Revisiting the Mars Surveyor Challenge Andrew Stubblebine, Tejas Deshpande, Dr. Elad Kivelevitch

University of Cincinnati, College of Engineering and Applied Science

Introduction Simulation

Conclusion and Future Work

1999 MATLAB competition – Mars Surveyor Challenge

Develop most effective solution to guide rovers through a map

Motivation:

UAV search and rescue missions are very common

Ability to search an area faster could save lives

Methods explored would increase search efficiency, especially

multiple vehicles

Greater search efficiency means search time reduced

Original Challenge Conditions

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Algorithm Selection 2 approaches based on lit. survey and performance speculation

Rule-Based Algorithm:

4 New Maps:

Genetic Algorithm:

4 New Maps:

𝑆𝑜𝑙𝑢𝑡𝑖𝑜𝑛 𝑆𝑡𝑟𝑒𝑛𝑔𝑡ℎ = 𝑆𝑝𝑎𝑐𝑒𝑠 𝑀𝑖𝑠𝑠𝑒𝑑, 𝐴𝑙𝑔𝑜𝑟𝑖𝑡ℎ𝑚 − 𝑆𝑝𝑎𝑐𝑒𝑠 𝑀𝑖𝑠𝑠𝑒𝑑, 𝐵𝑒𝑠𝑡

𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑂𝑝𝑒𝑛 𝑆𝑝𝑎𝑐𝑒𝑠

Evaluates ability of algorithms to find efficient solution

Based on winning comp solution (considered best)

Lower % is better

Competition winner hard-coded to maps given by MathWorks

Competition solution breaks down when required to work with

different maps and/or additional rovers

4 additional maps added to further test new algorithms vs. comp.

soln.

Rule-Based Algorithm Genetic Algorithm

Pros:

Simple, easily scalable

Fast computation time

Gets decent results

Pros:

Works on any map

Theoretically get best solution

Theoretically scalable

Cons:

Limited visibility area

Only local decisions made

Can overwhelm rovers on

difficult maps

Cons:

Computationally intensive

Conclusions

Rule-Based Algorithm: Genetic Algorithm:

Rovers behave as expected

Produced results nearly as good

as competition solutions

7/12 maps strength < 5%

Computationally intensive

Given more time, can result in

better solutions

Improvements To Algorithms For Future Work

Explore “back-up” algorithm in

competition solution

Implement fuzzy logic

Incorporate path “back-tracking”

Implement computationally

efficient guided mutation

Combine rule-based approach

with GA

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Algorithm vs. Contest Solution Strength

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Original Maps (1-8) New Maps (9-12) All Maps (1-12)

Total Solution Strength

Total Solution Strength

Rank #20 Soln

Rank #50 Soln

Rank #100 Soln

Rule-Based Soln

GA - 100

GA - 300