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]:
from collections.abc import Iterable
from typing import ClassVar
import numpy as np
from Basilisk.architecture import bskLogging, messaging
from Basilisk.fswAlgorithms import rwNullSpace
from Basilisk.simulation import reactionWheelStateEffector
from Basilisk.utilities import macros, orbitalMotion, simIncludeRW
from bsk_rl import SatelliteTasking, act, data, obs, sats, scene
from bsk_rl.sim import dyn, fsw
from bsk_rl.utils.functional import default_args
from bsk_rl.utils.orbital import random_orbit, random_unit_vector
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: ClassVar[list[obs.Observation]] = [
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: ClassVar[list[act.Action]] = [
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,
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)
2026-07-28 23:39:19,619 gym INFO Resetting environment with seed=1
2026-07-28 23:39:19,621 scene.targets INFO Generating 1000 targets
2026-07-28 23:39:19,675 sats.satellite.EO INFO <0.00> EO: Finding opportunity windows from 0.00 to 17400.00 seconds
2026-07-28 23:39:19,845 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(0.5980174335717612)}, '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(0.6341742328673367)}, '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(0.6810789969475335)}, '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)
2026-07-28 23:39:19,857 gym INFO <0.00> === STARTING STEP ===
2026-07-28 23:39:19,858 sats.satellite.EO INFO <0.00> EO: target index 31 tasked
2026-07-28 23:39:19,858 sats.satellite.EO INFO <0.00> EO: Target(tgt-723) tasked for imaging
2026-07-28 23:39:19,859 sats.satellite.EO INFO <0.00> EO: Target(tgt-723) window enabled: 2921.5 to 2962.2
2026-07-28 23:39:19,860 sats.satellite.EO INFO <0.00> EO: setting timed terminal event at 2962.2
2026-07-28 23:39:19,861 sats.satellite.EO INFO <0.00> EO: RW Power Fault: RW1's power limit reduced to 1.0 Watts at 0.0 minutes!
2026-07-28 23:39:19,862 sats.satellite.EO INFO <0.50> EO: imaged Target(tgt-723)
2026-07-28 23:39:19,862 data.base INFO <0.50> Total reward: {'EO': 0.13642904696262903}
2026-07-28 23:39:19,863 comm.communication INFO <0.50> Optimizing data communication between all pairs of satellites
2026-07-28 23:39:19,863 sats.satellite.EO INFO <0.50> EO: Satellite EO requires retasking
2026-07-28 23:39:19,867 gym INFO <0.50> Step reward: 0.13642904696262903
2026-07-28 23:39:19,868 gym INFO <0.50> === STARTING STEP ===
2026-07-28 23:39:19,868 sats.satellite.EO INFO <0.50> EO: target index 16 tasked
2026-07-28 23:39:19,868 sats.satellite.EO INFO <0.50> EO: Target(tgt-12) tasked for imaging
2026-07-28 23:39:19,869 sats.satellite.EO INFO <0.50> EO: Target(tgt-12) window enabled: 1488.7 to 1592.6
2026-07-28 23:39:19,869 sats.satellite.EO INFO <0.50> EO: setting timed terminal event at 1592.6
2026-07-28 23:39:19,871 sats.satellite.EO INFO <1.00> EO: imaged Target(tgt-12)
2026-07-28 23:39:19,871 data.base INFO <1.00> Total reward: {'EO': 0.379803286768697}
2026-07-28 23:39:19,872 comm.communication INFO <1.00> Optimizing data communication between all pairs of satellites
2026-07-28 23:39:19,872 sats.satellite.EO INFO <1.00> EO: Satellite EO requires retasking
2026-07-28 23:39:19,876 gym INFO <1.00> Step reward: 0.379803286768697
2026-07-28 23:39:19,877 gym INFO <1.00> === STARTING STEP ===
2026-07-28 23:39:19,877 sats.satellite.EO INFO <1.00> EO: target index 1 tasked
2026-07-28 23:39:19,877 sats.satellite.EO INFO <1.00> EO: Target(tgt-634) tasked for imaging
2026-07-28 23:39:19,878 sats.satellite.EO INFO <1.00> EO: Target(tgt-634) window enabled: 214.4 to 324.0
2026-07-28 23:39:19,878 sats.satellite.EO INFO <1.00> EO: setting timed terminal event at 324.0
2026-07-28 23:39:19,880 sats.satellite.EO INFO <1.50> EO: imaged Target(tgt-634)
2026-07-28 23:39:19,880 data.base INFO <1.50> Total reward: {'EO': 0.1011278274566988}
2026-07-28 23:39:19,881 comm.communication INFO <1.50> Optimizing data communication between all pairs of satellites
2026-07-28 23:39:19,881 sats.satellite.EO INFO <1.50> EO: Satellite EO requires retasking
2026-07-28 23:39:19,885 gym INFO <1.50> Step reward: 0.1011278274566988
2026-07-28 23:39:19,885 gym INFO <1.50> === STARTING STEP ===
2026-07-28 23:39:19,885 sats.satellite.EO INFO <1.50> EO: target index 31 tasked
2026-07-28 23:39:19,886 sats.satellite.EO INFO <1.50> EO: Target(tgt-524) tasked for imaging
2026-07-28 23:39:19,886 sats.satellite.EO INFO <1.50> EO: Target(tgt-524) window enabled: 3154.0 to 3193.7
2026-07-28 23:39:19,887 sats.satellite.EO INFO <1.50> EO: setting timed terminal event at 3193.7
2026-07-28 23:39:19,888 sats.satellite.EO INFO <2.00> EO: imaged Target(tgt-524)
2026-07-28 23:39:19,889 data.base INFO <2.00> Total reward: {'EO': 0.9023343976384425}
2026-07-28 23:39:19,889 comm.communication INFO <2.00> Optimizing data communication between all pairs of satellites
2026-07-28 23:39:19,889 sats.satellite.EO INFO <2.00> EO: Satellite EO requires retasking
2026-07-28 23:39:19,893 gym INFO <2.00> Step reward: 0.9023343976384425
2026-07-28 23:39:19,893 gym INFO <2.00> === STARTING STEP ===
2026-07-28 23:39:19,894 sats.satellite.EO INFO <2.00> EO: target index 0 tasked
2026-07-28 23:39:19,894 sats.satellite.EO INFO <2.00> EO: Target(tgt-873) tasked for imaging
2026-07-28 23:39:19,895 sats.satellite.EO INFO <2.00> EO: Target(tgt-873) window enabled: 0.0 to 81.3
2026-07-28 23:39:19,895 sats.satellite.EO INFO <2.00> EO: setting timed terminal event at 81.3
2026-07-28 23:39:19,897 sats.satellite.EO INFO <2.50> EO: imaged Target(tgt-873)
2026-07-28 23:39:19,897 data.base INFO <2.50> Total reward: {'EO': 0.6797657443023485}
2026-07-28 23:39:19,897 comm.communication INFO <2.50> Optimizing data communication between all pairs of satellites
2026-07-28 23:39:19,898 sats.satellite.EO INFO <2.50> EO: Satellite EO requires retasking
2026-07-28 23:39:19,901 gym INFO <2.50> Step reward: 0.6797657443023485
2026-07-28 23:39:19,902 gym INFO <2.50> === STARTING STEP ===
2026-07-28 23:39:19,902 sats.satellite.EO INFO <2.50> EO: target index 30 tasked
2026-07-28 23:39:19,903 sats.satellite.EO INFO <2.50> EO: Target(tgt-431) tasked for imaging
2026-07-28 23:39:19,904 sats.satellite.EO INFO <2.50> EO: Target(tgt-431) window enabled: 3091.7 to 3194.3
2026-07-28 23:39:19,904 sats.satellite.EO INFO <2.50> EO: setting timed terminal event at 3194.3
2026-07-28 23:39:19,905 sats.satellite.EO INFO <3.00> EO: imaged Target(tgt-431)
2026-07-28 23:39:19,906 data.base INFO <3.00> Total reward: {'EO': 0.992581228889437}
2026-07-28 23:39:19,906 comm.communication INFO <3.00> Optimizing data communication between all pairs of satellites
2026-07-28 23:39:19,906 sats.satellite.EO INFO <3.00> EO: Satellite EO requires retasking
2026-07-28 23:39:19,910 gym INFO <3.00> Step reward: 0.992581228889437
2026-07-28 23:39:19,911 gym INFO <3.00> === STARTING STEP ===
2026-07-28 23:39:19,911 sats.satellite.EO INFO <3.00> EO: target index 17 tasked
2026-07-28 23:39:19,912 sats.satellite.EO INFO <3.00> EO: Target(tgt-274) tasked for imaging
2026-07-28 23:39:19,913 sats.satellite.EO INFO <3.00> EO: Target(tgt-274) window enabled: 2008.3 to 2019.5
2026-07-28 23:39:19,913 sats.satellite.EO INFO <3.00> EO: setting timed terminal event at 2019.5
2026-07-28 23:39:19,914 sats.satellite.EO INFO <3.50> EO: imaged Target(tgt-274)
2026-07-28 23:39:19,915 data.base INFO <3.50> Total reward: {'EO': 0.6283839193934228}
2026-07-28 23:39:19,915 comm.communication INFO <3.50> Optimizing data communication between all pairs of satellites
2026-07-28 23:39:19,916 sats.satellite.EO INFO <3.50> EO: Satellite EO requires retasking
2026-07-28 23:39:19,919 gym INFO <3.50> Step reward: 0.6283839193934228
2026-07-28 23:39:19,920 gym INFO <3.50> === STARTING STEP ===
2026-07-28 23:39:19,920 sats.satellite.EO INFO <3.50> EO: target index 9 tasked
2026-07-28 23:39:19,921 sats.satellite.EO INFO <3.50> EO: Target(tgt-336) tasked for imaging
2026-07-28 23:39:19,921 sats.satellite.EO INFO <3.50> EO: Target(tgt-336) window enabled: 810.7 to 925.8
2026-07-28 23:39:19,922 sats.satellite.EO INFO <3.50> EO: setting timed terminal event at 925.8
2026-07-28 23:39:19,923 sats.satellite.EO INFO <4.00> EO: imaged Target(tgt-336)
2026-07-28 23:39:19,923 data.base INFO <4.00> Total reward: {'EO': 0.6184129633885858}
2026-07-28 23:39:19,924 comm.communication INFO <4.00> Optimizing data communication between all pairs of satellites
2026-07-28 23:39:19,924 sats.satellite.EO INFO <4.00> EO: Satellite EO requires retasking
2026-07-28 23:39:19,928 gym INFO <4.00> Step reward: 0.6184129633885858
2026-07-28 23:39:19,928 gym INFO <4.00> === STARTING STEP ===
2026-07-28 23:39:19,928 sats.satellite.EO INFO <4.00> EO: target index 24 tasked
2026-07-28 23:39:19,929 sats.satellite.EO INFO <4.00> EO: Target(tgt-11) tasked for imaging
2026-07-28 23:39:19,930 sats.satellite.EO INFO <4.00> EO: Target(tgt-11) window enabled: 2779.9 to 2878.2
2026-07-28 23:39:19,930 sats.satellite.EO INFO <4.00> EO: setting timed terminal event at 2878.2
2026-07-28 23:39:19,931 sats.satellite.EO INFO <4.50> EO: imaged Target(tgt-11)
2026-07-28 23:39:19,931 data.base INFO <4.50> Total reward: {'EO': 0.07379201140065461}
2026-07-28 23:39:19,932 comm.communication INFO <4.50> Optimizing data communication between all pairs of satellites
2026-07-28 23:39:19,932 sats.satellite.EO INFO <4.50> EO: Satellite EO requires retasking
2026-07-28 23:39:19,936 gym INFO <4.50> Step reward: 0.07379201140065461
2026-07-28 23:39:19,937 gym INFO <4.50> === STARTING STEP ===
2026-07-28 23:39:19,937 sats.satellite.EO INFO <4.50> EO: target index 5 tasked
2026-07-28 23:39:19,938 sats.satellite.EO INFO <4.50> EO: Target(tgt-93) tasked for imaging
2026-07-28 23:39:19,938 sats.satellite.EO INFO <4.50> EO: Target(tgt-93) window enabled: 648.0 to 671.8
2026-07-28 23:39:19,939 sats.satellite.EO INFO <4.50> EO: setting timed terminal event at 671.8
2026-07-28 23:39:20,000 sim.simulator INFO <304.50> Max step duration reached
2026-07-28 23:39:20,001 data.base INFO <304.50> Total reward: {}
2026-07-28 23:39:20,001 comm.communication INFO <304.50> Optimizing data communication between all pairs of satellites
2026-07-28 23:39:20,005 sats.satellite.EO WARNING <304.50> EO: failed rw_speeds_valid check
2026-07-28 23:39:20,005 gym INFO <304.50> Step reward: 0.0
2026-07-28 23:39:20,006 gym INFO <304.50> Episode terminated: True
2026-07-28 23:39:20,006 gym INFO <304.50> Episode truncated: False
Episode complete.
Total reward: 4.512630426200916