Floor Friction Assessment from Robot Tire-Surface Interaction
Authors: Kulbhushan Komarraju, Nathaniel Pearson, Yu Gu, Jason Gross
Organizations: Department of Mechanical, Materials and Aerospace Engineering, West Virginia University · Lane Department of Computer Science and Electrical Engineering, West Virginia University
Floor traction influences wheel-slip behavior, dead-reckoning accuracy, and the navigation performance of wheeled mobile robots that rely on wheel odometry. As these robots become increasingly common in GNSS-denied indoor environments, including automated manufacturing and retail facilities, their onboard measurements could also be used to characterize floor conditions and improve localization. In this study, a skid-steer robot was driven over five floor surfaces with distinct traction characteristics using commanded velocity profiles designed to induce wheel slip through repeated acceleration and direction reversal. Differences between the body velocity estimated from inertial sensing and the velocity obtained from wheel odometry were evaluated using the integrated relative-velocity error, phase-derived wheel-to-body response delay, and velocity-reversal response delay. Welch power spectral density analysis was additionally applied to characterize the frequency distribution of the velocity mismatch during positive and negative acceleration. Band-integrated spectral power, spectral centroid, and spectral decay rate were extracted from each recording and evaluated using linear discriminant analysis and multiclass classification. The response-delay measurements produced consistent surface-dependent trends, with the longest delays observed on soap and the shortest delays observed on carpet. The spectral features also provided clear separation among the tested surfaces, achieving a balanced classification accuracy of 97.1% under recording-level cross-validation. These results demonstrate that both the magnitude and frequency domain characteristics of wheel-to-body velocity mismatch contain useful information about tire-surface interactions. Such information could support surface recognition and the development of slip-aware wheel-odometry weighting within robot state-estimation frameworks.
Figures & tables
Fig. 1 : Skid-steer robot undergoing experiments in the motion capture facility. In this example experiment, the driving surface is covered with a soapy mixture.
Fig. 2 : Schematic of the factor graph estimation being used for robot state estimation. Blue nodes represent the state variables (pose, velocity, and IMU biases) at each timestep. Red factors denote IMU preintegration constraints between consecutive states, while green factors represent Motion Capture (MoCap) pose and velocity measurement updates.
Fig. 3 : Commanded velocity profile
Fig. 4 : Velocity comparison and relative velocity profile for Concrete surface.
Fig. 5 : Velocity comparison and relative velocity profile for Vinyl surface.
Fig. 6 : Velocity comparison and relative velocity profile for Melamine Board surface.
Fig. 7 : Velocity comparison and relative velocity profile for Soap surface.
Fig. 8 : Velocity comparison and relative velocity profile for Carpet surface.
Fig. 9 : Normalized spectral-power distribution of the relative velocity during negative and positive FG slopes. Each segment was normalized by its total spectral power before the frequency-wise median was calculated. The heatmap therefore represents the distribution of mismatch power across frequency rather than its absolute magnitude. White regions correspond to frequencies excluded because of segment-resolution or sampling limitations.
Fig. 10 : Surface discrimination using run-level spectral features. Each point in (a) represents one complete recording. The LDA projection is a supervised visualization of the spectral-feature space. In (b), the matrix entries show the number of held-out recordings assigned to each surface, while the shading represents the row-normalized classification fraction.
Fig. 11 : Median AUC for different surface relative velocity profiles for up and down segments.
Surface
Median ± Std.
Board
0.420±0.080
Carpet
0.845±0.065
Concrete
0.390±0.033
Soap
0.150±0.080
Vinyl
0.500±0.043
TABLE I : Median static CoF values across surfaces measured by a drag-sled tribometer.
Fig. 12 : AUC vs SCoF; Horizontal error bars indicate the interquartile range (IQR) of the estimated AUC values, while vertical error bars indicate the standard deviation of the tribometer SCoF measurements.
Fig. 13 : Wheel-to-body velocity response delays measured for the five surfaces. Each point represents one complete recording. The boxes show the interquartile range, the center lines represent the medians, and the whiskers extend to 1.5 times the interquartile range. A positive delay indicates that the ground-referenced body velocity responded after the wheel-odometry velocity.
Fig. 14 : Relationship between the static coefficient of friction and (A) the phase-derived wheel-to-body response delay and (B) the velocity-reversal response delay. Each point represents the median delay across complete recordings for one surface. Horizontal error bars indicate the interquartile range of the response delay, while vertical error bars indicate the standard deviation of the measured static coefficient of friction. Dotted lines represent unweighted first-order linear fits.
Legged robots rely on accurate ground interaction awareness to traverse variable terrains, such as slippery surfaces. Existing slip detection methods often rely on kinematics and proprioception, which lack the sensitivity to detect early-stage slips that occur prior to catastrophic instability. Thus, this paper presents SlipSense, a novel framework for online force-based slip detection using a custom lightweight sensorized foot for quadrupeds to detect slip. The framework integrates a multimodal sensor design with a LSTM-based model to infer ground reaction forces and detect slip-indicative anomalies during locomotion. The proposed framework is deployed on a Unitree Go1 quadruped to demonstrate blind online slip detection over a slippery terrain. Our method detects early-stage slips down to an average displacement of 24.1 +/-6.4mm with an overall accuracy of 85.9%. This represents a 3.3-fold finer detection resolution and a 24% relative accuracy improvement over a standard kinematic baseline that uses foot velocity inferred through state estimation. The work in this paper serves as a foundation for force-aware gait adaptation in legged robotic locomotion, allowing future controllers to estimate terrain friction and adjust constraints, thus improving the overall stability of the system.
Iris Szu-Yao Liu, Chien Chern Cheah, Meng Yee Michael Chuah
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore · Institute for Infocomm Research, Agency for Science Technology and Research, Singapore
Robots that traverse natural terrain must interpret contact forces generated under highly dynamic conditions. However, most terrain characterization approaches rely on quasi-static assumptions that neglect velocity- and acceleration-dependent effects arising during impact and rapid stance transitions. In this work, we investigate granular terrain interaction during high-speed hopping and develop a physics-based framework for dynamic terrain characterization using proprioceptive sensing alone. Through controlled hopping experiments with systematically varied impact speed and leg compliance, our measurements reveal that quasi-static based assumptions lead to large discrepancies in granular terrain property estimation during high-speed hopping, particularly upon touchdown and controller-induced stiffness transitions. Velocity-dependent drag alone cannot explain these discrepancies. Instead, acceleration-dependent added-mass effects - associated with grain entrainment beneath the foot - dominate transient force responses. We integrate this force decomposition with a momentum-observer-based estimator that compensates for rigid-body inertia and gravity, and introduce an acceleration-aware weighted regression to account for increased force variance during high-acceleration events. Together, these methods enable consistent recovery of granular resistance across locomotion conditions, closely matching linear-actuator ground truth. Our results demonstrate that accurate terrain inference during high-speed locomotion requires explicit treatment of acceleration-dependent granular effects, and provide a foundation for robots to characterize complex deformable terrain during dynamic exploration of terrestrial and planetary environments.
Yifeng Zhang, Yue Wu, Jake Futterman +5
Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, USA
Wheel kinematics can support odometry for quadrupedal wheel-legged robots when LiDAR geometry provides weak motion constraints. However, slip and contact loss make rolling predictions unreliable, and contact state alone cannot determine their reliability across motion directions. We propose a tightly coupled LiDAR-inertial-kinematic odometry framework that retains analytical rolling predictions and learns per-wheel, direction-dependent process covariances from proprioceptive history. The estimator maintains moving wheel-center states, allowing the learned uncertainty to regulate the influence of rolling predictions through forward-kinematic corrections. Degeneracy-aware projection (DAP) complements this mechanism by suppressing LiDAR constraints along geometrically weak directions. The learned covariance network (CovNet) is trained through a differentiable filter using simulated velocity supervision and deployed on the real robot without fine-tuning. On the six room sequences with the highest degeneracy ratios in our indoor dataset, the method reduces mean per-sequence position root mean square error (RMSE) by 66.2% relative to FAST-LIO2 and by 31.0% relative to a fixed-covariance variant. In a severely degenerate corridor run, it achieves an endpoint position error of 0.198 m, compared with 7.531 m for FAST-LIO2. These results demonstrate robust odometry under challenging geometric and wheel-motion conditions. We plan to publicly release our code and multimodal dataset. Project website: https://weihairuo.github.io/wheel-motion-uncertainty/
Hairuo Wei, Yunfan Ren, Renda Yang +2
The Chinese University of Hong Kong, Hong Kong SAR, China. · Tongji University, Shanghai, China. · SEER Robotics.