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Dribble/world/commons/Path_Manager.py

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from cpp.a_star import a_star
from math_ops.Math_Ops import Math_Ops as M
from world.World import World
import math
import numpy as np
class Path_Manager():
MODE_CAUTIOUS = 0
MODE_DRIBBLE = 1 # safety margins are increased
MODE_AGGRESSIVE = 2 # safety margins are reduced for opponents
STATUS_SUCCESS = 0 # the pathfinding algorithm was executed normally
STATUS_TIMEOUT = 1 # timeout before the target was reached (may be impossible)
STATUS_IMPOSSIBLE = 2 # impossible to reach target (all options were tested)
STATUS_DIRECT = 3 # no obstacles between start and target (path contains only 2 points: the start and target)
HOT_START_DIST_WALK = 0.05 # hot start prediction distance (when walking)
HOT_START_DIST_DRIBBLE = 0.10 # hot start prediction distance (when dribbling)
def __init__(self, world : World) -> None:
self.world = world
self._draw_obstacles = False # enabled by function 'draw_options'
self._draw_path = False # enabled by function 'draw_options'
self._use_team_channel = False # enabled by function 'draw_options'
# internal variables to bootstrap the path to start from a prediction (to reduce path instability)
self.last_direction_rad = None
self.last_update = 0
self.last_start_dist = None
def draw_options(self, enable_obstacles, enable_path, use_team_drawing_channel=False):
'''
Enable or disable drawings, and change drawing channel
If self.world.draw.enable is False, these options are ignored
Parameters
----------
enable_obstacles : bool
draw relevant obstacles for path planning
enable_path : bool
draw computed path
use_team_drawing_channel : bool
True to use team drawing channel, otherwise use individual channel
Using individual channels for each player means that drawings with the same name can coexist
With the team channel, drawings with the same name will replace previous drawings, even if drawn by a teammate
'''
self._draw_obstacles = enable_obstacles
self._draw_path = enable_path
self._use_team_channel = use_team_drawing_channel
def get_obstacles(self, include_teammates, include_opponents, include_play_mode_restrictions, max_distance = 4, max_age = 500,
ball_safety_margin = 0, goalpost_safety_margin = 0, mode = MODE_CAUTIOUS, priority_unums=[]):
'''
Parameters
----------
include_teammates : bool
include teammates in the returned list of obstacles
include_opponents : bool
include opponents in the returned list of obstacles
max_distance : float
teammates or opponents are only considered if they are closer than `max_distance` (meters)
max_age : float
teammates or opponents are only considered if they were seen in the last `max_age` (milliseconds)
ball_safety_margin : float
minimum value for the ball's soft repulsion radius
this value is increased when the game is stopped, and when the ball is almost out of bounds
default is zero, the ball is ignored
goalpost_safety_margin : float
hard repulsion radius around the opponents' goalposts
default is zero, uses the minimum margin
mode : int
overall attitude towards safety margins (concerns teammates and opponents)
priority_unums : list
list of teammates to avoid (since their role is more important)
Returns
-------
obstacles : list
list of obstacles, where each obstacle is a tuple of 5 floats (x, y, hard radius, soft radius, repulsive force)
'''
w = self.world
ball_2d = w.ball_abs_pos[:2]
obstacles = []
# 'comparator' is a variable local to the lambda, which captures the current value of (w.time_local_ms - max_age)
check_age = lambda last_update, comparator = w.time_local_ms - max_age : last_update > 0 and last_update >= comparator
#---------------------------------------------- Get recently seen close teammates
if include_teammates:
soft_radius = 1.1 if mode == Path_Manager.MODE_DRIBBLE else 0.6 # soft radius: repulsive force is max at center and fades
def get_hard_radius(t):
if t.unum in priority_unums:
return 1.0 # extra distance for priority roles
else:
return t.state_ground_area[1]+0.2
# Get close teammates (center, hard radius, soft radius, force)
obstacles.extend( (*t.state_ground_area[0],
get_hard_radius(t),
1.5 if t.unum in priority_unums else soft_radius,
1.0) # repulsive force
for t in w.teammates if not t.is_self and check_age(t.state_last_update) and t.state_horizontal_dist < max_distance)
#---------------------------------------------- Get recently seen close opponents
if include_opponents:
# soft radius: repulsive force is max at center and fades
if mode == Path_Manager.MODE_AGGRESSIVE:
soft_radius = 0.6
hard_radius = lambda o : 0.2
elif mode == Path_Manager.MODE_DRIBBLE:
soft_radius = 2.3
hard_radius = lambda o : o.state_ground_area[1]+0.9
else:
soft_radius = 1.0
hard_radius = lambda o : o.state_ground_area[1]+0.2
# Get close opponents (center, hard radius, soft radius, force)
obstacles.extend( (*o.state_ground_area[0],
hard_radius(o),
soft_radius,
1.5 if o.unum == 1 else 1.0) # repulsive force (extra for their GK)
for o in w.opponents if o.state_last_update > 0 and w.time_local_ms - o.state_last_update <= max_age and o.state_horizontal_dist < max_distance)
#---------------------------------------------- Get play mode restrictions
if include_play_mode_restrictions:
if w.play_mode == World.M_THEIR_GOAL_KICK:
obstacles.extend((15,i,2.1,0,0) for i in range(-2,3)) # 5 circular obstacles to cover their goal area
elif w.play_mode == World.M_THEIR_PASS:
obstacles.append((*ball_2d, 1.2, 0, 0))
elif w.play_mode in [World.M_THEIR_KICK_IN,World.M_THEIR_CORNER_KICK,World.M_THEIR_FREE_KICK,World.M_THEIR_DIR_FREE_KICK, World.M_THEIR_OFFSIDE]:
obstacles.append((*ball_2d, 2.5, 0, 0))
#---------------------------------------------- Get ball
if ball_safety_margin > 0:
# increase ball safety margin in certain game scenarios
if (w.play_mode_group != w.MG_OTHER) or abs(ball_2d[1])>9.5 or abs(ball_2d[0])>14.5:
ball_safety_margin += 0.12
obstacles.append((*ball_2d, 0, ball_safety_margin, 8))
#---------------------------------------------- Get goal posts
if goalpost_safety_margin > 0:
obstacles.append((14.75, 1.10,goalpost_safety_margin,0,0))
obstacles.append((14.75,-1.10,goalpost_safety_margin,0,0))
#---------------------------------------------- Draw obstacles
if self._draw_obstacles:
d = w.team_draw if self._use_team_channel else w.draw
if d.enabled:
for o in obstacles:
if o[3] > 0: d.circle(o[:2],o[3],o[4]/2, d.Color.orange, "path_obstacles", False)
if o[2] > 0: d.circle(o[:2],o[2],1, d.Color.red, "path_obstacles", False)
d.flush("path_obstacles")
return obstacles
def _get_hot_start(self, start_distance):
'''
Get hot start position for path (considering the previous path)
(as opposed to a cold start, where the path starts at the player)
'''
if self.last_update > 0 and self.world.time_local_ms - self.last_update == 20 and self.last_start_dist == start_distance:
return self.world.robot.loc_head_position[:2] + M.vector_from_angle(self.last_direction_rad, is_rad = True) * start_distance
else:
return self.world.robot.loc_head_position[:2] # return cold start if start_distance was different or the position was not updated in the last step
def _update_hot_start(self, next_dir_rad, start_distance):
''' Update hot start position for next run '''
self.last_direction_rad = next_dir_rad
self.last_update = self.world.time_local_ms
self.last_start_dist = start_distance
def _extract_target_from_path(self, path, path_len, ret_segments):
ret_seg_ceil = math.ceil(ret_segments)
if path_len >= ret_seg_ceil:
i = ret_seg_ceil * 2 # path index of ceil point (x)
if ret_seg_ceil == ret_segments:
return path[i:i+2]
else:
floor_w = ret_seg_ceil-ret_segments
return path[i-2:i] * floor_w + path[i:i+2] * (1-floor_w)
else:
return path[-2:] # path end
def get_path_to_ball(self, x_ori = None, x_dev = -0.2, y_dev = 0, torso_ori = None, torso_ori_thrsh = 1,
priority_unums:list=[], is_aggressive=True, safety_margin = 0.25, timeout = 3000):
'''
Get next target from path to ball (next absolute position + next absolute orientation)
If the robot is an active player, and close to the ball, it makes sense to be aggressive
If the robot is far, it should follow the role_position instead to predict the intersection with ball
Parameters
----------
x_ori : float
(This variable allows the specification of a target position, relative to the ball, in a custom reference frame.)
absolute orientation of the custom reference frame's x-axis
if None, the orientation is given by the vector (robot->ball)
x_dev : float
(This variable allows the specification of a target position, relative to the ball, in a custom reference frame.)
target position deviation, in the custom reference frame's x-axis
y_dev : float
(This variable allows the specification of a target position, relative to the ball, in a custom reference frame.)
target position deviation, in the custom reference frame's y-axis
torso_ori : float
torso's target absolute orientation (see `torso_ori_thrsh`)
if None, the orientation is given by the vector (robot->target)
torso_ori_thrsh : float
`torso_ori` will only be applied when the distance between robot and final target is < `torso_ori_thrsh` meters
otherwise, the robot will orient itself towards the final target
priority_unums : list
list of teammates to avoid (since their role is more important)
is_aggressive : bool
if True, safety margins are reduced for opponents
safety_margin : float
repulsion radius around ball to avoid colliding with it
timeout : float
maximum execution time (in microseconds)
Returns
-------
next_pos : ndarray
next absolute position from path to ball
next_ori : float
next absolute orientation
distance : float
minimum between (distance to final target) and (distance to ball)
Example
-------
----------------------------------------------------------------------------------------------
x_ori | x_dev | y_dev | torso_ori | OBS
-------------+---------+---------+-------------+----------------------------------------------
None => | - | !0 | - | Not recommended. Will not converge.
(orient. of: | 0 | 0 | None | Frontal ball chase, expected* slow approach
robot->ball) | 0 | 0 | value | Oriented ball chase, expected* slow approach
| >0 | 0 | - | Not recommended. Will not converge.
| <0 | 0 | None | Frontal ball chase until distance == x_dev
| <0 | 0 | value | Oriented ball chase until distance == x_dev
-------------+---------+---------+-------------+----------------------------------------------
value | - | - | None | Frontal point chase
| - | - | value | Oriented point chase
----------------------------------------------------------------------------------------------
* it depends on the caller function (expected slow walking near target)
`torso_ori` will only be applied when the distance between robot and final target is < `torso_ori_thrsh` meters
'''
w = self.world
r = w.robot
dev = np.array([x_dev,y_dev])
dev_len = np.linalg.norm(dev)
dev_mult = 1
# use ball prediction if we are further than 0.5 m and in PlayOn
if np.linalg.norm(w.ball_abs_pos[:2] - r.loc_head_position[:2]) > 0.5 and w.play_mode_group == w.MG_OTHER:
ball_2d = w.get_intersection_point_with_ball(0.4)[0] # intersection point, while moving at 0.4 m/s
else:
ball_2d = w.ball_abs_pos[:2]
# custom reference frame orientation
vec_me_ball = ball_2d - r.loc_head_position[:2]
if x_ori is None:
x_ori = M.vector_angle(vec_me_ball)
distance_boost = 0 # boost for returned distance to target
if torso_ori is not None and dev_len > 0:
approach_ori_diff = abs(M.normalize_deg( r.imu_torso_orientation - torso_ori ))
if approach_ori_diff > 15: # increase walking speed near target if robot is far from approach orientation
distance_boost = 0.15
if approach_ori_diff > 30: # increase target distance to ball if robot is far from approach orientation
dev_mult = 1.3
if approach_ori_diff > 45: # increase safety margin around ball if robot is far from approach orientation
safety_margin = max(0.32,safety_margin)
#------------------------------------------- get target
front_unit_vec = M.vector_from_angle(x_ori)
left_unit_vec = np.array([-front_unit_vec[1], front_unit_vec[0]])
rel_target = front_unit_vec * dev[0] + left_unit_vec * dev[1]
target = ball_2d + rel_target * dev_mult
target_vec = target - r.loc_head_position[:2]
target_dist = np.linalg.norm(target_vec)
if self._draw_path:
d = self.world.team_draw if self._use_team_channel else self.world.draw
d.point(target, 4, d.Color.red, "path_target") # will not draw if drawing object is internally disabled
#------------------------------------------- get obstacles
# Ignore ball if we are on the same side of the target (with small margin)
if dev_len>0 and np.dot(vec_me_ball, rel_target) < -0.10:
safety_margin = 0
obstacles = self.get_obstacles(include_teammates = True, include_opponents = True, include_play_mode_restrictions = True,
ball_safety_margin = safety_margin,
mode = Path_Manager.MODE_AGGRESSIVE if is_aggressive else Path_Manager.MODE_CAUTIOUS,
priority_unums = priority_unums)
# Add obstacle on the side opposite to the target
if dev_len>0 and safety_margin > 0:
center = ball_2d - M.normalize_vec( rel_target ) * safety_margin
obstacles.append((*center, 0, safety_margin*0.9, 5))
if self._draw_obstacles:
d = w.team_draw if self._use_team_channel else w.draw
if d.enabled:
d.circle(center,safety_margin*0.8,2.5, d.Color.orange, "path_obstacles_1")
#------------------------------------------- get path
# see explanation for the context at the hot start update section below
start_pos = self._get_hot_start(Path_Manager.HOT_START_DIST_WALK) if target_dist > 0.4 else self.world.robot.loc_head_position[:2]
path, path_len, path_status, path_cost = self.get_path(start_pos, True, obstacles, target, timeout)
path_end = path[-2:] # last position allowed by A*
#------------------------------------------- get relevant distances
if w.ball_last_seen > w.time_local_ms - w.VISUALSTEP_MS: # ball is in FOV
raw_ball_dist = np.linalg.norm(w.ball_rel_torso_cart_pos[:2]) # - distance between torso center and ball center
else: # otherwise use absolute coordinates to compute distance
raw_ball_dist = np.linalg.norm(vec_me_ball) # - distance between head center and ball center
avoid_touching_ball = (w.play_mode_group != w.MG_OTHER)
distance_to_final_target = np.linalg.norm(path_end - r.loc_head_position[:2])
distance_to_ball = max(0.07 if avoid_touching_ball else 0.14, raw_ball_dist - 0.13)
caution_dist = min(distance_to_ball,distance_to_final_target)
#------------------------------------------- get next target position
next_pos = self._extract_target_from_path( path, path_len, ret_segments=1 if caution_dist < 1 else 2 )
#------------------------------------------ get next target orientation
# use given orientation if it exists, else target's orientation if far enough, else current orientation
if torso_ori is not None:
if caution_dist > torso_ori_thrsh:
next_ori = M.vector_angle(target_vec)
else:
mid_ori = M.normalize_deg( M.vector_angle(vec_me_ball) - M.vector_angle(-dev) - x_ori + torso_ori )
mid_ori_diff = abs(M.normalize_deg(mid_ori - r.imu_torso_orientation))
final_ori_diff = abs(M.normalize_deg(torso_ori - r.imu_torso_orientation))
next_ori = mid_ori if mid_ori_diff + 10 < final_ori_diff else torso_ori
elif target_dist > 0.1:
next_ori = M.vector_angle(target_vec)
else:
next_ori = r.imu_torso_orientation
#------------------------------------------ update hot start for next run
''' Defining the hot start distance:
- if path_len is zero, there is no hot start, because we are already there (dist=0)
- if the target is close, the hot start is not applied (see above)
- if the next pos is very close (due to hard obstacle), the hot start is the next pos (dist<Path_Manager.HOT_START_DIST_WALK)
- otherwise, the hot start distance is constant (dist=Path_Manager.HOT_START_DIST_WALK)
'''
if path_len != 0:
next_pos_vec = next_pos - self.world.robot.loc_head_position[:2]
next_pos_dist = np.linalg.norm(next_pos_vec)
self._update_hot_start(M.vector_angle(next_pos_vec, is_rad=True), min(Path_Manager.HOT_START_DIST_WALK,next_pos_dist))
return next_pos, next_ori, min(distance_to_ball, distance_to_final_target + distance_boost)
def get_path_to_target(self, target, ret_segments = 1.0, torso_ori = None, priority_unums:list=[], is_aggressive=True, timeout = 3000):
'''
Get next position from path to target (next absolute position + next absolute orientation)
Parameters
----------
ret_segments : float
returned target's maximum distance (measured in path segments from hot start position)
actual distance: min(ret_segments,path_length)
each path segment has 0.10 m or 0.1*sqrt(2) m (if diagonal)
if `ret_segments` is 0, the current position is returned
torso_ori : float
torso's target absolute orientation
if None, the orientation is given by the vector (robot->target)
priority_unums : list
list of teammates to avoid (since their role is more important)
is_aggressive : bool
if True, safety margins are reduced for opponents
timeout : float
maximum execution time (in microseconds)
'''
w = self.world
#------------------------------------------- get target
target_vec = target - w.robot.loc_head_position[:2]
target_dist = np.linalg.norm(target_vec)
#------------------------------------------- get obstacles
obstacles = self.get_obstacles(include_teammates = True, include_opponents = True, include_play_mode_restrictions = True,
mode = Path_Manager.MODE_AGGRESSIVE if is_aggressive else Path_Manager.MODE_CAUTIOUS, priority_unums = priority_unums)
#------------------------------------------- get path
# see explanation for the context at the hot start update section below
start_pos = self._get_hot_start(Path_Manager.HOT_START_DIST_WALK) if target_dist > 0.4 else self.world.robot.loc_head_position[:2]
path, path_len, path_status, path_cost = self.get_path(start_pos, True, obstacles, target, timeout)
path_end = path[-2:] # last position allowed by A*
#------------------------------------------- get next target position
next_pos = self._extract_target_from_path(path, path_len, ret_segments)
#------------------------------------------ get next target orientation
# use given orientation if it exists, else target's orientation if far enough, else current orientation
if torso_ori is not None:
next_ori = torso_ori
elif target_dist > 0.1:
next_ori = M.vector_angle(target_vec)
else:
next_ori = w.robot.imu_torso_orientation
#------------------------------------------ update hot start for next run
''' Defining the hot start distance:
- if path_len is zero, there is no hot start, because we are already there (dist=0)
- if the target is close, the hot start is not applied (see above)
- if the next pos is very close (due to hard obstacle), the hot start is the next pos (dist<Path_Manager.HOT_START_DIST_WALK)
- otherwise, the hot start distance is constant (dist=Path_Manager.HOT_START_DIST_WALK)
'''
if path_len != 0:
next_pos_vec = next_pos - self.world.robot.loc_head_position[:2]
next_pos_dist = np.linalg.norm(next_pos_vec)
self._update_hot_start(M.vector_angle(next_pos_vec, is_rad=True), min(Path_Manager.HOT_START_DIST_WALK,next_pos_dist))
distance_to_final_target = np.linalg.norm(path_end - w.robot.loc_head_position[:2])
return next_pos, next_ori, distance_to_final_target
def get_dribble_path(self, ret_segments = None, optional_2d_target = None, goalpost_safety_margin=0.4, timeout = 3000):
'''
Get next position from path to target (next relative orientation)
Path is optimized for dribble
Parameters
----------
ret_segments : float
returned target's maximum distance (measured in path segments from hot start position)
actual distance: min(ret_segments,path_length)
each path segment has 0.10 m or 0.1*sqrt(2) m (if diagonal)
if `ret_segments` is 0, the current position is returned
if `ret_segments` is None, it is dynamically set according to the robot's speed
optional_2d_target : float
2D target
if None, the target is the opponent's goal (the specific goal point is decided by the A* algorithm)
goalpost_safety_margin : float
hard repulsion radius around the opponents' goalposts
if zero, the minimum margin is used
timeout : float
maximum execution time (in microseconds)
'''
r = self.world.robot
ball_2d = self.world.ball_abs_pos[:2]
#------------------------------------------- get obstacles
obstacles = self.get_obstacles(include_teammates = True, include_opponents = True, include_play_mode_restrictions = False,
max_distance=5, max_age=1000, goalpost_safety_margin=goalpost_safety_margin, mode = Path_Manager.MODE_DRIBBLE)
#------------------------------------------- get path
start_pos = self._get_hot_start(Path_Manager.HOT_START_DIST_DRIBBLE)
path, path_len, path_status, path_cost = self.get_path(start_pos, False, obstacles, optional_2d_target, timeout)
#------------------------------------------- get next target position & orientation
if ret_segments is None:
ret_segments = 2.0
next_pos = self._extract_target_from_path(path, path_len, ret_segments)
next_rel_ori = M.normalize_deg(M.vector_angle(next_pos - ball_2d) - r.imu_torso_orientation)
#------------------------------------------ update hot start for next run
if path_len != 0:
self._update_hot_start(M.deg_to_rad(r.imu_torso_orientation), Path_Manager.HOT_START_DIST_DRIBBLE)
#------------------------------------------ draw
if self._draw_path and path_status != Path_Manager.STATUS_DIRECT:
d = self.world.team_draw if self._use_team_channel else self.world.draw
d.point(next_pos, 2, d.Color.pink, "path_next_pos",False) # will not draw if drawing object is internally disabled
d.line(ball_2d, next_pos, 2, d.Color.pink, "path_next_pos") # will not draw if drawing object is internally disabled
return next_pos, next_rel_ori
def get_push_path(self, ret_segments = 1.5, optional_2d_target = None, avoid_opponents=False, timeout = 3000):
'''
Get next position from path ball to target (next absolute position)
Path is optimized for critical push (no obstacles, also for preparation stability)
Parameters
----------
ret_segments : float
returned target's maximum distance (measured in path segments from hot start position)
actual distance: min(ret_segments,path_length)
each path segment has 0.10 m or 0.1*sqrt(2) m (if diagonal)
if `ret_segments` is 0, the current position is returned
optional_2d_target : float
2D target
if None, the target is the opponent's goal (the specific goal point is decided by the A* algorithm)
timeout : float
maximum execution time (in microseconds)
'''
ball_2d = self.world.ball_abs_pos[:2]
#------------------------------------------- get obstacles
obstacles = self.get_obstacles(include_teammates = False, include_opponents = avoid_opponents, include_play_mode_restrictions = False)
#------------------------------------------- get path
path, path_len, path_status, path_cost = self.get_path(ball_2d, False, obstacles, optional_2d_target, timeout)
#------------------------------------------- get next target position & orientation
next_pos = self._extract_target_from_path(path, path_len, ret_segments)
return next_pos
def get_path(self, start, allow_out_of_bounds, obstacles=[], optional_2d_target = None, timeout = 3000):
'''
Parameters
----------
allow_out_of_bounds : bool
allow path to go out of bounds, should be False when dribbling
obstacles : list
list of obstacles, where each obstacle is a tuple of 5 floats (x, y, hard radius, soft radius, repulsive force)
optional_2d_target : float
2D target
if None, the target is the opponent's goal (the specific goal point is decided by the A* algorithm)
timeout : float
maximum execution time (in microseconds)
'''
go_to_goal = int(optional_2d_target is None)
if optional_2d_target is None:
optional_2d_target = (0,0)
# flatten obstacles
obstacles = sum(obstacles, tuple())
assert len(obstacles) % 5 == 0, "Each obstacle should be characterized by exactly 5 float values"
# Path parameters: start, allow_out_of_bounds, go_to_goal, optional_target, timeout (us), obstacles
params = np.array([*start, int(allow_out_of_bounds), go_to_goal, *optional_2d_target, timeout, *obstacles], np.float32)
path_ret = a_star.compute(params)
path = path_ret[:-2]
path_status = path_ret[-2]
#---------------------------------------------- Draw path segments
if self._draw_path:
d = self.world.team_draw if self._use_team_channel else self.world.draw
if d.enabled:
c = {0: d.Color.green_lawn, 1: d.Color.yellow, 2: d.Color.red, 3: d.Color.cyan}[path_status]
for j in range(0, len(path)-2, 2):
d.line((path[j],path[j+1]),(path[j+2],path[j+3]), 1, c, "path_segments", False)
d.flush("path_segments")
return path, len(path)//2-1, path_status, path_ret[-1] # path, path_len (number of segments), path_status, path_cost (A* cost)
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