Fault Environment Example
This tutorial demonstrates how to configure and use a simple BSK-RL environment to model faults in a system with four reaction wheels (RWs).
Load Modules
[1]:
import numpy as np
from typing import Iterable
from Basilisk.architecture import bskLogging
from Basilisk.utilities import macros, orbitalMotion, simIncludeRW
from Basilisk.simulation import reactionWheelStateEffector
from Basilisk.fswAlgorithms import rwNullSpace
from Basilisk.architecture import messaging
from bsk_rl import SatelliteTasking, act, data, obs, scene, sats
from bsk_rl.sim import dyn, fsw, world
from bsk_rl.utils.orbital import random_orbit, random_unit_vector
from bsk_rl.utils.functional import default_args
bskLogging.setDefaultLogLevel(bskLogging.BSK_WARNING)
Making Faults Cases
Creating a fault base class and defining individual fault types enables modeling multiple kinds of faults within a single satellite. In this example, a power draw limit is applied to RWs, causing it to operate at reduced speed compared to nominal conditions. By default, while a torque limit is enforced, there are no restrictions on power draw. time is used to define the time at which the fault occurs, reducedLimit specifies the power draw limit in watts, and wheel_Idx indicates which
RW is affected by the fault. It can be set to a value from 1 to 4, or to all to apply the fault to every RW.
[2]:
class FaultObject:
def __init__(self, name, time, verbose=True, **kwargs):
self.name = name
self.time = time
self.verbose = verbose
self.message = None
self.message_printed = False
def execute(self, satellite):
raise NotImplementedError(
f"{self.name} does not have a custom execute function!"
)
def print_message(self, message, satellite):
if not self.message_printed:
satellite.logger.info(message)
self.message_printed = True
def addFaultToSimulation(self, satellite, listIdx):
self.uniqueFaultIdx = listIdx # Index in the faultList array.
satellite.simulator.createNewEvent(
f"add{self.name}Fault",
satellite.dynamics.dyn_rate,
eventActive=True,
conditionTime=self.time,
actionList=[
f"self.faultList[{self.uniqueFaultIdx}].execute({satellite._satellite_command})",
f"self.faultList[{self.uniqueFaultIdx}].print({satellite._satellite_command})",
],
)
class RwPowerFault(FaultObject):
def __init__(self, name, time, reducedLimit, wheelIdx):
super().__init__(name, time)
self.reducedLimit = reducedLimit
if isinstance(wheelIdx, float):
# int needed around wheelIdx because np.random.choice doesn't return
# a type int, and the index will not register in execute without it.
self.wheelIdx = int(wheelIdx)
elif isinstance(wheelIdx, int) or wheelIdx == "all":
# option to trigger the fault in all wheels reflecting a larger power issue
self.wheelIdx = wheelIdx
else:
raise ValueError(
"Fault parameter 'wheelIdx' must either be a number corresponding to a reaction wheel or the string 'all'"
)
def execute(self, satellite):
dynModels = satellite.dynamics
if self.wheelIdx == 1:
dynModels.rwFactory.rwList["RW1"].P_max = self.reducedLimit
elif self.wheelIdx == 2:
dynModels.rwFactory.rwList["RW2"].P_max = self.reducedLimit
elif self.wheelIdx == 3:
dynModels.rwFactory.rwList["RW3"].P_max = self.reducedLimit
elif self.wheelIdx == 4:
dynModels.rwFactory.rwList["RW4"].P_max = self.reducedLimit
elif self.wheelIdx == "all":
# option to trigger the fault in all wheels (not supported for all fault types)
dynModels.rwFactory.rwList["RW1"].P_max = self.reducedLimit
dynModels.rwFactory.rwList["RW2"].P_max = self.reducedLimit
dynModels.rwFactory.rwList["RW3"].P_max = self.reducedLimit
dynModels.rwFactory.rwList["RW4"].P_max = self.reducedLimit
def print(self, satellite):
if self.wheelIdx == "all":
self.message = f"RW Power Fault: all RW's power limit reduced to {self.reducedLimit} Watts at {self.time * macros.NANO2MIN} minutes!"
else:
self.message = f"RW Power Fault: RW{self.wheelIdx}'s power limit reduced to {self.reducedLimit} Watts at {self.time * macros.NANO2MIN} minutes!"
super().print_message(self.message, satellite)
Configure the Simulation Models
Dynamics model:
FullFeaturedDynModelis used as the base class, andsetup_reaction_wheel_dyn_effectoris overridden to support four RWs. Two additional properties are added: the angle between the Sun and the solar panel, and the speed fraction of each RW.
[3]:
class CustomDynModel(dyn.FullFeaturedDynModel):
@property
def solar_angle_norm(self) -> float:
sun_vec_N = (
self.world.gravFactory.spiceObject.planetStateOutMsgs[self.world.sun_index]
.read()
.PositionVector
)
sun_vec_N_hat = sun_vec_N / np.linalg.norm(sun_vec_N)
solar_panel_vec_B = np.array([0, 0, -1])
mat = np.transpose(self.BN)
solar_panel_vec_N = np.matmul(mat, solar_panel_vec_B)
error_angle = np.arccos(np.dot(solar_panel_vec_N, sun_vec_N_hat))
return error_angle / np.pi
@property
def wheel_speeds_frac(self):
rw_speed = self.wheel_speeds
return rw_speed[0:4] / (self.maxWheelSpeed * macros.rpm2radsec)
@default_args(
wheelSpeeds=lambda: np.random.uniform(-1500, 1500, 4),
maxWheelSpeed=np.inf,
u_max=0.200,
)
def setup_reaction_wheel_dyn_effector(
self,
wheelSpeeds: Iterable[float],
maxWheelSpeed: float,
u_max: float,
priority: int = 997,
**kwargs,
) -> None:
"""Set the RW state effector parameters.
Args:
wheelSpeeds: Initial speeds of each wheel [RPM]
maxWheelSpeed: Failure speed for wheels [RPM]
u_max: Torque producible by wheel [N*m]
priority: Model priority.
kwargs: Ignored
"""
def balancedHR16Triad(
useRandom=False, randomBounds=(-400, 400), wheelSpeeds=[500, 500, 500, 500]
):
"""Create a set of three HR16 reaction wheels.
Args:
useRandom: Use random values for wheel speeds.
randomBounds: Bounds for random wheel speeds.
wheelSpeeds: Fixed wheel speeds.
Returns:
tuple:
* **rwStateEffector**: Reaction wheel state effector instance.
* **rwFactory**: Factory containing defined reaction wheels.
* **wheelSpeeds**: Wheel speeds.
"""
rwFactory = simIncludeRW.rwFactory()
if useRandom:
wheelSpeeds = np.random.uniform(randomBounds[0], randomBounds[1], 4)
c = 3 ** (-0.5)
rwFactory.create(
"Honeywell_HR16",
[1, 0, 0],
maxMomentum=50.0,
Omega=wheelSpeeds[0],
)
rwFactory.create(
"Honeywell_HR16",
[0, 1, 0],
maxMomentum=50.0,
Omega=wheelSpeeds[1],
)
rwFactory.create(
"Honeywell_HR16",
[0, 0, 1],
maxMomentum=50.0,
Omega=wheelSpeeds[2],
)
rwFactory.create(
"Honeywell_HR16",
[c, c, c],
maxMomentum=50.0,
Omega=wheelSpeeds[3],
)
rwStateEffector = reactionWheelStateEffector.ReactionWheelStateEffector()
return rwStateEffector, rwFactory, wheelSpeeds
self.maxWheelSpeed = maxWheelSpeed
self.rwStateEffector, self.rwFactory, _ = balancedHR16Triad(
useRandom=False,
wheelSpeeds=wheelSpeeds,
)
for RW in self.rwFactory.rwList.values():
RW.u_max = u_max
self.rwStateEffector.ModelTag = "ReactionWheels"
self.rwFactory.addToSpacecraft(
self.scObject.ModelTag, self.rwStateEffector, self.scObject
)
self.simulator.AddModelToTask(
self.task_name, self.rwStateEffector, ModelPriority=priority
)
self.Gs = np.array(
[
[1, 0, 0, 1 / np.sqrt(3)], # RW1 and RW4 x-components
[0, 1, 0, 1 / np.sqrt(3)], # RW2 and RW4 y-components
[0, 0, 1, 1 / np.sqrt(3)], # RW3 and RW4 z-components
]
)
Flight software model: A custom flight software model is defined to support four RWs. It is based on the
SteeringImagerFSWModel, with the main modification being the addition of therwNullSpacemodule. Due to the redundancy of having four RWs, there are infinitely many solutions for mapping the required body control torque to individual RW torques. To address this, once the control torque is computed, the RW null space is used to decelerate the wheels without applying additional torque to the spacecraft.
[4]:
class CustomSteeringImagerFSWModel(fsw.SteeringImagerFSWModel):
def __init__(self, *args, **kwargs) -> None:
"""Convenience type that combines the imaging FSW model with MRP steering for four reaction wheels."""
super().__init__(*args, **kwargs)
def _set_config_msgs(self) -> None:
super()._set_config_msgs()
self._set_rw_constellation_msg()
def _set_rw_constellation_msg(self) -> None:
"""Set the reaction wheel constellation message."""
rwConstellationConfig = messaging.RWConstellationMsgPayload()
rwConstellationConfig.numRW = self.dynamics.rwFactory.getNumOfDevices()
rwConfigElementList = []
for i in range(4):
rwConfigElementMsg = messaging.RWConfigElementMsgPayload()
rwConfigElementMsg.gsHat_B = self.dynamics.Gs[:, i]
rwConfigElementMsg.Js = self.dynamics.rwFactory.rwList[f"RW{i + 1}"].Js
rwConfigElementMsg.uMax = self.dynamics.rwFactory.rwList[f"RW{i + 1}"].u_max
rwConfigElementList.append(rwConfigElementMsg)
rwConstellationConfig.reactionWheels = rwConfigElementList
self.rwConstellationConfigInMsg = messaging.RWConstellationMsg().write(
rwConstellationConfig
)
def _set_gateway_msgs(self) -> None:
"""Create C-wrapped gateway messages."""
self.attRefMsg = messaging.AttRefMsg_C()
self.attGuidMsg = messaging.AttGuidMsg_C()
self._zero_gateway_msgs()
# connect gateway FSW effector command msgs with the dynamics
self.dynamics.rwStateEffector.rwMotorCmdInMsg.subscribeTo(
self.rwNullSpace.rwMotorTorqueOutMsg
)
self.dynamics.thrusterSet.cmdsInMsg.subscribeTo(
self.thrDump.thrusterOnTimeOutMsg
)
class MRPControlTask(fsw.SteeringImagerFSWModel.MRPControlTask):
def _create_module_data(self) -> None:
super()._create_module_data()
self.rwNullSpace = self.fsw.rwNullSpace = rwNullSpace.rwNullSpace()
self.rwNullSpace.ModelTag = "rwNullSpace"
def _setup_fsw_objects(self, **kwargs) -> None:
super()._setup_fsw_objects(**kwargs)
self.set_rw_null_space(**kwargs)
@default_args(OmegaGain=0.3)
def set_rw_null_space(
self,
OmegaGain: float,
**kwargs,
) -> None:
"""Define the null space to slow down the wheels."""
self.rwNullSpace.rwMotorTorqueInMsg.subscribeTo(
self.rwMotorTorque.rwMotorTorqueOutMsg
)
self.rwNullSpace.rwSpeedsInMsg.subscribeTo(
self.fsw.dynamics.rwStateEffector.rwSpeedOutMsg
)
self.rwNullSpace.rwConfigInMsg.subscribeTo(
self.fsw.rwConstellationConfigInMsg
)
self.rwNullSpace.OmegaGain = OmegaGain
self._add_model_to_task(self.rwNullSpace, priority=1193)
Configure the Satellite
-
SatProperties: Body angular velocity, instrument pointing direction, body position, body velocity, battery charge (properties in flight software model or dynamics model). Also, customized dynamics property in CustomDynModel above: Angle between the sun and the solar panel and four RW speed fraction.
OpportunityProperties: Target’s priority, normalized location, and target angle (upcoming 32 targets).
Time: Simulation time.
Eclipse: Next eclipse start and end times.
-
Desat: Manage momentum for the RWs for 60 seconds.
Charge: Enter a sun-pointing charging mode for 60 seconds.
Image: Image target from upcoming 32 targets
The fault is introduced by overriding the reset_post_sim_init function. The probability of the fault occurring can be specified using the fault_chance argument, and the time of occurrence can be set using the fault_time argument.
[5]:
class CustomSatComposed(sats.ImagingSatellite):
observation_spec = [
obs.SatProperties(
dict(prop="omega_BP_P", norm=0.03),
dict(prop="c_hat_P"),
dict(prop="r_BN_P", norm=orbitalMotion.REQ_EARTH * 1e3),
dict(prop="v_BN_P", norm=7616.5),
dict(prop="battery_charge_fraction"),
dict(prop="solar_angle_norm"),
dict(prop="wheel_speeds_frac"),
),
obs.OpportunityProperties(
dict(prop="priority"),
dict(prop="r_LP_P", norm=orbitalMotion.REQ_EARTH * 1e3),
dict(prop="target_angle", norm=np.pi),
type="target",
n_ahead_observe=32,
),
obs.Time(),
obs.Eclipse(norm=5700),
]
action_spec = [
act.Desat(duration=60.0),
act.Charge(duration=60.0),
act.Image(n_ahead_image=32),
]
# Modified the constructor to include the fault chance and list
def __init__(self, *args, fault_chance=0, fault_time=0.0, **kwargs) -> None:
super().__init__(*args, **kwargs)
self.fault_chance = fault_chance
self.fault_time = fault_time
self.faultList = [] # List to store faults
def reset_post_sim_init(self) -> None:
super().reset_post_sim_init()
if np.random.random() < self.fault_chance:
powerFault = RwPowerFault(
"rwPowerLimited", self.fault_time, reducedLimit=1.0, wheelIdx=1
)
self.faultList = [powerFault]
self.simulator.faultList = self.faultList
for i in range(len(self.faultList)):
self.faultList[i].addFaultToSimulation(self, i)
dyn_type = CustomDynModel
fsw_type = CustomSteeringImagerFSWModel
Configure Satellite Pareameters
When instantiating a satellite, these parameters can be overriden with a constant or rerandomized every time the environment is reset using the sat_args dictionary.
[6]:
dataStorageCapacity = 20 * 8e6 * 100
batteryStorageCapacity = 80.0 * 3600 * 2
sat_args = CustomSatComposed.default_sat_args(
oe=random_orbit,
imageAttErrorRequirement=0.01,
imageRateErrorRequirement=0.01,
batteryStorageCapacity=batteryStorageCapacity,
storedCharge_Init=lambda: np.random.uniform(0.4, 1.0) * batteryStorageCapacity,
u_max=0.2, # More realistic values than 1.0
K1=0.5, # Updated value to have smooth and more predictable control
nHat_B=np.array([0, 0, -1]),
imageTargetMinimumElevation=np.radians(45),
rwBasePower=20,
maxWheelSpeed=1500,
storageInit=lambda: np.random.randint(
0 * dataStorageCapacity,
0.01 * dataStorageCapacity,
),
wheelSpeeds=lambda: np.random.uniform(-900, 900, 4),
disturbance_vector=lambda: random_unit_vector(),
)
# Make the satellites
satellites = []
satellites.append(
CustomSatComposed(
"EO",
sat_args,
fault_chance=1.0,
fault_time=0.0, # Fault occurs at 0.0 (nano seconds)
)
)
Making and interacting the Environment
For this example, the single-agent SatelliteTasking environment is used. n addition to the configured satellite, the environment requires a scenario, which defines the context in which the satellite operates. In this case, the scenario uses UniformTargets, placing 1000 uniformly distributed targets across the Earth’s surface. The environment also takes a rewarder, which defines how
data collected from the scenario is rewarded. Here, UniqueImageReward is used, which assigns rewards based on the sum of the priorities of uniquely imaged targets in each episode.
[7]:
env = SatelliteTasking(
satellite=satellites,
terminate_on_time_limit=True,
world_type=world.GroundStationWorldModel,
world_args=world.GroundStationWorldModel.default_world_args(),
scenario=scene.UniformTargets(n_targets=1000),
rewarder=data.UniqueImageReward(),
sim_rate=0.5,
max_step_duration=300.0,
time_limit=95 * 60 * 3,
log_level="INFO",
failure_penalty=0,
# disable_env_checker=True, # For debugging
)
First, the environment is reset. A seed is provided to ensure reproducibility of the results; it can be removed to enable randomized testing.
[8]:
observation, info = env.reset(seed=1)
2025-11-05 22:48:35,129 gym INFO Resetting environment with seed=1
2025-11-05 22:48:35,131 scene.targets INFO Generating 1000 targets
2025-11-05 22:48:35,226 sats.satellite.EO INFO <0.00> EO: Finding opportunity windows from 0.00 to 17400.00 seconds
2025-11-05 22:48:35,503 gym INFO <0.00> Environment reset
The composed satellite action space returns a human-readable action map and each satellite has the same action space and similar observation space.
[9]:
print("Actions:", satellites[0].action_description)
print("States:", env.unwrapped.satellites[0].observation_description, "\n")
# Using the composed satellite features also provides a human-readable state:
for satellite in env.unwrapped.satellites:
for k, v in satellite.observation_builder.obs_dict().items():
print(f"{k}: {v}")
Actions: ['action_desat', 'action_charge', 'action_image_0', 'action_image_1', 'action_image_2', 'action_image_3', 'action_image_4', 'action_image_5', 'action_image_6', 'action_image_7', 'action_image_8', 'action_image_9', 'action_image_10', 'action_image_11', 'action_image_12', 'action_image_13', 'action_image_14', 'action_image_15', 'action_image_16', 'action_image_17', 'action_image_18', 'action_image_19', 'action_image_20', 'action_image_21', 'action_image_22', 'action_image_23', 'action_image_24', 'action_image_25', 'action_image_26', 'action_image_27', 'action_image_28', 'action_image_29', 'action_image_30', 'action_image_31']
States: [np.str_('sat_props.omega_BP_P_normd[0]'), np.str_('sat_props.omega_BP_P_normd[1]'), np.str_('sat_props.omega_BP_P_normd[2]'), np.str_('sat_props.c_hat_P[0]'), np.str_('sat_props.c_hat_P[1]'), np.str_('sat_props.c_hat_P[2]'), np.str_('sat_props.r_BN_P_normd[0]'), np.str_('sat_props.r_BN_P_normd[1]'), np.str_('sat_props.r_BN_P_normd[2]'), np.str_('sat_props.v_BN_P_normd[0]'), np.str_('sat_props.v_BN_P_normd[1]'), np.str_('sat_props.v_BN_P_normd[2]'), np.str_('sat_props.battery_charge_fraction'), np.str_('sat_props.solar_angle_norm'), np.str_('sat_props.wheel_speeds_frac[0]'), np.str_('sat_props.wheel_speeds_frac[1]'), np.str_('sat_props.wheel_speeds_frac[2]'), np.str_('target.target_0.priority'), np.str_('target.target_0.r_LP_P_normd[0]'), np.str_('target.target_0.r_LP_P_normd[1]'), np.str_('target.target_0.r_LP_P_normd[2]'), np.str_('target.target_0.target_angle_normd'), np.str_('target.target_1.priority'), np.str_('target.target_1.r_LP_P_normd[0]'), np.str_('target.target_1.r_LP_P_normd[1]'), np.str_('target.target_1.r_LP_P_normd[2]'), np.str_('target.target_1.target_angle_normd'), np.str_('target.target_2.priority'), np.str_('target.target_2.r_LP_P_normd[0]'), np.str_('target.target_2.r_LP_P_normd[1]'), np.str_('target.target_2.r_LP_P_normd[2]'), np.str_('target.target_2.target_angle_normd'), np.str_('target.target_3.priority'), np.str_('target.target_3.r_LP_P_normd[0]'), np.str_('target.target_3.r_LP_P_normd[1]'), np.str_('target.target_3.r_LP_P_normd[2]'), np.str_('target.target_3.target_angle_normd'), np.str_('target.target_4.priority'), np.str_('target.target_4.r_LP_P_normd[0]'), np.str_('target.target_4.r_LP_P_normd[1]'), np.str_('target.target_4.r_LP_P_normd[2]'), np.str_('target.target_4.target_angle_normd'), np.str_('target.target_5.priority'), np.str_('target.target_5.r_LP_P_normd[0]'), np.str_('target.target_5.r_LP_P_normd[1]'), np.str_('target.target_5.r_LP_P_normd[2]'), np.str_('target.target_5.target_angle_normd'), np.str_('target.target_6.priority'), np.str_('target.target_6.r_LP_P_normd[0]'), np.str_('target.target_6.r_LP_P_normd[1]'), np.str_('target.target_6.r_LP_P_normd[2]'), np.str_('target.target_6.target_angle_normd'), np.str_('target.target_7.priority'), np.str_('target.target_7.r_LP_P_normd[0]'), np.str_('target.target_7.r_LP_P_normd[1]'), np.str_('target.target_7.r_LP_P_normd[2]'), np.str_('target.target_7.target_angle_normd'), np.str_('target.target_8.priority'), np.str_('target.target_8.r_LP_P_normd[0]'), np.str_('target.target_8.r_LP_P_normd[1]'), np.str_('target.target_8.r_LP_P_normd[2]'), np.str_('target.target_8.target_angle_normd'), np.str_('target.target_9.priority'), np.str_('target.target_9.r_LP_P_normd[0]'), np.str_('target.target_9.r_LP_P_normd[1]'), np.str_('target.target_9.r_LP_P_normd[2]'), np.str_('target.target_9.target_angle_normd'), np.str_('target.target_10.priority'), np.str_('target.target_10.r_LP_P_normd[0]'), np.str_('target.target_10.r_LP_P_normd[1]'), np.str_('target.target_10.r_LP_P_normd[2]'), np.str_('target.target_10.target_angle_normd'), np.str_('target.target_11.priority'), np.str_('target.target_11.r_LP_P_normd[0]'), np.str_('target.target_11.r_LP_P_normd[1]'), np.str_('target.target_11.r_LP_P_normd[2]'), np.str_('target.target_11.target_angle_normd'), np.str_('target.target_12.priority'), np.str_('target.target_12.r_LP_P_normd[0]'), np.str_('target.target_12.r_LP_P_normd[1]'), np.str_('target.target_12.r_LP_P_normd[2]'), np.str_('target.target_12.target_angle_normd'), np.str_('target.target_13.priority'), np.str_('target.target_13.r_LP_P_normd[0]'), np.str_('target.target_13.r_LP_P_normd[1]'), np.str_('target.target_13.r_LP_P_normd[2]'), np.str_('target.target_13.target_angle_normd'), np.str_('target.target_14.priority'), np.str_('target.target_14.r_LP_P_normd[0]'), np.str_('target.target_14.r_LP_P_normd[1]'), np.str_('target.target_14.r_LP_P_normd[2]'), np.str_('target.target_14.target_angle_normd'), np.str_('target.target_15.priority'), np.str_('target.target_15.r_LP_P_normd[0]'), np.str_('target.target_15.r_LP_P_normd[1]'), np.str_('target.target_15.r_LP_P_normd[2]'), np.str_('target.target_15.target_angle_normd'), np.str_('target.target_16.priority'), np.str_('target.target_16.r_LP_P_normd[0]'), np.str_('target.target_16.r_LP_P_normd[1]'), np.str_('target.target_16.r_LP_P_normd[2]'), np.str_('target.target_16.target_angle_normd'), np.str_('target.target_17.priority'), np.str_('target.target_17.r_LP_P_normd[0]'), np.str_('target.target_17.r_LP_P_normd[1]'), np.str_('target.target_17.r_LP_P_normd[2]'), np.str_('target.target_17.target_angle_normd'), np.str_('target.target_18.priority'), np.str_('target.target_18.r_LP_P_normd[0]'), np.str_('target.target_18.r_LP_P_normd[1]'), np.str_('target.target_18.r_LP_P_normd[2]'), np.str_('target.target_18.target_angle_normd'), np.str_('target.target_19.priority'), np.str_('target.target_19.r_LP_P_normd[0]'), np.str_('target.target_19.r_LP_P_normd[1]'), np.str_('target.target_19.r_LP_P_normd[2]'), np.str_('target.target_19.target_angle_normd'), np.str_('target.target_20.priority'), np.str_('target.target_20.r_LP_P_normd[0]'), np.str_('target.target_20.r_LP_P_normd[1]'), np.str_('target.target_20.r_LP_P_normd[2]'), np.str_('target.target_20.target_angle_normd'), np.str_('target.target_21.priority'), np.str_('target.target_21.r_LP_P_normd[0]'), np.str_('target.target_21.r_LP_P_normd[1]'), np.str_('target.target_21.r_LP_P_normd[2]'), np.str_('target.target_21.target_angle_normd'), np.str_('target.target_22.priority'), np.str_('target.target_22.r_LP_P_normd[0]'), np.str_('target.target_22.r_LP_P_normd[1]'), np.str_('target.target_22.r_LP_P_normd[2]'), np.str_('target.target_22.target_angle_normd'), np.str_('target.target_23.priority'), np.str_('target.target_23.r_LP_P_normd[0]'), np.str_('target.target_23.r_LP_P_normd[1]'), np.str_('target.target_23.r_LP_P_normd[2]'), np.str_('target.target_23.target_angle_normd'), np.str_('target.target_24.priority'), np.str_('target.target_24.r_LP_P_normd[0]'), np.str_('target.target_24.r_LP_P_normd[1]'), np.str_('target.target_24.r_LP_P_normd[2]'), np.str_('target.target_24.target_angle_normd'), np.str_('target.target_25.priority'), np.str_('target.target_25.r_LP_P_normd[0]'), np.str_('target.target_25.r_LP_P_normd[1]'), np.str_('target.target_25.r_LP_P_normd[2]'), np.str_('target.target_25.target_angle_normd'), np.str_('target.target_26.priority'), np.str_('target.target_26.r_LP_P_normd[0]'), np.str_('target.target_26.r_LP_P_normd[1]'), np.str_('target.target_26.r_LP_P_normd[2]'), np.str_('target.target_26.target_angle_normd'), np.str_('target.target_27.priority'), np.str_('target.target_27.r_LP_P_normd[0]'), np.str_('target.target_27.r_LP_P_normd[1]'), np.str_('target.target_27.r_LP_P_normd[2]'), np.str_('target.target_27.target_angle_normd'), np.str_('target.target_28.priority'), np.str_('target.target_28.r_LP_P_normd[0]'), np.str_('target.target_28.r_LP_P_normd[1]'), np.str_('target.target_28.r_LP_P_normd[2]'), np.str_('target.target_28.target_angle_normd'), np.str_('target.target_29.priority'), np.str_('target.target_29.r_LP_P_normd[0]'), np.str_('target.target_29.r_LP_P_normd[1]'), np.str_('target.target_29.r_LP_P_normd[2]'), np.str_('target.target_29.target_angle_normd'), np.str_('target.target_30.priority'), np.str_('target.target_30.r_LP_P_normd[0]'), np.str_('target.target_30.r_LP_P_normd[1]'), np.str_('target.target_30.r_LP_P_normd[2]'), np.str_('target.target_30.target_angle_normd'), np.str_('target.target_31.priority'), np.str_('target.target_31.r_LP_P_normd[0]'), np.str_('target.target_31.r_LP_P_normd[1]'), np.str_('target.target_31.r_LP_P_normd[2]'), np.str_('target.target_31.target_angle_normd'), np.str_('time'), np.str_('eclipse[0]'), np.str_('eclipse[1]')]
sat_props: {'omega_BP_P_normd': array([0.00137284, 0.00080893, 0.00185074]), 'c_hat_P': array([-0.94095395, -0.07120216, -0.3309621 ]), 'r_BN_P_normd': array([-0.76023893, -0.76226973, 0.03873832]), 'v_BN_P_normd': array([-0.74001565, 0.72585949, -0.23976204]), 'battery_charge_fraction': 0.48805353449026784, 'solar_angle_norm': np.float64(0.3675725758375922), 'wheel_speeds_frac': array([ 0.2222634 , -0.3546573 , 0.45374092])}
target: {'target_0': {'priority': 0.6797657443023485, 'r_LP_P_normd': array([-0.72304393, -0.69048255, 0.02100749]), 'target_angle_normd': np.float64(0.6357597658378449)}, 'target_1': {'priority': 0.1011278274566988, 'r_LP_P_normd': array([-0.8839314 , -0.46366914, -0.06063175]), 'target_angle_normd': np.float64(0.3764086411837673)}, 'target_2': {'priority': 0.6931851990942818, 'r_LP_P_normd': array([-0.92043412, -0.36952122, -0.12749548]), 'target_angle_normd': np.float64(0.3723877146688926)}, 'target_3': {'priority': 0.17225514293500632, 'r_LP_P_normd': array([-0.92565997, -0.37833103, -0.00438879]), 'target_angle_normd': np.float64(0.38981813759316086)}, 'target_4': {'priority': 0.5711709172856598, 'r_LP_P_normd': array([-0.96343073, -0.26718398, -0.02034567]), 'target_angle_normd': np.float64(0.3943146022248573)}, 'target_5': {'priority': 0.39915339691165386, 'r_LP_P_normd': array([-0.9832796 , -0.17545716, -0.04874432]), 'target_angle_normd': np.float64(0.39937897834300234)}, 'target_6': {'priority': 0.3659991252731889, 'r_LP_P_normd': array([-0.99415125, -0.07715346, -0.07556872]), 'target_angle_normd': np.float64(0.4078843692651548)}, 'target_7': {'priority': 0.8196046614443331, 'r_LP_P_normd': array([-0.99765329, -0.01207943, -0.06739442]), 'target_angle_normd': np.float64(0.4168294389284707)}, 'target_8': {'priority': 0.31321450974410614, 'r_LP_P_normd': array([-0.97449116, 0.1725315 , -0.1435265 ]), 'target_angle_normd': np.float64(0.4358647819265422)}, 'target_9': {'priority': 0.9484757208286156, 'r_LP_P_normd': array([-0.96031054, 0.17441228, -0.21767872]), 'target_angle_normd': np.float64(0.433242760838707)}, 'target_10': {'priority': 0.48592850306846924, 'r_LP_P_normd': array([-0.96853779, 0.21402263, -0.12699944]), 'target_angle_normd': np.float64(0.4426489488945429)}, 'target_11': {'priority': 0.6184129633885858, 'r_LP_P_normd': array([-0.96210743, 0.21701823, -0.16508293]), 'target_angle_normd': np.float64(0.4411215055325246)}, 'target_12': {'priority': 0.17947175160982998, 'r_LP_P_normd': array([-0.94017117, 0.243405 , -0.23839502]), 'target_angle_normd': np.float64(0.4427323667761162)}, 'target_13': {'priority': 0.7384995398085523, 'r_LP_P_normd': array([-0.80487741, 0.52282763, -0.28075545]), 'target_angle_normd': np.float64(0.48648454206124414)}, 'target_14': {'priority': 0.27114812998614257, 'r_LP_P_normd': array([-0.76729423, 0.58576762, -0.26102845]), 'target_angle_normd': np.float64(0.4977256880591026)}, 'target_15': {'priority': 0.602211552115518, 'r_LP_P_normd': array([-0.69808315, 0.6848033 , -0.20910371]), 'target_angle_normd': np.float64(0.5172281968118192)}, 'target_16': {'priority': 0.379803286768697, 'r_LP_P_normd': array([-0.49501598, 0.82573585, -0.27040613]), 'target_angle_normd': np.float64(0.5507581988576493)}, 'target_17': {'priority': 0.4436831213331952, 'r_LP_P_normd': array([-0.45042693, 0.86931897, -0.20347018]), 'target_angle_normd': np.float64(0.5625207407345922)}, 'target_18': {'priority': 0.7048706468084478, 'r_LP_P_normd': array([-0.21420724, 0.9326702 , -0.29024395]), 'target_angle_normd': np.float64(0.59373704837557)}, 'target_19': {'priority': 0.9285111717464954, 'r_LP_P_normd': array([-0.19966509, 0.94633197, -0.25414496]), 'target_angle_normd': np.float64(0.5980174335717613)}, 'target_20': {'priority': 0.6283839193934228, 'r_LP_P_normd': array([ 0.04079828, 0.96062029, -0.27485298]), 'target_angle_normd': np.float64(0.6314125127394534)}, 'target_21': {'priority': 0.1929743249397491, 'r_LP_P_normd': array([ 0.0355612 , 0.97627369, -0.21360027]), 'target_angle_normd': np.float64(0.6341742328673365)}, 'target_22': {'priority': 0.44341724161916973, 'r_LP_P_normd': array([ 0.11710105, 0.96569059, -0.23179522]), 'target_angle_normd': np.float64(0.6446733019141536)}, 'target_23': {'priority': 0.11836853522372437, 'r_LP_P_normd': array([ 0.34219224, 0.92570533, -0.16116486]), 'target_angle_normd': np.float64(0.6810789969475336)}, 'target_24': {'priority': 0.8270836989643272, 'r_LP_P_normd': array([ 0.37614852, 0.90638066, -0.19231846]), 'target_angle_normd': np.float64(0.6842502087329536)}, 'target_25': {'priority': 0.5185496026201819, 'r_LP_P_normd': array([ 0.38424706, 0.90105932, -0.20111263]), 'target_angle_normd': np.float64(0.6849377870859835)}, 'target_26': {'priority': 0.9675170836931263, 'r_LP_P_normd': array([ 0.38356319, 0.9111912 , -0.15036584]), 'target_angle_normd': np.float64(0.6878383017756565)}, 'target_27': {'priority': 0.9065897890064923, 'r_LP_P_normd': array([ 0.55753272, 0.82426831, -0.09868644]), 'target_angle_normd': np.float64(0.7179839245481688)}, 'target_28': {'priority': 0.9282669521531632, 'r_LP_P_normd': array([ 0.62887363, 0.76646723, -0.13056009]), 'target_angle_normd': np.float64(0.7278214487998897)}, 'target_29': {'priority': 0.07379201140065461, 'r_LP_P_normd': array([ 0.83349796, 0.55251892, -0.00199665]), 'target_angle_normd': np.float64(0.7770010755992804)}, 'target_30': {'priority': 0.010627938976362383, 'r_LP_P_normd': array([ 0.87933586, 0.46953401, -0.07941194]), 'target_angle_normd': np.float64(0.7821048162611637)}, 'target_31': {'priority': 0.13642904696262903, 'r_LP_P_normd': array([ 0.90564527, 0.41905752, -0.06478763]), 'target_angle_normd': np.float64(0.7904117166194159)}}
time: 0.0
eclipse: [np.float64(0.7684210526315789), np.float64(0.14210526315789473)]
The simulation runs until either the battery is depleted, a RW exceeds its maximum speed (both considered failures), or a timeout occurs (which simply stops the simulation).
[10]:
total_reward = 0.0
while True:
observation, reward, terminated, truncated, info = env.step(
env.action_space.sample()
)
total_reward += reward
if terminated or truncated:
print("Episode complete.")
break
print("Total reward:", total_reward)
2025-11-05 22:48:35,518 gym INFO <0.00> === STARTING STEP ===
2025-11-05 22:48:35,519 sats.satellite.EO INFO <0.00> EO: target index 5 tasked
2025-11-05 22:48:35,519 sats.satellite.EO INFO <0.00> EO: Target(tgt-97) tasked for imaging
2025-11-05 22:48:35,520 sats.satellite.EO INFO <0.00> EO: Target(tgt-97) window enabled: 482.7 to 557.8
2025-11-05 22:48:35,520 sats.satellite.EO INFO <0.00> EO: setting timed terminal event at 557.8
2025-11-05 22:48:35,521 sats.satellite.EO INFO <0.00> EO: RW Power Fault: RW1's power limit reduced to 1.0 Watts at 0.0 minutes!
2025-11-05 22:48:35,523 sats.satellite.EO INFO <0.50> EO: imaged Target(tgt-97)
2025-11-05 22:48:35,523 data.base INFO <0.50> Total reward: {'EO': 0.39915339691165386}
2025-11-05 22:48:35,524 comm.communication INFO <0.50> Optimizing data communication between all pairs of satellites
2025-11-05 22:48:35,525 sats.satellite.EO INFO <0.50> EO: Satellite EO requires retasking
2025-11-05 22:48:35,529 gym INFO <0.50> Step reward: 0.39915339691165386
2025-11-05 22:48:35,530 gym INFO <0.50> === STARTING STEP ===
2025-11-05 22:48:35,530 sats.satellite.EO INFO <0.50> EO: target index 0 tasked
2025-11-05 22:48:35,531 sats.satellite.EO INFO <0.50> EO: Target(tgt-873) tasked for imaging
2025-11-05 22:48:35,532 sats.satellite.EO INFO <0.50> EO: Target(tgt-873) window enabled: 0.0 to 81.3
2025-11-05 22:48:35,532 sats.satellite.EO INFO <0.50> EO: setting timed terminal event at 81.3
2025-11-05 22:48:35,534 sats.satellite.EO INFO <1.00> EO: imaged Target(tgt-873)
2025-11-05 22:48:35,534 data.base INFO <1.00> Total reward: {'EO': 0.6797657443023485}
2025-11-05 22:48:35,534 comm.communication INFO <1.00> Optimizing data communication between all pairs of satellites
2025-11-05 22:48:35,535 sats.satellite.EO INFO <1.00> EO: Satellite EO requires retasking
2025-11-05 22:48:35,540 gym INFO <1.00> Step reward: 0.6797657443023485
2025-11-05 22:48:35,540 gym INFO <1.00> === STARTING STEP ===
2025-11-05 22:48:35,541 sats.satellite.EO INFO <1.00> EO: target index 4 tasked
2025-11-05 22:48:35,541 sats.satellite.EO INFO <1.00> EO: Target(tgt-378) tasked for imaging
2025-11-05 22:48:35,542 sats.satellite.EO INFO <1.00> EO: Target(tgt-378) window enabled: 564.0 to 650.0
2025-11-05 22:48:35,543 sats.satellite.EO INFO <1.00> EO: setting timed terminal event at 650.0
2025-11-05 22:48:35,544 sats.satellite.EO INFO <1.50> EO: imaged Target(tgt-378)
2025-11-05 22:48:35,545 data.base INFO <1.50> Total reward: {'EO': 0.3659991252731889}
2025-11-05 22:48:35,545 comm.communication INFO <1.50> Optimizing data communication between all pairs of satellites
2025-11-05 22:48:35,546 sats.satellite.EO INFO <1.50> EO: Satellite EO requires retasking
2025-11-05 22:48:35,550 gym INFO <1.50> Step reward: 0.3659991252731889
2025-11-05 22:48:35,551 gym INFO <1.50> === STARTING STEP ===
2025-11-05 22:48:35,552 sats.satellite.EO INFO <1.50> EO: target index 12 tasked
2025-11-05 22:48:35,552 sats.satellite.EO INFO <1.50> EO: Target(tgt-594) tasked for imaging
2025-11-05 22:48:35,553 sats.satellite.EO INFO <1.50> EO: Target(tgt-594) window enabled: 1276.7 to 1383.1
2025-11-05 22:48:35,553 sats.satellite.EO INFO <1.50> EO: setting timed terminal event at 1383.1
2025-11-05 22:48:35,555 sats.satellite.EO INFO <2.00> EO: imaged Target(tgt-594)
2025-11-05 22:48:35,555 data.base INFO <2.00> Total reward: {'EO': 0.602211552115518}
2025-11-05 22:48:35,556 comm.communication INFO <2.00> Optimizing data communication between all pairs of satellites
2025-11-05 22:48:35,556 sats.satellite.EO INFO <2.00> EO: Satellite EO requires retasking
2025-11-05 22:48:35,561 gym INFO <2.00> Step reward: 0.602211552115518
2025-11-05 22:48:35,561 gym INFO <2.00> === STARTING STEP ===
2025-11-05 22:48:35,562 sats.satellite.EO INFO <2.00> EO: target index 8 tasked
2025-11-05 22:48:35,563 sats.satellite.EO INFO <2.00> EO: Target(tgt-336) tasked for imaging
2025-11-05 22:48:35,564 sats.satellite.EO INFO <2.00> EO: Target(tgt-336) window enabled: 810.7 to 925.8
2025-11-05 22:48:35,564 sats.satellite.EO INFO <2.00> EO: setting timed terminal event at 925.8
2025-11-05 22:48:35,565 sats.satellite.EO INFO <2.50> EO: imaged Target(tgt-336)
2025-11-05 22:48:35,566 data.base INFO <2.50> Total reward: {'EO': 0.6184129633885858}
2025-11-05 22:48:35,567 comm.communication INFO <2.50> Optimizing data communication between all pairs of satellites
2025-11-05 22:48:35,567 sats.satellite.EO INFO <2.50> EO: Satellite EO requires retasking
2025-11-05 22:48:35,571 gym INFO <2.50> Step reward: 0.6184129633885858
2025-11-05 22:48:35,572 gym INFO <2.50> === STARTING STEP ===
2025-11-05 22:48:35,573 sats.satellite.EO INFO <2.50> EO: target index 16 tasked
2025-11-05 22:48:35,573 sats.satellite.EO INFO <2.50> EO: Target(tgt-842) tasked for imaging
2025-11-05 22:48:35,574 sats.satellite.EO INFO <2.50> EO: Target(tgt-842) window enabled: 1958.3 to 2072.1
2025-11-05 22:48:35,575 sats.satellite.EO INFO <2.50> EO: setting timed terminal event at 2072.1
2025-11-05 22:48:35,576 sats.satellite.EO INFO <3.00> EO: imaged Target(tgt-842)
2025-11-05 22:48:35,577 data.base INFO <3.00> Total reward: {'EO': 0.1929743249397491}
2025-11-05 22:48:35,577 comm.communication INFO <3.00> Optimizing data communication between all pairs of satellites
2025-11-05 22:48:35,578 sats.satellite.EO INFO <3.00> EO: Satellite EO requires retasking
2025-11-05 22:48:35,583 gym INFO <3.00> Step reward: 0.1929743249397491
2025-11-05 22:48:35,584 gym INFO <3.00> === STARTING STEP ===
2025-11-05 22:48:35,584 sats.satellite.EO INFO <3.00> EO: target index 3 tasked
2025-11-05 22:48:35,585 sats.satellite.EO INFO <3.00> EO: Target(tgt-706) tasked for imaging
2025-11-05 22:48:35,586 sats.satellite.EO INFO <3.00> EO: Target(tgt-706) window enabled: 409.7 to 464.9
2025-11-05 22:48:35,587 sats.satellite.EO INFO <3.00> EO: setting timed terminal event at 464.9
2025-11-05 22:48:35,588 sats.satellite.EO INFO <3.50> EO: imaged Target(tgt-706)
2025-11-05 22:48:35,588 data.base INFO <3.50> Total reward: {'EO': 0.5711709172856598}
2025-11-05 22:48:35,589 comm.communication INFO <3.50> Optimizing data communication between all pairs of satellites
2025-11-05 22:48:35,590 sats.satellite.EO INFO <3.50> EO: Satellite EO requires retasking
2025-11-05 22:48:35,594 gym INFO <3.50> Step reward: 0.5711709172856598
2025-11-05 22:48:35,594 gym INFO <3.50> === STARTING STEP ===
2025-11-05 22:48:35,595 sats.satellite.EO INFO <3.50> EO: target index 11 tasked
2025-11-05 22:48:35,596 sats.satellite.EO INFO <3.50> EO: Target(tgt-530) tasked for imaging
2025-11-05 22:48:35,596 sats.satellite.EO INFO <3.50> EO: Target(tgt-530) window enabled: 1545.1 to 1638.7
2025-11-05 22:48:35,597 sats.satellite.EO INFO <3.50> EO: setting timed terminal event at 1638.7
2025-11-05 22:48:35,598 sats.satellite.EO INFO <4.00> EO: imaged Target(tgt-530)
2025-11-05 22:48:35,599 data.base INFO <4.00> Total reward: {'EO': 0.4436831213331952}
2025-11-05 22:48:35,599 comm.communication INFO <4.00> Optimizing data communication between all pairs of satellites
2025-11-05 22:48:35,600 sats.satellite.EO INFO <4.00> EO: Satellite EO requires retasking
2025-11-05 22:48:35,604 gym INFO <4.00> Step reward: 0.4436831213331952
2025-11-05 22:48:35,605 gym INFO <4.00> === STARTING STEP ===
2025-11-05 22:48:35,605 sats.satellite.EO INFO <4.00> EO: target index 7 tasked
2025-11-05 22:48:35,606 sats.satellite.EO INFO <4.00> EO: Target(tgt-417) tasked for imaging
2025-11-05 22:48:35,607 sats.satellite.EO INFO <4.00> EO: Target(tgt-417) window enabled: 872.2 to 935.8
2025-11-05 22:48:35,608 sats.satellite.EO INFO <4.00> EO: setting timed terminal event at 935.8
2025-11-05 22:48:35,609 sats.satellite.EO INFO <4.50> EO: imaged Target(tgt-417)
2025-11-05 22:48:35,609 data.base INFO <4.50> Total reward: {'EO': 0.17947175160982998}
2025-11-05 22:48:35,610 comm.communication INFO <4.50> Optimizing data communication between all pairs of satellites
2025-11-05 22:48:35,610 sats.satellite.EO INFO <4.50> EO: Satellite EO requires retasking
2025-11-05 22:48:35,615 gym INFO <4.50> Step reward: 0.17947175160982998
2025-11-05 22:48:35,616 gym INFO <4.50> === STARTING STEP ===
2025-11-05 22:48:35,616 sats.satellite.EO INFO <4.50> EO: target index 4 tasked
2025-11-05 22:48:35,617 sats.satellite.EO INFO <4.50> EO: Target(tgt-548) tasked for imaging
2025-11-05 22:48:35,617 sats.satellite.EO INFO <4.50> EO: Target(tgt-548) window enabled: 772.6 to 881.0
2025-11-05 22:48:35,618 sats.satellite.EO INFO <4.50> EO: setting timed terminal event at 881.0
2025-11-05 22:48:35,696 sim.simulator INFO <304.50> Max step duration reached
2025-11-05 22:48:35,696 data.base INFO <304.50> Total reward: {}
2025-11-05 22:48:35,697 comm.communication INFO <304.50> Optimizing data communication between all pairs of satellites
2025-11-05 22:48:35,701 sats.satellite.EO WARNING <304.50> EO: failed rw_speeds_valid check
2025-11-05 22:48:35,702 gym INFO <304.50> Step reward: 0.0
2025-11-05 22:48:35,703 gym INFO <304.50> Episode terminated: True
2025-11-05 22:48:35,703 gym INFO <304.50> Episode truncated: False
Episode complete.
Total reward: 4.05284289715973