RSO Inspection

This example demonstrates the configuration of a resident space object (RSO) inspection environment, in which a servicer spacecraft circumnavigates a RSO to image the illuminated facets.

[1]:
from importlib.metadata import version
from bsk_rl import sats, obs, act, ConstellationTasking, scene, data
from bsk_rl.obs.relative_observations import rso_imaged_regions
from bsk_rl.utils.orbital import fibonacci_sphere
from bsk_rl.sim import dyn, fsw
import types
import numpy as np
from Basilisk.architecture import bskLogging
from functools import partial
from bsk_rl.utils.orbital import random_orbit, random_unit_vector, relative_to_chief
from Basilisk.utilities.orbitalMotion import elem2rv
from Basilisk.utilities.RigidBodyKinematics import C2MRP

bskLogging.setDefaultLogLevel(bskLogging.BSK_WARNING)

RLlib is actively developed and can change significantly from version to version. For this script, the following version is used:

[2]:
version("ray")  # Parent package of RLlib
[2]:
'2.35.0'

Defining the Satellites

First, the RSO satellite is configured. It is given support for nadir pointing through the ImagingDynModel and Downlink action.

[3]:
class RSOSat(sats.Satellite):
    observation_spec = [
        obs.SatProperties(dict(prop="one", fn=lambda _: 1.0)),
    ]
    action_spec = [act.Downlink(duration=1e9)]
    dyn_type = types.new_class(
        "Dyn", (dyn.ImagingDynModel, dyn.ConjunctionDynModel, dyn.RSODynModel)
    )
    fsw_type = fsw.ContinuousImagingFSWModel

Arguments for the satellite are configured for smooth pointing behavior.

[4]:
rso_sat_args = dict(
    conjunction_radius=2.0,
    K=7.0 / 20,
    P=35.0 / 20,
    Ki=1e-6,
    dragCoeff=0.0,
    batteryStorageCapacity=1e9,
    storedCharge_Init=1e9,
    wheelSpeeds=[0.0, 0.0, 0.0],
    u_max=1.0,
)

The inspector satellite has a more complex configuration. First, an observation function for the sun vector is defined.

[5]:
def sun_hat_chief(self, other):
    r_SN_N = (
        self.simulator.world.gravFactory.spiceObject.planetStateOutMsgs[
            self.simulator.world.sun_index
        ]
        .read()
        .PositionVector
    )
    r_BN_N = self.dynamics.r_BN_N
    r_SN_N = np.array(r_SN_N)
    r_SB_N = r_SN_N - r_BN_N
    r_SB_N_hat = r_SB_N / np.linalg.norm(r_SB_N)
    HN = other.dynamics.HN
    return HN @ r_SB_N_hat

The inspector satellite is configured with observations relating to the relative state and the mission objectives. The satellite is given an action for impulsively thrusting and drifting. The dynamics and flight software models introduce a maximum range check, collision checking orbital maneuvers, and RSO inspection.

[6]:
class InspectorSat(sats.Satellite):
    observation_spec = [
        obs.SatProperties(
            dict(prop="dv_available", norm=10),
            dict(prop="inclination", norm=np.pi),
            dict(prop="eccentricity", norm=0.1),
            dict(prop="semi_major_axis", norm=7000),
            dict(prop="ascending_node", norm=2 * np.pi),
            dict(prop="argument_of_periapsis", norm=2 * np.pi),
            dict(prop="true_anomaly", norm=2 * np.pi),
            dict(prop="beta_angle", norm=np.pi),
        ),
        obs.ResourceRewardWeight(),
        obs.RelativeProperties(
            dict(prop="r_DC_Hc", norm=500),
            dict(prop="v_DC_Hc", norm=5),
            dict(
                prop="rso_imaged_regions",
                fn=partial(
                    rso_imaged_regions,
                    region_centers=fibonacci_sphere(15),
                    frame="chief_hill",
                ),
            ),
            dict(prop="sun_hat_Hc", fn=sun_hat_chief),
            chief_name="RSO",
        ),
        obs.Eclipse(norm=5700),
        obs.Time(),
    ]
    action_spec = [
        act.ImpulsiveThrustHill(
            chief_name="RSO",
            max_dv=1.0,
            max_drift_duration=5700.0 * 2,
            fsw_action="action_inspect_rso",
        )
    ]
    dyn_type = types.new_class(
        "Dyn",
        (
            dyn.MaxRangeDynModel,
            dyn.ConjunctionDynModel,
            dyn.RSOInspectorDynModel,
        ),
    )
    fsw_type = types.new_class(
        "FSW",
        (
            fsw.SteeringFSWModel,
            fsw.MagicOrbitalManeuverFSWModel,
            fsw.RSOInspectorFSWModel,
        ),
    )

Generous configurations are used for the inspector, allowing for “sloppy” attitude control with a low simulation step rate.

[7]:
inspector_sat_args = dict(
    imageAttErrorRequirement=1.0,
    imageRateErrorRequirement=None,
    instrumentBaudRate=1,
    dataStorageCapacity=1e6,
    batteryStorageCapacity=1e9,
    storedCharge_Init=1e9,
    conjunction_radius=2.0,
    dv_available_init=10.0,
    max_range_radius=1000,
    chief_name="RSO",
    u_max=1.0,
)

Environment Generation

A satellite argument randomizer is defined to configure the initial state of the satellites. The RSO is put into a random orbit with an apogee and perigee between 500 km and 1100 km. The inspector is placed in the region 250 to 750 meters from the RSO, with up to 1 m/s of relative velocity. Finally, the RSO’s attitude and body rate are set up to be in the nadir-pointing initial configuration.

[8]:
def sat_arg_randomizer(satellites):
    # Generate the RSO orbit
    R_E = 6371.0  # km
    a = R_E + np.random.uniform(500, 1100)
    e = np.random.uniform(0.0, min(1 - (R_E + 500) / a, (R_E + 1100) / a - 1))
    chief_orbit = random_orbit(a=a, e=e)

    inspectors = [sat for sat in satellites if "Inspector" in sat.name]
    rso = [satellite for satellite in satellites if satellite.name == "RSO"][0]

    # Generate the inspector initial states.
    args = {}
    for inspector in inspectors:
        relative_randomizer = relative_to_chief(
            chief_name="RSO",
            chief_orbit=chief_orbit,
            deputy_relative_state={
                inspector.name: lambda: np.concatenate(
                    (
                        random_unit_vector() * np.random.uniform(250, 750),
                        random_unit_vector() * np.random.uniform(0, 1.0),
                    )
                ),
            },
        )
        args.update(relative_randomizer([rso, inspector]))

    # Align RSO Hill frame for initial nadir pointing
    mu = rso.sat_args_generator["mu"]
    r_N, v_N = elem2rv(mu, args[rso]["oe"])

    r_hat = r_N / np.linalg.norm(r_N)
    v_hat = v_N / np.linalg.norm(v_N)
    x = r_hat
    z = np.cross(r_hat, v_hat)
    z = z / np.linalg.norm(z)
    y = np.cross(z, x)
    HN = np.array([x, y, z])
    BH = np.eye(3)

    a = chief_orbit.a
    T = np.sqrt(a**3 / mu) * 2 * np.pi
    omega_BN_N = z * 2 * np.pi / T

    args[rso]["sigma_init"] = C2MRP(BH @ HN)
    args[rso]["omega_init"] = BH @ HN @ omega_BN_N

    return args

The scenario is configured to set the RSO geometry as a sphere with 100 points at a radius of 1 meter. Points must be imaged within 30 degrees of their normal, with illumination coming from no more than 60 degrees from normal. The inspector must be within 250 meters to inspect the RSO.

[9]:
scenario = scene.SphericalRSO(
    n_points=100,
    radius=1.0,
    theta_max=np.radians(30),
    range_max=250,
    theta_solar_max=np.radians(60),
)

This scenario uses two rewarders. For the RSO inspection component of the task, a bonus of 1.0 is yielded once at least 90% of the illuminated points have been inspected. The ResourceReward is used to penalize fuel use, with some basic logic add to only apply the reward to the Inspector.

[10]:
rewarders = (
    data.RSOInspectionReward(
        completion_bonus=1.0,
        completion_threshold=0.90,
    ),
    data.ResourceReward(
        resource_fn=lambda sat: sat.fsw.dv_available
        if isinstance(sat.fsw, fsw.MagicOrbitalManeuverFSWModel)
        else 0.0,
        reward_weight=np.random.uniform(0.0, 0.5),
    ),
)

With all the components defined, the environment can be instantiated.

[11]:
env = ConstellationTasking(
    satellites=[
        RSOSat("RSO", sat_args=rso_sat_args),
        InspectorSat("Inspector", sat_args=inspector_sat_args, obs_type=dict),
    ],
    sat_arg_randomizer=sat_arg_randomizer,
    scenario=scenario,
    rewarder=rewarders,
    time_limit=60000,
    sim_rate=5.0,
    log_level="INFO",
)

Environment Interaction

The environment is reset and randomly stepped through.

Future Work: This example will be updated with an actual trained policy in the future.

[12]:
env.reset()
for i in range(4):
    env.step(dict(RSO=0, Inspector=env.action_space("Inspector").sample()))
2025-06-20 19:57:33,368 gym                            INFO       Resetting environment with seed=3914517442
2025-06-20 19:57:33,499 gym                            INFO       <0.00> Environment reset
/opt/hostedtoolcache/Python/3.11.12/x64/lib/python3.11/site-packages/gymnasium/spaces/box.py:130: UserWarning: WARN: Box bound precision lowered by casting to float32
  gym.logger.warn(f"Box bound precision lowered by casting to {self.dtype}")
2025-06-20 19:57:33,501 gym                            INFO       <0.00> === STARTING STEP ===
2025-06-20 19:57:33,502 sats.satellite.RSO             INFO       <0.00> RSO: action_downlink tasked for 1000000000.0 seconds
2025-06-20 19:57:33,502 sats.satellite.RSO             INFO       <0.00> RSO: setting timed terminal event at 1000000000.0
2025-06-20 19:57:33,504 sats.satellite.Inspector       INFO       <0.00> Inspector: Thrusting with inertial dV [-0.07623727  0.53327311  0.16486687] with 3164.70849609375 second drift.
2025-06-20 19:57:33,505 sats.satellite.Inspector       INFO       <0.00> Inspector: setting timed terminal event at 3164.7
2025-06-20 19:57:33,506 sats.satellite.Inspector       INFO       <0.00> Inspector: FSW action action_inspect_rso activated.
2025-06-20 19:57:33,540 sats.satellite.Inspector       INFO       <700.00> Inspector: Exceeded maximum range of 1000 m from RSO
2025-06-20 19:57:33,602 data.composition               INFO       <700.00> ResourceReward reward: {'Inspector': np.float64(-0.2741748690734543)}
2025-06-20 19:57:33,602 data.base                      INFO       <700.00> Total reward: {'Inspector': np.float64(-0.2741748690734543)}
2025-06-20 19:57:33,604 sats.satellite.Inspector       WARNING    <700.00> Inspector: failed range_valid check
2025-06-20 19:57:33,608 gym                            INFO       <700.00> Step reward: {'Inspector': np.float64(-1.2741748690734542)}
2025-06-20 19:57:33,609 gym                            INFO       <700.00> Episode terminated: ['Inspector']
2025-06-20 19:57:33,610 gym                            INFO       <700.00> === STARTING STEP ===
2025-06-20 19:57:33,611 sats.satellite.RSO             INFO       <700.00> RSO: action_downlink tasked for 1000000000.0 seconds
2025-06-20 19:57:33,611 sats.satellite.RSO             INFO       <700.00> RSO: setting timed terminal event at 1000000700.0
2025-06-20 19:57:33,613 sats.satellite.Inspector       INFO       <700.00> Inspector: Thrust clamped from 1.0111019522397129 m/s to 1.0 m/s.
2025-06-20 19:57:33,613 sats.satellite.Inspector       INFO       <700.00> Inspector: Thrusting with inertial dV [-0.21772064  0.69484437 -0.68541157] with 3909.960205078125 second drift.
2025-06-20 19:57:33,614 sats.satellite.Inspector       INFO       <700.00> Inspector: setting timed terminal event at 4610.0
2025-06-20 19:57:33,615 sats.satellite.Inspector       INFO       <700.00> Inspector: FSW action action_inspect_rso activated.
2025-06-20 19:57:33,812 sats.satellite.Inspector       INFO       <4610.00> Inspector: timed termination at 4610.0
2025-06-20 19:57:34,120 data.composition               INFO       <4610.00> ResourceReward reward: {'Inspector': np.float64(-0.4866787352516483)}
2025-06-20 19:57:34,120 data.base                      INFO       <4610.00> Total reward: {'Inspector': np.float64(-0.4866787352516483)}
2025-06-20 19:57:34,121 sats.satellite.Inspector       INFO       <4610.00> Inspector: Satellite Inspector requires retasking
2025-06-20 19:57:34,123 gym                            INFO       <4610.00> Step reward: {}
2025-06-20 19:57:34,124 gym                            INFO       <4610.00> === STARTING STEP ===
2025-06-20 19:57:34,125 sats.satellite.RSO             INFO       <4610.00> RSO: action_downlink tasked for 1000000000.0 seconds
2025-06-20 19:57:34,125 sats.satellite.RSO             INFO       <4610.00> RSO: setting timed terminal event at 1000004610.0
2025-06-20 19:57:34,126 sats.satellite.Inspector       INFO       <4610.00> Inspector: Thrust clamped from 1.338736194546819 m/s to 1.0 m/s.
2025-06-20 19:57:34,127 sats.satellite.Inspector       INFO       <4610.00> Inspector: Thrusting with inertial dV [ 0.94238262 -0.17252083  0.28662092] with 5453.11279296875 second drift.
2025-06-20 19:57:34,128 sats.satellite.Inspector       INFO       <4610.00> Inspector: setting timed terminal event at 10063.1
2025-06-20 19:57:34,128 sats.satellite.Inspector       INFO       <4610.00> Inspector: FSW action action_inspect_rso activated.
2025-06-20 19:57:34,378 sats.satellite.Inspector       INFO       <10065.00> Inspector: timed termination at 10063.1
2025-06-20 19:57:34,803 data.composition               INFO       <10065.00> ResourceReward reward: {'Inspector': np.float64(-0.4866787352516483)}
2025-06-20 19:57:34,804 data.base                      INFO       <10065.00> Total reward: {'Inspector': np.float64(-0.4866787352516483)}
2025-06-20 19:57:34,804 sats.satellite.Inspector       INFO       <10065.00> Inspector: Satellite Inspector requires retasking
2025-06-20 19:57:34,806 gym                            INFO       <10065.00> Step reward: {}
2025-06-20 19:57:34,807 gym                            INFO       <10065.00> === STARTING STEP ===
2025-06-20 19:57:34,808 sats.satellite.RSO             INFO       <10065.00> RSO: action_downlink tasked for 1000000000.0 seconds
2025-06-20 19:57:34,808 sats.satellite.RSO             INFO       <10065.00> RSO: setting timed terminal event at 1000010065.0
2025-06-20 19:57:34,810 sats.satellite.Inspector       INFO       <10065.00> Inspector: Thrusting with inertial dV [-0.45133711  0.49408398  0.37333138] with 3052.97607421875 second drift.
2025-06-20 19:57:34,811 sats.satellite.Inspector       INFO       <10065.00> Inspector: setting timed terminal event at 13118.0
2025-06-20 19:57:34,812 sats.satellite.Inspector       INFO       <10065.00> Inspector: FSW action action_inspect_rso activated.
2025-06-20 19:57:34,957 sats.satellite.Inspector       INFO       <13120.00> Inspector: timed termination at 13118.0
2025-06-20 19:57:35,197 data.composition               INFO       <13120.00> ResourceReward reward: {'Inspector': np.float64(-0.3729370883928985)}
2025-06-20 19:57:35,198 data.base                      INFO       <13120.00> Total reward: {'Inspector': np.float64(-0.3729370883928985)}
2025-06-20 19:57:35,199 sats.satellite.Inspector       INFO       <13120.00> Inspector: Satellite Inspector requires retasking
2025-06-20 19:57:35,200 gym                            INFO       <13120.00> Step reward: {}