Adaptive Self‑maintenance
Agricultural Robotic Systems for
Cultivation on Mars
Report 2023
Isuru Jayarathne
14/04/2023

Contents
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Problem definition and motivation
Research goals
Agricultural robotics○ Agricultural cycle○ Robots
Self-maintenance○ Definition○ Stages
Evolutionary robotics○ Virtual creatures vs manufacturable robots○ Virtual creatures○ Manufacturable robots
Available robotic architectures○ Analogy
Fabrication technologies
Process breakdown
References

Problem definition and motivation●Cultivation on Mars without human involvement could be helpful for colonizing
the planet.

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Agricultural robots are more vulnerable to damage, wear-off, or malfunction,compared to other robotic systems.

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Due to the absence of humans, maintaining space robotics systems is achallenging task.

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Utilizing available resources and adapting to the environment autonomously isessential for making space robotic systems sustainable.

Research goals●

Sustainability of robots with self-maintenance●

Evolution of structure or movements depending on the movements●

Optimization of resource consumption

Examples for different structures for agricultural robots

Agricultural cycle
Harvestingand storage

Preparationof soil

Agriculturalcycle
Irrigation

Adding manureand fertilizers

Sowing(planting)Agricultural robots●

Wheeled robot with cartesian manipulator●

Wheeled robot with articulated manipulator●Drones

Ecorobotix

IDTechEx

Dji MG-1S agri drone

Agricultural robots (cont.)

Source: https://www.idtechex.com/ja/research-article/idtechex-outlines-the-future-of-the-agricultural-robotics-industry/25744

Self-maintenance●Self-maintenance is a part of reliability engineering, which involves designingand building systems that can independently identify any loss or potential lossof function and then automatically restore functionality to maintain availability
and improve system resilience.

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Self-maintenance has five stages: monitoring, trigger, evaluation,
implementation, and verification.

Self-maintenance stages●

Monitoring: Observing system functions to reduce the chance of degradation
or a failure.

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Trigger: The point that decision which is been made to perform SE.

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Evaluation: Evaluate the damage which has occur or will occur.

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Implementation: Fix the damage or failure.
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DesignFabricateAssemble

Verification: Check and confirm the new system performance.

Evolutionary robotics
Virtual creatures● Fewer constraints● Hard to implement in real world● High diversity● High performance● Doesn’t have pre-defined bodyparts

Manufacturable robots● More constraints● Manufacturable● Low diversity● Low performance ⚠
● Have pre-defined body parts● Common factors○ Evolution happens in the simulated environment○ Follow the triangle of life to evolve○ Brains for agents are dynamically created andtrained independently in the environment

Triangle of life(Source: https://doi.org/10.1162/ARTL_a_00231)

Virtual creatures●

Computer-generated or simulated beings that mimic the behaviors andcharacteristics of real-life creatures

Karl Sims - Evolved virtual creatures(1994)

2D virtual creatures(2014)

Virtual creatures with muscles(2015)

Manufacturable evolutionary robotics●

Robotic systems that can be autonomously designed, optimized, andphysically manufactured using evolutionary algorithms

Autonomous design and fabrication(2020)

Assembling process of printed robot parts(2020)

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Task specific single robot●

Single robot with modular architecture●

Swarm robots (homogeneous)●Swarm robots (heterogeneous)

Self-maintenance complexity

Available robotic architecturesAnalogy
Ant-society

Heterogeneous swarm robot system

Queen

Centralized controller/decision maker ⚠

Workers

Entities that performing tasks (planting, irrigation,harvesting, etc.)

Specialist

Entities that has special abilities (fabricators,assembers)

Fabrication technologies●

Multi-material multi-nozzle 3D printers●

Conductive printing materials

Source:https://hackaday.com/2016/06/20/mosaic-palette-single-extr
uder-multi-color-and-multi-material-3d-printing/Source: https://www.nature.com/articles/s41586-019-1736-8

Process breakdown●This research will be started from autonomous assembly in the implementation
stage.

Process flow and data flow of self-maintenanceReferences
1. Kasiviswanathan P, Swanner ED, Halverson LJ, Vijayapalani P. Farming on Mars: Treatment of basaltic
regolith soil and briny water simulants sustains plant growth. PloS one, 17(8), 2022.
2. Brooks S, Roy R. An overview of self-engineering systems. Journal of Engineering Design., 3;32(8), 2021.
3. Umeda Y, Tomiyama T, Yoshikawa H. A design methodology for self-maintenance machines, 1995.
4. Aivaliotis P, Georgoulias K, Chryssolouris G. The use of Digital Twin for predictive maintenance inmanufacturing. International Journal of Computer Integrated Manufacturing., 32(11), 2019.
5. Buchanan E, Le Goff LK, Li W, Hart E, Eiben AE, De Carlo M, Winfield AF, Hale MF, Woolley R, Angus M,Timmis J. Bootstrapping artificial evolution to design robots for autonomous fabrication. Robotics., 9(4),2020.
6. Xu R, Li C. A modular agricultural robotic system (MARS) for precision farming: Concept andimplementation. Journal of Field Robotics., 39(4), 2022.

Thank you