Acoustics based terrain classification for legged robots

Christie, J.H.; Kottege, Navinda

doi:10.1109/icra.2016.7487543

Cited by 51 publications

(37 citation statements)

References 16 publications

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“…Feature Extraction. In [19][20][21], frequency domainbased features and deep features are employed in terrain classification. Using these features may improve the classification accuracy to some extent.…”

Section: Priori Estimation Calculate the A Priori Estimation S T Byŝmentioning

confidence: 99%

Terrain Adaptive Estimation of Instantaneous Centres of Rotation for Tracked Robots

Wang

et al. 2018

Complexity

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As a type of skid-steering mobile robot, the tracked robot suffers from inevitable slippage, which results in an imprecise kinematics model and a degradation of performance during navigation. Compared with the traditional robot, the kinematics model is able to reflect the influences of slippage through the introduction of instantaneous centres of rotation (ICRs). However, ICRs cannot be measured directly and are time-varying with terrain variation, and thus, here, we aim to develop an online estimation method to acquire the ICRs of a robot by means of data fusion technologies. First, an innovation-based extended Kalman filter (IEKF) is employed to fuse the readings from two incremental encoders and a GPS-compass integrated sensor, to provide a real-time ICR estimation. Second, a decision tree-based learning system is used to classify the terrains that the robot traverses, according to the vibration signals gathered by an accelerometer. The results of this terrain classification are improved via a Bayesian filter, by utilizing temporal correlation in the terrain time series. Third, the performances of the ICR estimation and terrain classification are mutually promoted. On one hand, terrain variation is detected with the aid of the terrain classification, and therefore, the process noise variance of IEKF can be automatically adjusted. Hence, the results of ICR estimation are smooth if the terrain does not change and converge rapidly upon terrain variation. On the other hand, the sudden changes in innovation are used to adjust the state transition probability during the recursive calculation of the Bayesian filter, thus increasing the accuracy of the terrain classification. A real-world experiment was undertaken on a tracked robot to validate the effectiveness of the proposed method. It is also demonstrated that the terrain adaptive odometry outperforms the traditional approach with the knowledge of ICRs.

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Section: Priori Estimation Calculate the A Priori Estimation S T Byŝmentioning

confidence: 99%

Terrain Adaptive Estimation of Instantaneous Centres of Rotation for Tracked Robots

Wang

et al. 2018

Complexity

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“…Graeme Best et al [24] used the actual position and goal position to build a support vector machine (SVM) classifier to distinguish different terrains. Joshua Christie et al [25] offered an acoustics based method to perceive the terrain-robot interactions during locomotion. Camilo Ordonez et al [26] used a probability neural network (PNN) to train the recorded observer current to identify terrain.…”

Section: Introductionmentioning

confidence: 99%

Obstacle Avoidance and Terrain Identification for a Hexapod Robot

Zhao

Gao

Yin

2020

Preprint

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Legged robots have advanced potential in mobility compared with wheeled or tracked vehicles in harsh environment. In real applications, both environmental perception and terrain identification play an important role in ensuring the safety of the robot. However, few papers involve the obstacle avoidance and terrain identification methods for legged robots. An obstacle avoidance and terrain identification method is proposed in this paper. Firstly, a novel dynamic obstacle avoidance motion planning is proposed which takes full account of the motion ability of the robot. Secondly, a terrain identification method is proposed. The robot can distinguish typical terrains like flat floor, step-on, step-down, ditch and so on. Besides, the ground properties like stiffness are identified to improve the performance of the robot. Finally, the proposed method is integrated on hexapod robots and tested by real experiments which show the method effective. Our methods and experimental results can be a valuable reference for other legged robots.

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“…Moreover, there is a need to explore the alternative approach against traditional terrain classifications. For instance, it is well known that human beings can capture information about terrain during walking by sensing it with their feet and by the sound of their footsteps [22]. The kinematic properties of the human motion pattern allow capturing the motion data for gait analysis, which in turn has been used as a reliable source for activity recognition [23] and estimating soft biometrics including gait-based age estimation [24,25], gender classification [24,26], emotion recognition [27] and human authentication/identification [28,29].…”

Section: Introductionmentioning

confidence: 99%

What Lies Beneath One’s Feet? Terrain Classification Using Inertial Data of Human Walk

et al. 2019

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The objective of this study was to investigate if the inertial data collected from normal human walk can be used to reveal the underlying terrain types. For this purpose, we recorded the gait patterns of normal human walk on six different terrain types with variation in hardness and friction using body mounted inertial sensors. We collected accelerations and angular velocities of 40 healthy subjects with two smartphones embedded inertial measurement units (MPU-6500) attached at two different body locations (chest and lower back). The recorded data were segmented with stride based segmentation approach and 194 tempo-spectral features were computed for each stride. We trained two machine learning classifiers, namely random forest and support vector machine, and cross validated the results with 10-fold cross-validation strategy. The classification tasks were performed on indoor–outdoor terrains, hard–soft terrains, and a combination of binary, ternary, quaternary, quinary and senary terrains. From the experimental results, the classification accuracies of 97% and 92% were achieved for indoor–outdoor and hard–soft terrains, respectively. The classification results for binary, ternary, quaternary, quinary and senary class classification were 96%, 94%, 92%, 90%, and 89%, respectively. These results demonstrate that the stride data collected with the low-level signals of a single IMU can be used to train classifiers and predict terrain types with high accuracy. Moreover, the problem at hand can be solved invariant of sensor type and sensor location.

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Acoustics based terrain classification for legged robots

Cited by 51 publications

References 16 publications

Terrain Adaptive Estimation of Instantaneous Centres of Rotation for Tracked Robots

Terrain Adaptive Estimation of Instantaneous Centres of Rotation for Tracked Robots

Obstacle Avoidance and Terrain Identification for a Hexapod Robot

What Lies Beneath One’s Feet? Terrain Classification Using Inertial Data of Human Walk

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