(dir_to_target, vec3(0,0,1))
if test_aerial.is_viable() and (predicate is None or predicate(car, ball)):
self.ball = ball
break
#if no slice is found, use the last one
if self.ball is None:
self.ball = ball_predictions[-1]
self.is_viable = False
self.time = self.ball.time
self.ground_pos = ground(self.ball.position)
self.position = self.ball.position
def car_trajectory(self, car: Car, end_time: float, dt: float = 1 / 10):
steps = []
test_car = Car(car)
while test_car.time < end_time:
dt = min(dt, end_time - test_car.time)
test_car.step(Input(), dt)
test_car.time += dt
steps.append(vec3(test_car.position))
self.polyline(steps)
def step(self, dt):
if self.aerialing:
self.aerial.target_orientation = look_at(
direction(self.car, self.target), vec3(0, 0, -1))
self.aerial.step(dt)
self.controls = self.aerial.controls
self.finished = self.aerial.finished
else:
super().step(dt)
# simulate aerial from current state
simulated_car = self.simulate_flight(self.car)
# if the car ended up too far, we're too fast and we need to slow down
if ground_distance(self.car,
self.aerial.target) + 100 < ground_distance(
self.car, simulated_car):
# self.controls.throttle = -1
pass
# if it ended up near the target, we could take off
elif distance(simulated_car,
self.aerial.target) < self.MAX_DISTANCE_ERROR:
if angle_to(self.car, self.aerial.target) < 0.1 or norm(
self.car.velocity) < 1000:
if self.DELAY_TAKEOFF:
# extrapolate current state a small amount of time
future_car = Car(self.car)
time = 0.2
future_car.time += time
displacement = future_car.velocity * time if norm(future_car.velocity) > 500\
else normalize(future_car.velocity) * 500 * time
future_car.position += displacement
# simulate aerial fot the extrapolated car again
future_simulated_car = self.simulate_flight(
future_car, write_to_flight_path=False)
# if the aerial is also successful, that means we should continue driving instead of taking off
# this makes sure that we go for the most late possible aerials, which are the most effective
if distance(
future_simulated_car,
self.aerial.target) > self.MAX_DISTANCE_ERROR:
self.aerialing = True
else:
self.too_early = True
else:
self.aerialing = True
else:
# self.controls.boost = True
self.controls.throttle = 1
def simulate(self, bot) -> vec3:
# print('simulate intercept')
# Init vars
c = Car(bot.game.my_car)
b = Ball(bot.game.ball)
t = vec3(bot.target)
intercept = self.location
dt = 1.0 / 60.0
hit = False
min_error = None
# Drive towards intercept (moving in direction of c.forward())
c.rotation = look_at(intercept, c.up())
direction = normalize(intercept - c.location) #c.forward()
advance_distance = norm(intercept - c.location) - c.hitbox(
).half_width[0] - b.collision_radius
translation = direction * advance_distance
sim_start_state: ThrottleFrame = BoostAnalysis().travel_distance(
advance_distance, norm(c.velocity))
c.velocity = direction * sim_start_state.speed
c.location += translation
c.time += sim_start_state.time
bot.ball_predictions = [vec3(b.location)]
while b.time < c.time:
b.step(dt)
bot.ball_predictions.append(vec3(b.location))
# print(c.time, b.time)
# print(c.location, b.location)
# Simulate the collision and resulting
for i in range(60 * 3):
c.location += c.velocity * dt
b.step(dt, c)
# Check if we hit the ball yet
if norm(b.location - c.location) < (c.hitbox().half_width[0] +
b.collision_radius) * 1.05:
hit = True
# print('hit')
# Measure dist from target
error = t - b.location
if hit and (min_error == None or norm(error) < norm(min_error)):
min_error = error
# Record trajectory
bot.ball_predictions.append(vec3(b.location))
if not hit: return None
return min_error
def best_intercept(self, cars, max_height=9999) -> Intercept:
best_intercept = None
best_car = None
for car in cars:
intercept = Intercept(car, self.info.ball_predictions, lambda car, ball: ball.position[2] < max_height)
if best_intercept is None or intercept.time <= best_intercept.time:
best_intercept = intercept
best_car = car
if best_intercept is None:
best_car = Car()
best_intercept = Intercept(best_car, [])
return best_intercept, best_car
def simulate_flight(car: Car, aerial: Aerial, flight_path: List[vec3] = None) -> Car:
test_car = Car(car)
test_aerial = Aerial(test_car)
test_aerial.target = aerial.target
test_aerial.arrival_time = aerial.arrival_time
test_aerial.angle_threshold = aerial.angle_threshold
test_aerial.up = aerial.up
test_aerial.single_jump = aerial.single_jump
if flight_path: flight_path.clear()
while not test_aerial.finished:
test_aerial.step(1 / 120)
test_car.boost = 100 # TODO: fix boost depletion in RLU car sim
test_car.step(test_aerial.controls, 1 / 120)
if flight_path:
flight_path.append(vec3(test_car.position))
return test_car
def simulate_flight(self, car: Car, write_to_flight_path=True) -> Car:
test_car = Car(car)
test_aerial = Aerial(test_car)
test_aerial.target = self.aerial.target
test_aerial.arrival_time = self.aerial.arrival_time
test_aerial.angle_threshold = self.aerial.angle_threshold
test_aerial.up = self.aerial.up
test_aerial.single_jump = self.aerial.single_jump
if write_to_flight_path:
self._flight_path.clear()
while not test_aerial.finished:
test_aerial.step(1 / 120)
test_car.boost = 100 # TODO: fix boost depletion in RLU car sim
test_car.step(test_aerial.controls, 1 / 120)
if write_to_flight_path:
self._flight_path.append(vec3(test_car.position))
return test_car
def reset_gamestate(self):
print('> reset_gamestate()')
# Initialize inputs
self.reset_for_ground_shots()
t = self.target
b = Ball(self.game.ball)
c = Car(self.game.cars[self.index])
b.location = to_vec3(self.initial_ball_location)
b.velocity = to_vec3(self.initial_ball_velocity)
c.location = to_vec3(self.initial_car_location)
c.velocity = to_vec3(self.initial_car_velocity)
# Point car at ball
c.rotation = look_at(
vec3(b.location[0] - c.location[0], b.location[1] - c.location[1],
0), vec3(0, 0, 1))
rotator = rotation_to_euler(c.rotation)
# Reset
self.aerial = None
self.dodge = None
self.rotation_input = None
self.timer = 0.0
# Set gamestate
car_state = CarState(boost_amount=100,
physics=Physics(
location=self.initial_car_location,
velocity=self.initial_car_velocity,
rotation=rotator,
angular_velocity=Vector3(0, 0, 0)))
ball_state = BallState(
Physics(location=self.initial_ball_location,
velocity=self.initial_ball_velocity,
rotation=Rotator(0, 0, 0),
angular_velocity=Vector3(0, 0, 0)))
game_state = GameState(ball=ball_state, cars={self.index: car_state})
self.set_game_state(game_state)
from rlutilities.simulation import Car, Navigator
from rlutilities.mechanics import FollowPath
from rlutilities.linear_algebra import vec3, normalize
c = Car()
c.time = 0.0
c.position = vec3(0, 0, 0)
c.velocity = vec3(1000, 0, 0)
c.angular_velocity = vec3(0.1, -2.0, 1.2)
c.on_ground = False
navigator = Navigator(c)
drive = FollowPath(c)
drive.arrival_speed = 1234
drive.path = navigator.path_to(vec3(1000, 0, 0), vec3(1, 0, 0), 1000)
for p in drive.path.points:
print(p)
def get_output(self, packet: GameTickPacket) -> SimpleControllerState:
# Record start time
self.tick_start = time.time()
# Gather some information about our car and the ball
my_car: CarState = packet.game_cars[self.index]
car_location = Vec3(my_car.physics.location)
car_velocity = Vec3(my_car.physics.velocity)
car_direction = car_velocity.ang_to(Vec3(
1, 0, 0)) if car_velocity.length() > 0 else 0
ball_location = Vec3(packet.game_ball.physics.location)
ball_velocity = Vec3(packet.game_ball.physics.velocity)
ball_direction = ball_velocity.ang_to(Vec3(
1, 0, 0)) if ball_velocity.length() > 0 else 0
reset = False
# Initialize simulation game model
if self.game == None:
Game.set_mode('soccar')
self.game = Game(self.index, self.team)
self.game.read_game_information(packet, self.get_rigid_body_tick(),
self.get_field_info())
self.target = vec3(
0, 5120 + 880 / 2 if self.team is 0 else -(5120 + 880 / 2),
642.775 / 2) # Opposing net
self.reset_gamestate()
print('TEAM', self.team)
return SimpleControllerState()
# Update simulation
self.game.read_game_information(packet, self.get_rigid_body_tick(),
self.get_field_info())
# Check for car hit ball
if self.last_touch_location != packet.game_ball.latest_touch.hit_location:
self.last_touch_location = Vec3(
packet.game_ball.latest_touch.hit_location)
print(f'> Car hit ball')
self.not_hit_yet = False
# Recalculate intercept every frame
self.plan()
# Re-simulate the aerial every frame
if self.aerial is not None and not self.aerial.finished:
simulate_aerial(self)
simulate_alternate_aerials(self)
# Update dodge (init or clean up old)
# try_init_dodge(self)
# Rendering
if len(self.ball_predictions) > 2:
self.renderer.draw_polyline_3d(self.ball_predictions,
self.renderer.red())
if self.aerial != None and self.aerial.target:
self.renderer.draw_rect_3d(self.aerial.target,
8,
8,
True,
self.renderer.green(),
centered=True)
self.renderer.draw_line_3d(car_location, self.aerial.target,
self.renderer.white())
self.renderer.draw_line_3d(vec3_to_Vec3(self.target),
self.target + self.avg_aerial_error,
self.renderer.cyan())
if self.intercept != None:
self.renderer.draw_rect_3d(self.intercept.location,
8,
8,
True,
self.renderer.green(),
centered=True)
self.renderer.draw_rect_3d(vec3_to_Vec3(self.target),
8,
8,
True,
self.renderer.green(),
centered=True)
# Controller state
if reset:
self.reset_gamestate()
return SimpleControllerState()
# "Do a flip!"
elif self.dodge is not None:
if self.dodge.finished:
self.dodge = None
return SimpleControllerState()
self.dodge.step(self.game.time_delta)
return self.dodge.controls
# Just do an aerial :4head:
elif self.aerial is not None:
aerial_step(self.aerial, Car(self.game.my_car),
self.rotation_input, self.game.time_delta)
return self.aerial.controls
# Just hit the ball :4head:
elif self.intercept is not None:
if self.intercept.dodge and abs(self.game.time - self.intercept.
jump_time) <= self.game.time_delta:
print('im gonna nut')
self.dodge = Dodge(self.game.my_car)
self.dodge.duration = 0.2
self.dodge.preorientation = self.intercept.dodge_preorientation
self.dodge.delay = self.intercept.dodge_delay + 0.1
self.dodge.direction = self.intercept.dodge_direction
self.dodge.step(self.game.time_delta)
return self.dodge.controls
return self.intercept.get_controls(
my_car, self.game.my_car
) #drive_at(self, my_car, self.intercept.location)
return SimpleControllerState()
def simulate(self, global_target=None):
lol = 0
# Initialize the ball prediction
# Estimate the probable duration of the jump and round it down to the floor decimal
ball_prediction = self.get_ball_prediction_struct()
if self.info.my_car.boost < 6:
duration_estimate = math.floor(
get_time_at_height(self.info.ball.position[2]) * 10) / 10
else:
adjacent = norm(
vec2(self.info.my_car.position - self.info.ball.position))
opposite = (self.info.ball.position[2] -
self.info.my_car.position[2])
theta = math.atan(opposite / adjacent)
t = get_time_at_height_boost(self.info.ball.position[2], theta,
self.info.my_car.boost)
duration_estimate = (math.ceil(t * 10) / 10)
# Loop for 6 frames meaning adding 0.1 to the estimated duration. Keeps the time constraint under 0.3s
for i in range(6):
# Copy the car object and reset the values for the hitbox
car = Car(self.info.my_car)
# Create a dodge object on the copied car object
# Direction is from the ball to the enemy goal
# Duration is estimated duration plus the time added by the for loop
# preorientation is the rotation matrix from the ball to the goal
# TODO make it work on both sides
# Test with preorientation. Currently it still picks a low duration at a later time meaning it
# wont do any of the preorientation.
dodge = Dodge(car)
prediction_slice = ball_prediction.slices[round(
60 * (duration_estimate + i / 60))]
physics = prediction_slice.physics
ball_location = vec3(physics.location.x, physics.location.y,
physics.location.z)
# ball_location = vec3(0, ball_y, ball_z)
dodge.duration = duration_estimate + i / 60
if dodge.duration > 1.4:
break
if global_target is not None:
dodge.direction = vec2(global_target - ball_location)
target = vec3(vec2(global_target)) + vec3(
0, 0, jeroens_magic_number * ball_location[2])
dodge.preorientation = look_at(target - ball_location,
vec3(0, 0, 1))
else:
dodge.target = ball_location
dodge.direction = vec2(ball_location) + vec2(ball_location -
car.position)
dodge.preorientation = look_at(ball_location, vec3(0, 0, 1))
# Loop from now till the end of the duration
fps = 30
for j in range(round(fps * dodge.duration)):
lol = lol + 1
# Get the ball prediction slice at this time and convert the location to RLU vec3
prediction_slice = ball_prediction.slices[round(60 * j / fps)]
physics = prediction_slice.physics
ball_location = vec3(physics.location.x, physics.location.y,
physics.location.z)
dodge.step(1 / fps)
T = dodge.duration - dodge.timer
if T > 0:
if dodge.timer < 0.2:
dodge.controls.boost = 1
dodge.controls.pitch = 1
else:
xf = car.position + 0.5 * T * T * vec3(
0, 0, -650) + T * car.velocity
delta_x = ball_location - xf
if angle_between(vec2(car.forward()),
dodge.direction) < 0.3:
if norm(delta_x) > 50:
dodge.controls.boost = 1
dodge.controls.throttle = 0.0
else:
dodge.controls.boost = 0
dodge.controls.throttle = clip(
0.5 * (200 / 3) * T * T, 0.0, 1.0)
else:
dodge.controls.boost = 0
dodge.controls.throttle = 0.0
else:
dodge.controls.boost = 0
car.step(dodge.controls, 1 / fps)
succesfull = self.dodge_succesfull(car, ball_location, dodge)
if succesfull is not None:
if succesfull:
return True, j / fps, ball_location
else:
break
return False, None, None
def step(self, dt):
time_left = self.aerial.arrival_time - self.car.time
if self.aerialing:
to_ball = direction(self.car, self.info.ball)
# freestyling
if self.car.position[2] > 200:
# if time_left > 0.7:
# rotation = axis_to_rotation(self.car.forward() * 0.5)
# self.aerial.up = dot(rotation, self.car.up())
# else:
self.aerial.up = vec3(0, 0, -1) + xy(to_ball)
self.aerial.target_orientation = look_at(to_ball,
vec3(0, 0, -3) + to_ball)
self.aerial.step(dt)
self.controls = self.aerial.controls
self.finished = self.aerial.finished and time_left < -0.3
else:
super().step(dt)
# simulate aerial from current state
simulated_car = self.simulate_flight(self.car, self.aerial,
self._flight_path)
speed_towards_target = dot(
self.car.velocity,
ground_direction(self.car, self.aerial.target_position))
speed_needed = ground_distance(
self.car, self.aerial.target_position) / time_left
# too fast, slow down
if speed_towards_target > speed_needed and angle_to(
self.car, self.aerial.target_position) < 0.1:
self.controls.throttle = -1
# if it ended up near the target, we could take off
elif distance(
simulated_car,
self.aerial.target_position) < self.MAX_DISTANCE_ERROR:
if angle_to(self.car,
self.aerial.target_position) < 0.1 or norm(
self.car.velocity) < 1000:
if self.DELAY_TAKEOFF and ground_distance(
self.car, self.aerial.target_position) > 1000:
# extrapolate current state a small amount of time
future_car = Car(self.car)
time = 0.5
future_car.time += time
displacement = future_car.velocity * time if norm(future_car.velocity) > 500\
else normalize(future_car.velocity) * 500 * time
future_car.position += displacement
# simulate aerial fot the extrapolated car again
future_simulated_car = self.simulate_flight(
future_car, self.aerial)
# if the aerial is also successful, that means we should continue driving instead of taking off
# this makes sure that we go for the most late possible aerials, which are the most effective
if distance(future_simulated_car, self.aerial.
target_position) > self.MAX_DISTANCE_ERROR:
self.aerialing = True
else:
self.too_early = True
else:
self.aerialing = True
else:
# self.controls.boost = True
self.controls.throttle = 1
def calculate_old(car: Car,
ball: Ball,
target: vec3,
ball_predictions=None):
# Init vars
fake_car = Car(car)
b = Ball(ball)
# Generate predictions of ball path
if ball_predictions is None:
ball_predictions = [vec3(b.location)]
for i in range(60 * 5):
b.step(1.0 / 60.0)
ball_predictions.append(vec3(b.location))
# Gradually converge on ball location by aiming at a location, checking time to that location,
# and then aiming at the ball's NEW position. Guaranteed to converge (typically in <10 iterations)
# unless the ball is moving away from the car faster than the car's max boost speed
intercept = Intercept(b.location)
intercept.purpose = 'ball'
intercept.boost = True
intercept_ball_position = vec3(b.location)
i = 0
max_tries = 100
analyzer = BoostAnalysis() if intercept.boost else ThrottleAnalysis()
while i < max_tries:
# Find optimal spot to hit the ball
optimal_hit_vector = normalize(
target - intercept_ball_position) * b.collision_radius
optimal_hit_location = intercept_ball_position - optimal_hit_vector
# Find ideal rotation, unless it intersects with ground
optimal_rotation = look_at(
optimal_hit_vector, vec3(0, 0, 1)
) #axis_to_rotation(optimal_hit_vector) # this might be wrong
fake_car.rotation = optimal_rotation
# print(f'fake_car.location {fake_car.location}')
# print(f'get_car_front_center(fake_car) {get_car_front_center(fake_car)}')
fake_car.location += optimal_hit_location - get_car_front_center(
fake_car
) # try to position the car's front center directly on top of the best hit vector
euler = rotation_to_euler(optimal_rotation)
# todo put some super precise trigonometry in here to find the max angle allowed at given height
if fake_car.location[2] <= fake_car.hitbox().half_width[0]:
euler.pitch = 0
fake_car.rotation = euler_to_rotation(
vec3(euler.pitch, euler.yaw, euler.roll))
fake_car.location += optimal_hit_location - get_car_front_center(
fake_car
) # try to position the car's front center directly on top of the best hit vector
# Adjust vertical position if it (still) intersects with ground
if fake_car.location[2] < 17.0:
fake_car.location[2] = 17.0
intercept.location = get_car_front_center(fake_car)
# Calculate jump time needed
jump_height_time = JumpAnalysis().get_frame_by_height(
intercept.location[2]).time # or solve with motion equation
# car_euler = rotation_to_euler(car.rotation)
# jump_pitch_time = (euler.pitch - car_euler.pitch) / 5.5 + 0.35 # disregarding angular acceleration
# jump_yaw_time = (euler.yaw - car_euler.yaw) / 5.5 + 0.35 # disregarding angular acceleration
# jump_roll_time = (euler.roll - car_euler.roll) / 5.5 + 0.35 # disregarding angular acceleration
# jump_time = max(jump_height_time, jump_pitch_time, jump_yaw_time, jump_roll_time)
jump_time = jump_height_time # todo revisit rotation time
# print('jump_time', jump_time)
# Calculate distance to drive before jumping (to arrive perfectly on target)
total_translation = intercept.location - get_car_front_center(car)
total_translation[2] = 0
total_distance = norm(total_translation)
start_index = analyzer.get_index_by_speed(norm(car.velocity))
start_frame = analyzer.frames[start_index]
custom_error_func = lambda frame: abs(total_distance - (
frame.distance - start_frame.distance) - frame.speed *
jump_time)
drive_analysis = analyzer.get_frame_by_error(
custom_error_func, start_index)
arrival_time = drive_analysis.time - start_frame.time + jump_time
# print('drive_analysis.time', drive_analysis.time)
# print('drive_analysis', start_index)
# arrival_time = analyzer.travel_distance(total_distance, norm(car.velocity)).time
# drive_analysis = analyzer.travel_distance(norm(intercept.location - c.location), norm(c.velocity))
ball_index = int(round(arrival_time * 60))
if ball_index >= len(ball_predictions):
intercept.location = ball_predictions[-1]
intercept.time = len(ball_predictions) / 60.0
break
ball_location = ball_predictions[ball_index]
# print(f'Iteration {i} distance {norm(ball_location + vec3(optimal_hit_vector[0], optimal_hit_vector[1], 0) - intercept.location)}')
if norm(ball_location - intercept_ball_position) <= 1:
# if norm(intercept_ball_position - get_car_front_center(fake_car)) > 100:
# intercept.location = ball_predictions[-1]
# intercept.time = len(ball_predictions) / 60.0
# return intercept
intercept.dodge = True #jump_time > 0.2
intercept.jump_time = car.time + arrival_time - jump_time
intercept.dodge_preorientation = euler_to_rotation(
vec3(euler.pitch, euler.yaw, euler.roll))
intercept.dodge_delay = jump_time
intercept.dodge_direction = normalize(vec2(optimal_hit_vector))
# print(f'intercept_ball_position', intercept_ball_position)
# print(f'intercept.location', intercept.location)
# print(f'time until jump {drive_analysis.time}')
# print(f'time now {car.time}')
# print(f'distance until jump {drive_analysis.distance}')
# print(f'total distance to target {total_distance}')
# print(f'horiz speed @ jump {drive_analysis.speed}')
# print(f'time intended to be in air {jump_time}')
# print(f'distance travelled in air {jump_time * drive_analysis.speed}')
# print(f'distance remaining to target @ jump {total_distance - drive_analysis.distance}')
# print(f'Intercept convergence in {i} iterations')
# print(f'desired roll {euler.roll}')
# print(f'actual roll {rotation_to_euler(c.rotation).roll}')
break
intercept_ball_position = vec3(ball_location)
# intercept.location = vec3(ball_location)
# intercept.location[2] = 0
intercept.time = arrival_time
i += 1
if i >= max_tries:
print(
f'Warning: max tries ({max_tries}) exceeded for calculating intercept'
)
# Intercept is only meant for ground paths (and walls/cieling are only indirectly supported)
# collision_radius = c.hitbox().half_width[2] * 2 + b.collision_radius + b.collision_radius * 8
# on_ground = intercept.location[2] <= collision_radius
# on_back_wall = abs(intercept.location[1]) >= 5120 - collision_radius
# on_side_wall = abs(intercept.location[0]) >= 4096 - collision_radius
# # on_cieling = intercept.location[2] >= 2044 - collision_radius
# reachable = on_ground # or on_back_wall or on_side_wall # or on_cieling
# if not reachable:
# return None
return intercept
def calculate(car: Car, ball: Ball, target: vec3, ball_predictions=None):
# Init vars
b = Ball(ball)
dt = 1.0 / 60.0
# Generate predictions of ball path
if ball_predictions is None:
ball_predictions = []
for i in range(60 * 5):
b.step(dt)
ball_predictions.append(vec3(b.location))
# Gradually converge on ball location by aiming at a location, checking time to that location,
# and then aiming at the ball's NEW position. Guaranteed to converge (typically in <10 iterations)
# unless the ball is moving away from the car faster than the car's max boost speed
intercept = Intercept(b.location)
intercept.purpose = 'ball'
intercept.boost = True
intercept_ball_position = vec3(b.location)
collision_achieved = False
last_horizontal_error = None
last_horizontal_offset = None
i = 0
max_tries = 101
analyzer = BoostAnalysis() if intercept.boost else ThrottleAnalysis()
while i < max_tries:
i += 1
fake_car = Car(car)
direction = normalize(intercept.location - car.location)
fake_car.rotation = look_at(direction, fake_car.up())
for t in range(60 * 5):
# Step car location with throttle/boost analysis data
# Not super efficient but POITROAE
frame = analyzer.travel_time(dt, norm(fake_car.velocity))
# print('in 1 frame I travel', frame.time, frame.distance, frame.speed)
fake_car.location += direction * frame.distance
fake_car.velocity = direction * frame.speed
fake_car.time += dt
ball_location = ball_predictions[t]
# Check for collision
p = closest_point_on_obb(fake_car.hitbox(), ball_location)
if norm(p - ball_location) <= ball.collision_radius:
direction_vector = p - (fake_car.location - normalize(
fake_car.forward()) * 13.88) # octane center of mass
direction_vector[2] = 0
target_direction_vector = target - ball_location
target_direction_vector[2] = 0
intercept_ball_position = ball_location
direction = atan2(direction_vector[1], direction_vector[0])
ideal_direction = atan2(target_direction_vector[1],
target_direction_vector[0])
horizontal_error = direction - ideal_direction
# intercept.location = vec3(ball_location)
# intercept.time = fake_car.time
# return intercept
# Now descend the hit direction gradient
# Kick off the gradient descent with an arbitrary seed value
if last_horizontal_error is None:
last_horizontal_error = horizontal_error
last_horizontal_offset = 0
if horizontal_error > 0:
horizontal_offset = 25
else:
horizontal_offset = 25
intercept.location = ball_location - normalize(
fake_car.left()) * horizontal_offset
break
# Recursive case of gradient descent
if horizontal_offset == last_horizontal_offset:
gradient = 0
else:
gradient = (horizontal_error - last_horizontal_error
) / (horizontal_offset -
last_horizontal_offset)
if gradient == 0:
predicted_horizontal_offset = horizontal_offset
else:
predicted_horizontal_offset = horizontal_offset - horizontal_error / gradient
# Base case (convergence)
if abs(gradient) < 0.0005:
print(f'convergence in {i} iterations')
print(f'gradient = {gradient}')
print(
f'last_horizontal_offset = {last_horizontal_offset}'
)
print(f'direction = {degrees(direction)}')
print(f'ideal direction = {degrees(ideal_direction)}')
print(f'target = {target}')
print(f'ball_location = {ball_location}')
return intercept
# Edge case exit: offset maxed out
max_horizontal_offset = car.hitbox(
).half_width[1] + ball.collision_radius
if predicted_horizontal_offset > max_horizontal_offset:
predicted_horizontal_offset = max_horizontal_offset
elif predicted_horizontal_offset < -max_horizontal_offset:
predicted_horizontal_offset = -max_horizontal_offset
last_horizontal_offset = horizontal_offset
last_horizontal_error = horizontal_error
horizontal_offset = predicted_horizontal_offset
# Return the latest intercept location and continue descending the gradient
intercept.location = ball_location - normalize(
fake_car.left()) * predicted_horizontal_offset
print(f'iteration {i}')
print(f'gradient = {gradient}')
print(f'horizontal_offset = {horizontal_offset}')
print(f'horizontal_error = {degrees(horizontal_error)}')
# print(f'ideal direction = {degrees(ideal_direction)}')
break
# Check for arrival
if norm(fake_car.location -
intercept.location) < ball.collision_radius / 2:
intercept.location = ball_location
break
if i >= max_tries:
print(
f'Warning: max tries ({max_tries}) exceeded for calculating intercept'
)
return intercept
