View in EDS

Terrain Classification for Planetary Rovers Using Wireless In‐Wheel Sensor Modules and Machine Learning.

Saved in:
Bibliographic Details
Title: Terrain Classification for Planetary Rovers Using Wireless In‐Wheel Sensor Modules and Machine Learning.
Authors: Khan, Md Masrul1 (AUTHOR), Shaharea, Sultan2 (AUTHOR), Mannaf, Manseeb M.2 (AUTHOR), Ahemed, Shihab3 (AUTHOR), Anik, Fahim Islam2 (AUTHOR) fahimislamanik@gmail.com, Hossain, Md Jarir4 (AUTHOR) mh357@uakron.edu, Nahiyan, Helal An2 (AUTHOR), Karmaker, Sourav2 (AUTHOR)
Source: Journal of Field Robotics. May2026, Vol. 43 Issue 3, p1884-1904. 21p.
Subjects: Machine learning, Wireless sensor nodes, Random forest algorithms, Mars rovers, Energy consumption, Acquisition of data
Abstract: Safe and reliable mobility over different kinds of ground is important for planetary rovers on space missions. Since terrain changes might affect the mobility of the rover, energy consumption, and safety, detecting the type of ground in real‐time is vital. While past methods utilized cameras, motion sensors, or traditional machine learning, achieving consistent and accurate outputs on mixed terrains has been found to be challenging. Here, a wireless sensor module was developed and placed in the rover wheel, and sampled data from a Force‐Sensitive Resistor and an Inertial Measurement Unit at a 100‐samples/s sampling rate. A suite of supervised machine learning algorithms is used for terrain type labeling: rocky, sandy, hard, and grassy. The system could classify terrains in real‐time with good accuracy. Random Forest performed the best among them with 88% accuracy and an F1 score of 0.87. As a simple and efficient model, Random Forest is chosen for real‐time classification. This sensor‐based system enables real‐time, lighting‐independent terrain classification, overcoming the visual limitations and latency of deep learning image‐based methods. The solution helps the rover plan its movement based on the terrain sensed, making it energy efficient and responsive. Key contributions of this study include the design of a wireless multisensor module, high‐frequency data acquisition, and the integration of machine learning models for rapid terrain‐adaptive mobility. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Field Robotics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:Safe and reliable mobility over different kinds of ground is important for planetary rovers on space missions. Since terrain changes might affect the mobility of the rover, energy consumption, and safety, detecting the type of ground in real‐time is vital. While past methods utilized cameras, motion sensors, or traditional machine learning, achieving consistent and accurate outputs on mixed terrains has been found to be challenging. Here, a wireless sensor module was developed and placed in the rover wheel, and sampled data from a Force‐Sensitive Resistor and an Inertial Measurement Unit at a 100‐samples/s sampling rate. A suite of supervised machine learning algorithms is used for terrain type labeling: rocky, sandy, hard, and grassy. The system could classify terrains in real‐time with good accuracy. Random Forest performed the best among them with 88% accuracy and an F1 score of 0.87. As a simple and efficient model, Random Forest is chosen for real‐time classification. This sensor‐based system enables real‐time, lighting‐independent terrain classification, overcoming the visual limitations and latency of deep learning image‐based methods. The solution helps the rover plan its movement based on the terrain sensed, making it energy efficient and responsive. Key contributions of this study include the design of a wireless multisensor module, high‐frequency data acquisition, and the integration of machine learning models for rapid terrain‐adaptive mobility. [ABSTRACT FROM AUTHOR]
ISSN:15564959
DOI:10.1002/rob.70124