# Presentation: 336170

## Intelligent SystemsAutonomy for Space Science

- Dr. Butler Hine
- Deputy Manager
- Intelligent Systems Program

## Why Autonomy?

- In order to accomplish the next generation of challenging missions, NASA must develop highly autonomous systems capable of making critical decisions independently of human operators, as well as mixed-initiative ground-based systems, that work in collaboration with humans to support missions.

## Classes of Missions

| Class | Distance | Comm | Timescale | Autonomy |
| --- | --- | --- | --- | --- |
| Fly-by | Remote | High BW
Continuous | Slow | Pre-programmed |
| Survey | Remote | High BW
Continuous | Slow | Pre-programmed |
| Local Sampling | Static Contact | High BW
Continuous | Slow | Remotely-Operated |
| Local Exploration | Dynamic Contact | Medium BW
Intermittent | Medium | Remotely-Operated |
| Extended Exploration | Dynamic Contact | Low BW
Intermittent | Fast | Goal-Directed |
| Global Exploration | Dynamic Contact | Low BW
Intermittent | Fast | Goal-Directed |

## Task Hierarchy

## Science Autonomy

**Ultimate Goal**: Increase Science Productivity for Science Exploration Missions
**Method**: Develop spacecraft capable of acting at the level of a 1st year graduate student in the relevant science discipline (e.g. planetary geology)
**Technical Requirements**:
- (1) Autonomous Health and Safety Management (e.g. the vehicle keeps itself safe and able to function for long periods of time)
- (2) Autonomous Navigation and Path Planning (e.g. the vehicle is able to go where it needs to, and decide when it can't)
- (3) Autonomous Robust Instrument Operation (e.g. the ability to manipulate, operate, and process data from a science instrument without direct human control)
- (4) Autonomous Field Work (e.g. vehicle is able to operate multiple science instruments at a high level, and process the data)
- (5) On-Board Science Analysis and Understanding (e.g. the vehicle can accept a goal like "characterize this site" and can act on it by generating and testing hypotheses... ultimately building internal models of the environment)

## Autonomous Science

## Technical Driver for Autonomy Requirements:A Europa Sub Mission to Search for Life

## Europa - Possible Internal Structure

## Mission Profile for a Europa Sub

- Europa landing
- Comm/Power infrastructure deployment
- Ice crust penetration
- Initial liquid characterization
- Initial mapping survey
- “Normal” liquid characterization
- Trace signature tracking
- Vent characterization and sampling
- Sample Analysis

## Autonomy Requirements for a Europa Sub

- Continuous Vehicle Health Maintenance
- Mission planning
- Resource scheduling
- Environmental map building
- Tracking and navigation
- Sample collection
- Sample Analysis
- Target-of-opportunity identification

## Two Approaches to Autonomy

- Multi-layer Architectures
- Hierarchical Control
- Deliberative Planning
- Model-based Reasoning
- Complex Computational Requirements
- Highly controllable/complex goals
- Biologically-inspired Approaches
- Highly reactive
- Distributed Control
- Simple Computational Requirements
- Less controllable/simple goals

## Multi-layer Autonomy Architecture

- Deep Space One
- Remote Agent Flight Experiment

## Planning and Execution

## Vehicle Health Maintenance

## Results

## Bio-inspired Approaches to Autonomy

- Emergent Behavior Architectures
- Biomorphic Navigation
- Neurally Inspired Flight Control
- Search Strategies
- Biologically-Inspired State Sensors
- Locomotion Strategies

## Emergent Behavior Architectures

- Complex behaviors emerging from networks of simple behaviors (e.g. Subsumption)
- Emergent behavior from “colonies” of simple agents
- Reactive behaviors adapt rapidly to dynamic environments
- Enhanced failure-mode adaptation

## Biomorphic Navigation

**Honeybee Inspired ****landing, terrain following, gorge following, obstacle avoidance and point-to-point navigation**

**Insects (for example honey bees) cope remarkably well with their world, despite possessing a brain that carries fewer than 0.01% as many neurons as humans.  Although most insects have immobile eyes, fixed focus optics(no range info) and lack stereo vision, they use a number of ingenious strategies for perceiving their world in three dimensions and navigating successfully in it.  Some of these ‘bee’ inspired strategies provide unique solutions to navigation and landing for micro-flyer applications in future missions**

## Neurally Inspired Intelligent Flight Control

**Neurally-inspired intelligent flight control enables real time capability to respond to changes in aircraft stability due to varying weather conditions and make adjustments to maintain the best possible flight performance. Such adaptive controls provide for on-the-fly reconfigurability and self healing capability in flight.**

## Search Strategy for Features of Interest

- CLASS 1

- CLASS 2

- CLASS 3

## Slide 21

## Cellular Nonlinear Network Chips for Retinal imaging

**Biological Origins:**
- Recent biological studies have confirmed that representations of each different characteristic of the visual world is formed in parallel, and embodied in a stack of “strata” in the retina (Hubel and Wiesel,1972, 1977; Roska & Werblin, 2001). Each of these representations can be efficiently modeled in CNN. Many of the biological image processing operations, when translated into CNN image processing operations, constitute algorithmic cornerstones, useful in practical image processing missions.

- Seven of the dozen or so representations of the visual world formed by sophisticated image processing operations in the vertebrate retina. Each representation extracts a different feature from the visual world.

- Incorporation of the local computation capabilities of CNN may
- simplify the overall image processing architecture to deliver
- enhanced autonomous search operation

## Locomotion Strategies

- Sir Arthur

- MAKRO

- Snake

**Behavior-Based**
**Robots**

**Autonomous**
**Robots**

**Sustained**
**Real World**
**Behavior**

**The BEES Approach**

**Compliant Leg Design for **
**Dynamical Behavior**

- Rhex

**Bioinspired Control**

**Mechanical  Design**

## Conclusion

- Autonomous capability will be increasingly important for future NASA missions, as we transition from fly-by/survey science to contact science.
- There are several approaches to autonomy, not all of which require massive processing power.