In this we have studied survey of how NASA build their first Machine Learning enabled Rover to send it on Mars. Hope you Like it! If any improvements or copyright content removal needed feel free to communicate.

1. CASE STUDY PRESENTATION
Rajiv Gandhi College of Engineering Research and Technology ,Chandrapur , Maharashtra
Dr. Babasaheb Ambedkar Technological University, Lonere, Raigad
TOPIC : MACHINE LEARNING IN SELF AUTOMATED ROVERS
Group No: B-18 Under Guidance of:
Rutik Bhoyar Prof. Rupa Lichode
Ketan Singh Department of Computer
Ritik Viaragde Science and Engineering

2. TABLE OF CONTENTS
1) Abstract
2) Introduction
3) Scientists Opinion
4) Data Processing and Data Visualization
5) Feature Target Selection
6) Image Prioritization
7) Performance Verification and Validation
8) Onboard Technology Integration
9) Acknowledgement

3. ABSTRACT
Many machine learning models are nowadays useful to mankind. But how about implementing a
successful machine learning model in self automated rovers going to Mars where there is no limit
to data.
Machine learning is all about feeding data, how these rovers generated data and performed
onboard data analysis that helped the scientist to translate science goal into clear tasks which were
accomplished by algorithms.
In this paper we will describe what were the technical challenges, execution of verification and
validation plans, handling of huge amounts of data as limited downlink bandwidth and
communication time delay with earth was concerned, for onboard analysis in self automated
rovers.

4. INTRODUCTION
Two Fundamental Constraints:
1) Limited Downlink Bandwidth
2) Communication Time Delay Between Earth and Spacecraft
Opportunistic Science:
The capability of opportunistic science enables a rover to perform data collects, when an
interesting science target is encountered without having to wait for a command from Earth.
Keys to achieve this objective:
1)first identify a metric of increased science return that the scientists will support.
2) then have to develop algorithms that achieve this increase in science return.
3) we formulate a plan to integrate the technology into the mission at a risk level acceptable

5. SCIENTIST’S LOOKOUT
The scientist’s data gathering options include
1) taking no science data during the traverse,
2) collecting data at a fixed time interval, or fixed distance interval;
3) or allowing the rover to make intelligent decisions about what data to gather and transmit.
JPL’s (OASIS) team identified three classes of data evaluation criterion-
1) Researchers were interested in identifying the existence of certain pre-specified signals of
scientific interval
2) The identification of unexpected or anomalous features, as these can lead to new scientific
discoveries
3) to capture a description of the typical characteristics of a region.

6. DATA PROCESSING AND DISTRIBUTION
PTeP (Parallel Telementary Processor) tool: Used to process downlink data and produce resulting data products in the
database.
EDR(Engineering Data Records): Data products sent from the rover , Ex. Stereo image pair
RDR(Reduced Data Records): The resulting data products that PTeP generated from EDRs
Two primary browsers: The Downlink Browser and the Uplink Browser
1)Downlink Data Generation:
 The downlink browser was used to select and view downlink data products.
 Just like a web browser have a list of book marks on the left side of the window and the remaining space was
for viewing a webpage,
 the downlink browser arranges links to data products in a tree on the left and create a view on the right when a
link selected.
 It had three main attributes: 1) Select and open a data product 2) Configure the downlink browser 3) View
Types

10. Uplink Plan Generation:
 Uplink browsers was used for these purposes:
 1) Activity plan Editing
 2) Activity Glyphs, Simulation and Resource Modelling.
Activity plans consist of targets, observation, and activities and are stored in the Rover Markup
Language (RML) format, which was based on XML.

13. FEATURE AND TARGET SELECTION
Terrain Target: Targets are the 3D locations on the terrain that are selected from stereo image pairs that come from
the Hazecam, Navecam, Pancam instruments.
Feature: Features are also 3D locations on the terrain, but they are not used as parameters in activities features
represents objects in the terrain.
Geology and rocks in scene were focused in onboard analysis to extract features.
 Stereo Image Pair
 Height Image
Properties Extracted from Rocks Identified
 albedo, which indicates the reflectance properties of a surface, by computing the average grayscale value of the
pixels that comprise the image of the rock.
 Visual texture described by intensity variations at different orientations and spatial frequencies within the image.

14. IMAGE PRIORITIZATION
1)Key-Target Signature:
They specified the target signature for a rock of interest as a feature vector whose values were provided
by the scientists.
2)Novelty Detection:
Three methods for detecting and prioritizing novel rocks, representing the three dominant flavors of
machine learning approaches to novelty detection:
1)Distance-based,
2)Probability-based (i.e. "generative"),
3)Discriminative.
These methods for novelty detection were applicable to a variety of novelty detection tasks.
3)Representative Sampling:
In sampling algorithm, the rocks were clustered into groups based on their feature vectors using K-
means

15. PERFORMANCE VERIFICATION AND VALIDATION
Performance verification: Involves ensuring that the algorithm implementation is working
correctly under the specified operating conditions. The implementation must first correctly execute
the specified algorithm, i.e. no bugs, and then the algorithm must perform the desired operations
under claimed circumstances.
Performance Validation: The primary concern in the project was to develop technique for
validating the results of their autonomous prioritization algorithms.
They validated the design by comparing the results of prioritizing data using algorithms to
scientists’ prioritizations of the same data.

16. ONBOARD TECHNOLOGY INTEGRATION
1)Optimal time for introducing machine learning capability in the design process
2)Demonstrate the operational capability of the software.
It had took various steps to get machine learning model fly on the software back then.
And as result scientists are now able to study the Red Planet with very new approach.

17. Acknowledgement
We would like to thank our mentor Prof. Rupa Lichode, Professor, Department of
Computer Science Engineering. We would also like to acknowledge the hard work of
the NASA team for implementing such a helpful project for the Human World.

18. REFERENCES
 Chien, S., et al., “Autonomous Science on the EO-1 Mission”, iSAIRAS, Nara,
Japan, May 2003
 R. Castano, M. Judd, R. C. Anderson, T. Estlin., Machine Learning Challenges
in Mars Rover Traverse Science. International Conference on Machine
Learning, August, 2003.
 R. Castano, R. C. Anderson, T. Estlin, D. DeCoste, F. Fisher, D. Gaines, D.
Mazzoni, M. Judd., Rover Traverse Science for Increased Mission Science
Return. Proceedings of the 2003 IEEE Aerospace Conference, March, 2003.
 Machine Learning for Space Projects: Example Engineering and Science Case
Studies Hamed Valizadegan Data Science Group @ NASAAmes Research Center
(All the data , information, images used in this presentation are subjected to NASA Jet
Propulsion Laboratory)