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Formation Controller
====================
AI controller for maintaining the cross-formation of TABs.
Handles:
- Position control within cable constraints
- Aerodynamic force balancing
- Tangle prevention
- Coordinated formation maneuvers
"""
import numpy as np
from dataclasses import dataclass
from typing import Dict, List, Tuple, Optional
from enum import Enum
class FormationMode(Enum):
"""Formation operation modes"""
CRUISE = "cruise" # Standard cross-formation
DEFENSIVE = "defensive" # Expanded bubble for threat response
TIGHT = "tight" # Contracted for high-speed dash
SPIRAL = "spiral" # Orbiting for slingshot prep
DISPERSED = "dispersed" # Maximum separation
@dataclass
class FormationConfig:
"""Formation controller configuration"""
nominal_separation: float = 30.0 # m - standard cable length
defensive_expansion: float = 1.2 # Multiplier for defensive mode
tight_contraction: float = 0.7 # Multiplier for tight mode
correction_gain_p: float = 0.8 # Proportional gain
correction_gain_d: float = 0.2 # Derivative gain
max_correction_rate: float = 5.0 # m/s max position adjustment
class FormationController:
"""
AI controller for TAB formation maintenance.
Uses a PD control loop to maintain each TAB at its designated
position relative to the mother drone, using cable tension
and aerodynamic control surfaces.
"""
def __init__(self, config: Optional[FormationConfig] = None):
self.config = config or FormationConfig()
self.mode = FormationMode.CRUISE
# Error tracking for derivative control
self._prev_errors: Dict[str, np.ndarray] = {}
# Formation geometry (unit vectors from mother to TAB positions)
self._formation_vectors = {
"UP": np.array([0, 0, 1]), # Positive Z (up)
"DOWN": np.array([0, 0, -1]), # Negative Z (down)
"LEFT": np.array([0, -1, 0]), # Negative Y (left)
"RIGHT": np.array([0, 1, 0]), # Positive Y (right)
}
def get_target_positions(self,
mother_position: np.ndarray,
mother_velocity: np.ndarray) -> Dict[str, np.ndarray]:
"""
Calculate target positions for all TABs based on current mode.
Target positions trail behind the mother drone and offset
according to the cross-formation.
"""
# Base separation (affected by mode)
if self.mode == FormationMode.DEFENSIVE:
separation = self.config.nominal_separation * self.config.defensive_expansion
elif self.mode == FormationMode.TIGHT:
separation = self.config.nominal_separation * self.config.tight_contraction
else:
separation = self.config.nominal_separation
# Calculate trailing direction (opposite of velocity)
speed = np.linalg.norm(mother_velocity)
if speed > 1:
trail_direction = -mother_velocity / speed
else:
trail_direction = np.array([-1, 0, 0]) # Default: behind in X
# Base trailing position
trail_distance = separation * 0.8 # TABs trail 80% of cable length
base_trail = mother_position + trail_direction * trail_distance
targets = {}
for tab_id, offset_vector in self._formation_vectors.items():
# Cross offset perpendicular to flight direction
lateral_offset = offset_vector * (separation * 0.3) # 30% lateral spread
targets[tab_id] = base_trail + lateral_offset
return targets
def compute_control_commands(self,
mother_position: np.ndarray,
mother_velocity: np.ndarray,
tab_positions: Dict[str, np.ndarray],
tab_velocities: Dict[str, np.ndarray],
dt: float) -> Dict[str, Dict]:
"""
Calculate control commands for all TABs to maintain formation.
Returns elevator and rudder commands for each TAB.
"""
targets = self.get_target_positions(mother_position, mother_velocity)
commands = {}
for tab_id in tab_positions:
if tab_id not in targets:
continue
target = targets[tab_id]
current = tab_positions[tab_id]
velocity = tab_velocities.get(tab_id, np.zeros(3))
# Position error
error = target - current
# Derivative of error
prev_error = self._prev_errors.get(tab_id, error)
error_dot = (error - prev_error) / dt if dt > 0 else np.zeros(3)
self._prev_errors[tab_id] = error.copy()
# PD control
correction = (self.config.correction_gain_p * error +
self.config.correction_gain_d * error_dot)
# Limit correction rate
correction_mag = np.linalg.norm(correction)
if correction_mag > self.config.max_correction_rate:
correction = correction * (self.config.max_correction_rate / correction_mag)
# Convert to control surface commands
# Vertical correction -> elevator
# Lateral correction -> rudder
elevator_cmd = np.clip(correction[2] * 0.1, -0.4, 0.4) # radians
rudder_cmd = np.clip(correction[1] * 0.1, -0.35, 0.35)
commands[tab_id] = {
'elevator': elevator_cmd,
'rudder': rudder_cmd,
'aileron': 0.0,
'error_magnitude': np.linalg.norm(error),
'target_position': target,
'correction_vector': correction
}
return commands
def set_mode(self, mode: FormationMode):
"""Change formation mode"""
self.mode = mode
def check_formation_integrity(self,
tab_positions: Dict[str, np.ndarray],
cable_length: float) -> Dict:
"""
Check if formation is within acceptable bounds.
Returns warnings for any TABs approaching limits.
"""
warnings = []
status = "OK"
for tab_id, pos in tab_positions.items():
# Would need mother position for full check
# Simplified: check relative positions
pass
# Check for tangle risk (TABs too close to each other)
tab_ids = list(tab_positions.keys())
for i, id1 in enumerate(tab_ids):
for id2 in tab_ids[i+1:]:
distance = np.linalg.norm(
tab_positions[id1] - tab_positions[id2]
)
if distance < cable_length * 0.2:
warnings.append({
'type': 'tangle_risk',
'tabs': [id1, id2],
'distance': distance
})
status = "WARNING"
return {
'status': status,
'warnings': warnings,
'mode': self.mode.value
}
def prepare_spiral(self,
spiral_direction: str = "clockwise",
rate_dps: float = 45.0) -> Dict:
"""
Prepare formation for spiral maneuver (slingshot wind-up).
TABs will naturally orbit due to centripetal force.
"""
self.mode = FormationMode.SPIRAL
return {
'mode': 'spiral',
'direction': spiral_direction,
'rate': rate_dps,
'expected_orbital_velocity': rate_dps * self.config.nominal_separation * np.pi / 180
}
def get_formation_status(self,
mother_position: np.ndarray,
tab_positions: Dict[str, np.ndarray]) -> Dict:
"""Get detailed formation status"""
targets = self.get_target_positions(mother_position, np.array([50, 0, 0]))
tab_status = {}
for tab_id, pos in tab_positions.items():
if tab_id in targets:
error = np.linalg.norm(pos - targets[tab_id])
tab_status[tab_id] = {
'position': pos.tolist(),
'target': targets[tab_id].tolist(),
'error': error,
'on_station': error < 5.0 # Within 5m is "on station"
}
return {
'mode': self.mode.value,
'tabs': tab_status,
'formation_intact': all(t['on_station'] for t in tab_status.values())
}
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