Original title: RoughSense: Lightweight Terrain-Induced Rover Vibration Prediction Using Point Clouds and IMU Feedback
Gabriel Manuel Garcia, Stephanie Aravecchia, Miguel Angel Olivares-Mendez · September 2026
Space rovers exploring underground spaces have limited power and computer processing to navigate rough terrain safely. To solve this, researchers built RoughSense, a lightweight tool that predicts how much the ground will shake a rover before it drives across it. First, a laser scanner maps the environment, and an algorithm fits shapes to small patches of ground data to guess how bumpy the surface is. Next, an onboard motion sensor measures the real vibrations the rover feels as it moves. Finally, the system compares the real shakes to the laser guess and adjusts its future predictions online. The researchers tested this method in an outdoor field, a lunar analogue environment, and an underground mine.
Imagine hiking on a rocky trail where you can see the path ahead but not feel it underfoot. You try to estimate how much your ankles will twist based on the rocks, but you're often wrong. In this paper, the rover's laser scan is like seeing the trail, and its motion sensors are like feeling the twists as it walks. The system updates its estimate of roughness based on real twists to plan safer steps.
This helps rovers navigate safely on planets without constant human control, saving power and computing resources for deep space missions. Astronauts and robots in underground or remote areas can operate more reliably when vibrations are anticipated.
The abstract does not specify limitations, but it only evaluates the method in a lunar analogue, outdoor field, and underground mine, so performance in other environments is not established.
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RoughSense: Lightweight Terrain-Induced Rover Vibration Prediction Using Point Clouds and IMU Feedback — Gabriel Manuel Garcia, Stephanie Aravecchia, Miguel Angel Olivares-Mendez
Abstract licence: CC0
Explanation generated by Synthesis from the abstract — it is not the original paper. This is a preprint, so it hasn't necessarily been peer reviewed.