Machine learning for planetary rovers

Ono, Masahiro; Rothrock, Brandon; Iwashita, Yumi; Higa, Shoya; Timmaraju, Virisha; Sahnoune, Sami; Qiu, Dicong; Islam, Tanvir; Didier, Annie; Laporte, Christopher; Atha, Deegan; Sun, V. Z.; Otsu, Kyohei; Paton, Mike; Lamarre, Olivier; Daftry, Shreyansh; Swan, R. Michael; Stambouli, Adam; Chen, Flynn; Shah, Biren; Stack, K. M.; Mattman, Chris

doi:10.1016/b978-0-12-818721-0.00019-7

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…In celestial bodies, many of the environments are extreme in terms of bumpy ground or variable friction surfaces and are filled with hazards such as rocks of different sizes and craters. As such, much research has been conducted on how to create safe and efficient locomotion, path planning and obstacle avoidance for space exploration robots as well [10].…”

Section: Introductionmentioning

confidence: 99%

SegVisRL: Visuomotor Development for a Lunar Rover for Hazard Avoidance using Camera Images

Blum,

Paillet,

Masawat

et al. 2021

Preprint

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The visuomotor system of any animal is critical for its survival, and the development of a complex one within humans is large factor in our success as a species on Earth. This system is an essential part of our ability to adapt to our environment. We use this system continuously throughout the day, when picking something up, or walking around while avoiding bumping into objects. Equipping robots with such capabilities will help produce more intelligent locomotion with the ability to more easily understand their surroundings and to move safely. In particular, such capabilities are desirable for traversing the lunar surface, as it is full of hazardous obstacles, such as rocks. These obstacles need to be identified and avoided in real time. This paper seeks to demonstrate the development of a visuomotor system within a robot for navigation and obstacle avoidance, with complex rock shaped objects representing hazards. Our approach uses deep reinforcement learning with only image data. In this paper, we compare the results from several neural network architectures and a preprocessing methodology which includes producing a segmented image and downsampling.

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Section: Introductionmentioning

confidence: 99%

SegVisRL: Visuomotor Development for a Lunar Rover for Hazard Avoidance using Camera Images

Blum,

Paillet,

Masawat

et al. 2021

Preprint

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Cognition: Distributed Data Processing System for Lunar Activities

Walas,

Cwiek,

Strzalka

et al. 2023

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

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Deep Reinforcement Learning-Based Failure-Safe Motion Planning for a 4-Wheeled 2-Steering Lunar Rover

Park

Chung

2023

Aerospace

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The growing trend of onboard computational autonomy has increased the need for self-reliant rovers (SRRs) with high efficiency for unmanned rover activities. Mobility is directly associated with a successful execution mission, thus fault response for actuator failures is highly crucial for planetary exploration rovers in such a trend. However, most of the existing mobility health management systems for rovers have focused on fault diagnosis and protection sequences that are determined by human operators through ground-in-the-loop solutions. This paper presents a special four-wheeled two-steering lunar rover with a modified explicit steering mechanism, where each left and right wheel is controlled by only two actuators. Under these constraints, a new motion planning method that combines reinforcement learning with the rover’s kinematic model without the need for dynamics modeling is devised. A failure-safe algorithm is proposed to address the critical loss of mobility in the case of steering motor failure, by expanding the devised motion planning method, which is designed to ensure mobility for mission execution in a four-wheeled rover. The algorithm’s performance and applicability are validated through simulations on high-slip terrain scenarios caused by steering motor failure and compared with a conventional control method in terms of reliability. This simulation validation serves as a preliminary study toward future works on deformable terrain such as rough or soft areas and optimization of the deep neural network’s weight factor for fine-tuning in real experiments. The failure-safe motion planning provides valuable insights as a first-step approach toward developing autonomous recovery strategies for rover mobility.

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Machine learning for planetary rovers

Cited by 3 publications

References 6 publications

SegVisRL: Visuomotor Development for a Lunar Rover for Hazard Avoidance using Camera Images

SegVisRL: Visuomotor Development for a Lunar Rover for Hazard Avoidance using Camera Images

Cognition: Distributed Data Processing System for Lunar Activities

Deep Reinforcement Learning-Based Failure-Safe Motion Planning for a 4-Wheeled 2-Steering Lunar Rover

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