AI in Space Exploration Stephen Dabideen Yizenia Mora

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

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 Use location to determine how much energy generated by solar array Intended to be reusable

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

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 Reduces costs of the spacecraft-to-ground link Downlinks only pertinent information Downlinks only pertinent information

Communications with Earth (Beacon) Two subsystems Subsystem 1: 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: 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 High-level spacecraft information since the last ground contact Episode data Episode data Snapshot telemetry Snapshot telemetry Performance data Performance data ELMER used to detect anomalies

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

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 An autonomous Mars rover traversing the planets surface for a couple of years, unattended by humans, collecting and catching samples

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 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 4 drivers per rover About 20 simulations per move About 20 simulations per move Remote-controlled over 150 million miles away Remote-controlled over 150 million miles away Opportunity's farthest distance to date: 15 m Opportunity's farthest distance to date: 15 m

Autonomous On-Board Science Need for automated science: Slim window of opportunity for discovery Slim window of opportunity for discovery Autonomy can provide more reactive, flexible architecture to respond to unanticipated events Autonomy can provide more reactive, flexible architecture to respond to unanticipated events Limited downlink bandwidth Limited downlink bandwidth Time delay Time delay Accomplishments thus far: New method analyzing visible broadband images using neural networks 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 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

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

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 Due to limited bandwidth, rovers must “intelligently” select what data to transmit back to Earth How? How? Machine leaning techniques to prioritize data 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 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: Researchers are interested in: Pre-specified signals of scientific interest Pre-specified signals of scientific interest Unexpected or anomalous features Unexpected or anomalous features Typical characteristics of a region 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 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

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

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