AI in Space Exploration

1. AI in Space Exploration Stephen Dabideen Yizenia Mora

2. Agenda • Planning and Scheduling (CASPER) • Autonomous Navigation (AutoNav) • Communications with Earth (Beacon) • Autonomous Onboard Science (ASE & OASIS) • Data Mining (SKICAT)

3. Autonomous Navigation (AutoNav) • What is AutoNav? • Autonomous Optical Navigation system uses an expert-system-like architecture to guide a spacecraft to its target, first used in DS1 • Enables a spacecraft to navigate independently of ground teams and ground links • It commands the ion propulsion system and the spacecraft's altitude control system to change trajectory as needed • AutoNav also determines how much power to devote to the ion propulsion system • Use location to determine how much energy generated by solar array • Intended to be reusable

4. Autonomous Navigation (AutoNav) • Subsystems & functions: • Navigation executive function • Controls all AutoNav operations that cause physical action by spacecraft. • Optimizes time utilization by planning turn sequences • Image processing • Integrates camera and imaging spectrometer to take pictures of asteroids and stars, to determine its location • 0.1 pixel accuracy • Orbit determination • Uses a batch-sequential modified Kalman filter to compute the spacecraft’s position • Maneuver planning • Use OD to compute updates to upcoming trust plan.

5. Communications with Earth (Beacon) • Spacecraft determines when ground support is needed and what information is relevant • Advantages: • Reduces costs of the spacecraft-to-ground link • Downlinks only pertinent information

6. Communications with Earth (Beacon) • Two subsystems • Subsystem 1: • End-to-end tone system to inform the ground whether data needs to be sent • One of four possible requests (no action required, contact when convenient, contact within a certain time, or contact immediately) • Subsystem 2: • Produce intelligent data summaries to be downlinked as telemetry when ground responds to tone request • Four types of engineering telemetry • High-level spacecraft information since the last ground contact • Episode data • Snapshot telemetry • Performance data • ELMER used to detect anomalies

7. Communications with Earth (Beacon)Detecting Anomalies (ELMER) • Traditional thresholds • Static, manually predefined red lines • A lot of false alarms • ELMER (Envelope Learning and Monitoring using Error Relaxation) • Time-varying alarm thresholds • Neural networks • Trained with nominal sensor data • High- and low-expectation bounds (envelopes)

8. Autonomous On-Board Science • The dream: • An autonomous Mars rover traversing the planets surface for a couple of years, unattended by humans, collecting and catching samples

9. The dream: An autonomous Mars rover traversing the planets surface for a couple of years, unattended by humans, collecting and catching samples Reality check: Spirit and Opportunity 4 drivers per rover About 20 simulations per move Remote-controlled over 150 million miles away Opportunity's farthest distance to date: 15 m Autonomous On-Board Science

10. Autonomous On-Board Science • Need for automated science: • Slim window of opportunity for discovery • Autonomy can provide more reactive, flexible architecture to respond to unanticipated events • Limited downlink bandwidth • Time delay • Accomplishments thus far: • New method analyzing visible broadband images using neural networks • Important features extracted and combined with spectral classifications and decisions are made using a decision tree directed towards specific goals • Analysis of spectral data • Hierarchy of neural nets place spectra into progressively more detailed geologic classes • Decompose mixtures from unknown spectra • Will help automate characterization of planetary surface

11. Autonomous On-Board Science (ASE) • The Autonomous Sciencecraft Experiment (ASE) • Used on Earth Observing One (EO -1) • Demonstrates integrated autonomous science • Features several science algorithms including: • Event detection • Feature detection • Change detection • Analyzes to detect trigger conditions such as science events • Based on these observations CASPER will replan • Science analysis techniques include: • Thermal anomaly detection • Cloud detection • Flood scene classification

12. Autonomous On-Board Science (OASIS) • Onboard Autonomous Science investigation System • Due to limited bandwidth, rovers must “intelligently” select what data to transmit back to Earth • How? • Machine leaning techniques to prioritize data • The capability of OASIS enables a rover to perform data collections which were not originally planned, even without having to wait for a command from Earth • Researchers are interested in: • Pre-specified signals of scientific interest • Unexpected or anomalous features • Typical characteristics of a region • OASIS has different levels of autonomy, from following a predefined path and taking only planned measurements to commanding the rover to deviate slightly from path to get new measurements

13. Data Mining (SKICAT) • What? • SKy-Image Cataloging and Analysis Tool • Assign galaxies and stars to known classes and identify new classes • Why? • Databases are too large for an astronomer to analyze manually • How? • Automated Bayesian classification • Attributes such as brightness, area, color, morphology, … • Training data consisting of astronomer-classified sky objects • Classifiers applied to new survey images

14. Data Mining (SKICAT) • Results: • One of the most outstanding successes • 1,000-10,000 times faster than astronomers • More consistent classification • Able to classify extremely faint objects • Astronomers freed for more challenging analysis and interpretation • Comprehensive catalog of approximately 3 billion entries