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Issue #0: AEOS example environment
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‎docs/build/doctrees/nbsphinx/examples/aeos.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Agile Earth-Observing Satellite Environment\n",
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"\n",
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"This example demonstrates the environment configuration for a power-free and power-constrained agile Earth-observing satellite. These environments reflect the configuration and values from an upcoming journal paper."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from Basilisk.architecture import bskLogging\n",
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"from Basilisk.utilities import orbitalMotion\n",
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"\n",
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"from bsk_rl import SatelliteTasking, act, data, obs, sats, scene\n",
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"from bsk_rl.sim import fsw\n",
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"from bsk_rl.utils.orbital import random_orbit, rv2HN\n",
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"\n",
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"bskLogging.setDefaultLogLevel(bskLogging.BSK_WARNING)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Power-Free Environment\n",
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"\n",
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"First, a function for generating satellite types is introduced. This function can generate one of three different observation types, and can choose to include the time through episode in the observation."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"def satellite_generator(observation, n_ahead=32, include_time=False):\n",
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" \"\"\"_summary_\n",
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"\n",
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" Args:\n",
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" observation: Pick from \"S1\", \"S2\", \"S3\"\n",
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" n_ahead: Number of requests to include in the observation and action spaces\n",
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" include_time: Whether to include time through episode in the observation\n",
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" \"\"\"\n",
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"\n",
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" assert observation in [\"S1\", \"S2\", \"S3\"]\n",
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"\n",
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" class CustomSatellite(sats.ImagingSatellite):\n",
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" action_spec = [act.Image(n_ahead_image=n_ahead)]\n",
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" if observation == \"S1\":\n",
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" observation_spec = [\n",
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" obs.SatProperties(\n",
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" dict(prop=\"omega_BP_P\", norm=0.03),\n",
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" dict(prop=\"c_hat_P\"),\n",
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" dict(prop=\"r_BN_P\", norm=orbitalMotion.REQ_EARTH * 1e3),\n",
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" dict(prop=\"v_BN_P\", norm=7616.5),\n",
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" ),\n",
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" obs.OpportunityProperties(\n",
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" dict(prop=\"priority\"),\n",
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" dict(prop=\"r_LP_P\", norm=orbitalMotion.REQ_EARTH * 1e3),\n",
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" type=\"target\",\n",
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" n_ahead_observe=n_ahead,\n",
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" ),\n",
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" ]\n",
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" elif observation == \"S2\":\n",
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" observation_spec = [\n",
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" obs.SatProperties(\n",
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" dict(prop=\"omega_BH_H\", norm=0.03),\n",
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" dict(prop=\"c_hat_H\"),\n",
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" dict(prop=\"r_BN_P\", norm=orbitalMotion.REQ_EARTH * 1e3),\n",
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" dict(prop=\"v_BN_P\", norm=7616.5),\n",
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" ),\n",
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" obs.OpportunityProperties(\n",
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" dict(prop=\"priority\"),\n",
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" dict(prop=\"r_LB_H\", norm=orbitalMotion.REQ_EARTH * 1e3),\n",
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" type=\"target\",\n",
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" n_ahead_observe=n_ahead,\n",
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" ),\n",
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" ]\n",
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" elif observation == \"S3\":\n",
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" observation_spec = [\n",
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" obs.SatProperties(\n",
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" dict(prop=\"omega_BH_H\", norm=0.03),\n",
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" dict(prop=\"c_hat_H\"),\n",
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" dict(prop=\"r_BN_P\", norm=orbitalMotion.REQ_EARTH * 1e3),\n",
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" dict(prop=\"v_BN_P\", norm=7616.5),\n",
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" ),\n",
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" obs.OpportunityProperties(\n",
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" dict(prop=\"priority\"),\n",
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" dict(prop=\"r_LB_H\", norm=800 * 1e3),\n",
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" dict(prop=\"target_angle\", norm=np.pi / 2),\n",
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" dict(prop=\"target_angle_rate\", norm=0.03),\n",
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" dict(prop=\"opportunity_open\", norm=300.0),\n",
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" dict(prop=\"opportunity_close\", norm=300.0),\n",
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" type=\"target\",\n",
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" n_ahead_observe=n_ahead,\n",
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" ),\n",
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" ]\n",
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"\n",
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" if include_time:\n",
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" observation_spec.append(obs.Time())\n",
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" fsw_type = fsw.SteeringImagerFSWModel\n",
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"\n",
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" return CustomSatellite"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Next, the parameters for the satellite are defined."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"SAT_ARGS = dict(\n",
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" imageAttErrorRequirement=0.01,\n",
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" imageRateErrorRequirement=0.01,\n",
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" batteryStorageCapacity=80.0 * 3600 * 100,\n",
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" storedCharge_Init=80.0 * 3600 * 100.0,\n",
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" dataStorageCapacity=200 * 8e6 * 100,\n",
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" u_max=0.4,\n",
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" imageTargetMinimumElevation=np.arctan(800 / 500),\n",
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" K1=0.25,\n",
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" K3=3.0,\n",
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" omega_max=np.radians(5),\n",
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" servo_Ki=5.0,\n",
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" servo_P=150 / 5,\n",
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" oe=lambda: random_orbit(alt=800),\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Finally, the environment can be initialized."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"duration = 5700.0 * 5 # 5 orbits\n",
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"target_distribution = \"uniform\"\n",
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"n_targets = 3000\n",
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"n_ahead = 32\n",
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"\n",
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"if target_distribution == \"uniform\":\n",
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" targets = scene.UniformTargets(n_targets)\n",
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"elif target_distribution == \"cities\":\n",
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" targets = scene.CityTargets(n_targets)\n",
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"\n",
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"env = SatelliteTasking(\n",
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" satellite=satellite_generator(observation=\"S3\", n_ahead=32, include_time=False)(\n",
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" name=\"EO1\",\n",
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" sat_args=SAT_ARGS,\n",
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" ),\n",
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" scenario=targets,\n",
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" rewarder=data.UniqueImageReward(),\n",
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" sim_rate=0.5,\n",
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" max_step_duration=300.0,\n",
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" time_limit=duration,\n",
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" failure_penalty=0.0,\n",
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" terminate_on_time_limit=True,\n",
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" log_level=\"INFO\",\n",
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")\n",
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"_ = env.reset()\n",
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"for i in range(5):\n",
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" env.step(env.action_space.sample())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Power-Constrained Environment\n",
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"\n",
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"The power-constrained environment is like the power-free environment, but with an additional battery management requirement. The satellite has additional observation elements to be able to account for power.\n",
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"\n",
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"First, the upcoming reward density observation is defined."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"class Density(obs.Observation):\n",
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" def __init__(\n",
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" self,\n",
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" interval_duration=60 * 3,\n",
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" intervals=10,\n",
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" norm=3,\n",
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" ):\n",
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" self.satellite: \"sats.AccessSatellite\"\n",
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" super().__init__()\n",
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" self.interval_duration = interval_duration\n",
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" self.intervals = intervals\n",
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" self.norm = norm\n",
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"\n",
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" def get_obs(self):\n",
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" if self.intervals == 0:\n",
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" return []\n",
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"\n",
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" self.satellite.calculate_additional_windows(\n",
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" self.simulator.sim_time\n",
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" + (self.intervals + 1) * self.interval_duration\n",
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" - self.satellite.window_calculation_time\n",
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" )\n",
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" soonest = self.satellite.upcoming_opportunities_dict(types=\"target\")\n",
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" rewards = np.array([opportunity.priority for opportunity in soonest])\n",
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" times = np.array([opportunities[0][1] for opportunities in soonest.values()])\n",
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" time_bins = np.floor((times - self.simulator.sim_time) / self.interval_duration)\n",
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" densities = [sum(rewards[time_bins == i]) for i in range(self.intervals)]\n",
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" return np.array(densities) / self.norm"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The satellite generator function is then defined, along with some additional observations."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"def wheel_speed_3(sat):\n",
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" return np.array(sat.dynamics.wheel_speeds[0:3]) / 630\n",
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"\n",
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"\n",
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"def s_hat_H(sat):\n",
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" r_SN_N = (\n",
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" sat.simulator.world.gravFactory.spiceObject.planetStateOutMsgs[\n",
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" sat.simulator.world.sun_index\n",
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" ]\n",
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" .read()\n",
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" .PositionVector\n",
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" )\n",
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" r_BN_N = sat.dynamics.r_BN_N\n",
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" r_SB_N = np.array(r_SN_N) - np.array(r_BN_N)\n",
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" r_SB_H = rv2HN(r_BN_N, sat.dynamics.v_BN_N) @ r_SB_N\n",
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" return r_SB_H / np.linalg.norm(r_SB_H)\n",
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"\n",
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"\n",
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"def power_sat_generator(n_ahead=32, include_time=False):\n",
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" class PowerSat(sats.ImagingSatellite):\n",
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" action_spec = [act.Image(n_ahead_image=n_ahead), act.Charge()]\n",
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" observation_spec = [\n",
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" obs.SatProperties(\n",
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" dict(prop=\"omega_BH_H\", norm=0.03),\n",
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" dict(prop=\"c_hat_H\"),\n",
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" dict(prop=\"r_BN_P\", norm=orbitalMotion.REQ_EARTH * 1e3),\n",
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" dict(prop=\"v_BN_P\", norm=7616.5),\n",
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" dict(prop=\"battery_charge_fraction\"),\n",
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" dict(prop=\"wheel_speed_3\", fn=wheel_speed_3),\n",
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" dict(prop=\"s_hat_H\", fn=s_hat_H),\n",
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" ),\n",
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" obs.OpportunityProperties(\n",
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" dict(prop=\"priority\"),\n",
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" dict(prop=\"r_LB_H\", norm=800 * 1e3),\n",
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" dict(prop=\"target_angle\", norm=np.pi / 2),\n",
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" dict(prop=\"target_angle_rate\", norm=0.03),\n",
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" dict(prop=\"opportunity_open\", norm=300.0),\n",
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" dict(prop=\"opportunity_close\", norm=300.0),\n",
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" type=\"target\",\n",
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" n_ahead_observe=n_ahead,\n",
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" ),\n",
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" obs.Eclipse(norm=5700),\n",
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" Density(intervals=20, norm=5),\n",
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" ]\n",
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"\n",
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" if include_time:\n",
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" observation_spec.append(obs.Time())\n",
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"\n",
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" fsw_type = fsw.SteeringImagerFSWModel\n",
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"\n",
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" return PowerSat"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Satellite parameters are also modified for the power-constrained environment."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"SAT_ARGS_POWER = {}\n",
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"SAT_ARGS_POWER.update(SAT_ARGS)\n",
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"SAT_ARGS_POWER.update(\n",
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" dict(\n",
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" batteryStorageCapacity=120.0 * 3600,\n",
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" storedCharge_Init=lambda: 120.0 * 3600 * np.random.uniform(0.4, 1.0),\n",
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" rwBasePower=20.4,\n",
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" instrumentPowerDraw=-10,\n",
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" thrusterPowerDraw=-30,\n",
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" nHat_B=np.array([0, 0, -1]),\n",
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" wheelSpeeds=lambda: np.random.uniform(-2000, 2000, 3),\n",
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" desatAttitude=\"nadir\",\n",
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" )\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Finally, the environment can be initialized."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"duration = 5700.0 * 5 # 5 orbits\n",
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"target_distribution = \"uniform\"\n",
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"n_targets = 3000\n",
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"n_ahead = 32\n",
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"\n",
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"if target_distribution == \"uniform\":\n",
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" targets = scene.UniformTargets(n_targets)\n",
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"elif target_distribution == \"cities\":\n",
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" targets = scene.CityTargets(n_targets)\n",
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"\n",
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"env = SatelliteTasking(\n",
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" satellite=power_sat_generator(n_ahead=32, include_time=False)(\n",
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" name=\"EO1-power\",\n",
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" sat_args=SAT_ARGS_POWER,\n",
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" ),\n",
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" scenario=targets,\n",
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" rewarder=data.UniqueImageReward(),\n",
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" sim_rate=0.5,\n",
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" max_step_duration=300.0,\n",
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" time_limit=duration,\n",
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" failure_penalty=0.0,\n",
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" terminate_on_time_limit=True,\n",
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" log_level=\"INFO\",\n",
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")\n",
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"_ = env.reset()\n",
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"for i in range(5):\n",
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" env.step(env.action_space.sample())"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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