Automated Design of Robots for Exploring Extreme Environments of Mars Following an Animal Survivalist Approach

Himangshu Kalita, Jekan Thangavelautham

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The discovery of living organisms under extreme environmental conditions of pressure, temperature, and chemical composition on Earth has opened the possibility of existence and persistence of life in extreme environment pockets across the solar system, including planets such as Mars. The subterranean ecosystem on Earth has remained isolated but has thrived for millions of years. Exploring such environments on Mars can ascertain the range of conditions that can support life and identify planetary processes responsible for generating and sustaining habitable worlds. Over the last few decades, numerous missions started with flyby spacecraft, followed by orbiting satellites and missions with orbiter/lander capabilities. Since then, numerous missions have utilized rovers of ever-increasing size and complexity, equipped with state-of-The-Art laboratories on wheels. Although current generations of rovers have performed exceptionally well, they are designed for predefined tasks and are not suited for exploring the extreme environment pockets of the solar system. In this work, we propose to use machine learning methods to design robotic platforms end to end that is better suited for exploring extreme environments. Our methods show new notions of mobility that combine hopping and running that are better adapted to the low gravity, rugged surface environments. These procedures result in creative ideas that human designers may not have thought. Current design methods use engineering experience and the judgment of a team of experts to identify candidate designs. This process requires significant expertise and experience and is long and expensive in terms of time and labor. The lack of a systematic approach to fully evaluate the whole design space might lead to a sub-optimal solution or, worse, an intractable solution. Machine learning techniques, however, benefit from the exponential rise in computational speed. They can overcome human team limitations, including the number of experts available, time available to perform a design task, and time available to meet and work continuously. We have applied these principles to design rovers suited for rugged environments, walking/hopping robots for low-gravity environments, and hopping/rolling robots to explore underground environments like caves, pits, and lava tubes. Our approach can evolve a next-generation exploration platform that seeks to survive in an alien, resource-starved environment like individual animals surviving for long durations. What better way is there to seek out life on planets like Mars by advancing animal survival strategies to utilizing local resources and shelters.

Original languageEnglish (US)
Title of host publicationIAF Space Exploration Symposium 2021 - Held at the 72nd International Astronautical Congress, IAC 2021
PublisherInternational Astronautical Federation, IAF
ISBN (Electronic)9781713842965
StatePublished - 2021
EventIAF Space Exploration Symposium 2021 at the 72nd International Astronautical Congress, IAC 2021 - Dubai, United Arab Emirates
Duration: Oct 25 2021Oct 29 2021

Publication series

NameProceedings of the International Astronautical Congress, IAC
ISSN (Print)0074-1795


ConferenceIAF Space Exploration Symposium 2021 at the 72nd International Astronautical Congress, IAC 2021
Country/TerritoryUnited Arab Emirates


  • automated design.
  • extreme environments
  • mobility
  • planetary robotics

ASJC Scopus subject areas

  • Aerospace Engineering
  • Astronomy and Astrophysics
  • Space and Planetary Science


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