Acoustic surface perception from naturally occurring step sounds of a dexterous hexapod robot

Ozkul, Mine Cuneyitoglu; Saranlı, Afşar; Yazıcıoğlu, Yiğit

doi:10.1016/j.ymssp.2013.03.002

Cited by 17 publications

(4 citation statements)

References 25 publications

(21 reference statements)

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“…Several approaches use sounds recorded while tapping, shaking, holding, etc., to recognize and classify objects (Schenck et al, 2014; Sinapov et al, 2011; Kroemer et al, 2011; Tomoaki Nakamura et al, 2007; Richmond and Pai 2000; Luo et al, 2017). Recordings of sound have also been used to determine object materials (Krotkov et al, 1997), surface properties (Cuneyitoglu Ozkul et al, 2013), and the distance from objects while grasping (Jiang and Smith 2012). Even features like the flow rate of granular material (Clarke et al, 2018) or the ambient temperature (Cai et al, 2021) have been measured using sound.…”

Section: Related Workmentioning

confidence: 99%

Passive and active acoustic sensing for soft pneumatic actuators

Wall

Zöller

Brock

2023

The International Journal of Robotics Research

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We propose a sensorization method for soft pneumatic actuators that uses an embedded microphone and speaker to measure different actuator properties. The physical state of the actuator determines the specific modulation of sound as it travels through the structure. Using simple machine learning, we create a computational sensor that infers the corresponding state from sound recordings. We demonstrate the acoustic sensor on a soft pneumatic continuum actuator and use it to measure contact locations, contact forces, object materials, actuator inflation, and actuator temperature. We show that the sensor is reliable (average classification rate for six contact locations of 93%), precise (mean spatial accuracy of 3.7 mm), and robust against common disturbances like background noise. Finally, we compare different sounds and learning methods and achieve best results with 20 ms of white noise and a support vector classifier as the sensor model.

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Section: Related Workmentioning

confidence: 99%

Passive and active acoustic sensing for soft pneumatic actuators

Wall

Zöller

Brock

2023

The International Journal of Robotics Research

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“…In their work, the acoustic features are extracted by surveying acoustic methods from other domains and their efficacy is verified in terrain classification. Adopting feature extraction methods from speech processing literature, a novel feature vector composed of zero crossing rate, spectral band energies and their vector time derivatives is proposed [13]. In [15], a deep spatiotemporal model is designed for learning complex dynamics in audio signals which replaces manually handcrafted features.…”

Section: Related Workmentioning

confidence: 99%

“…In vibration-based damage detection, RMS, skewness and kurtosis are used since they have been proved to be useful for bearing fault detection [35,43]. Acoustic terrain classification is similar to the vibration-based one, so we refer to its features such as zero crossing rate and short time energy [13,14,22]. Here, we summarize the most common time-domain features.…”

Section: Feature-engineering Approachesmentioning

confidence: 99%

“…The non-interactive methods usually possess high classification accuracy, but they are demanding to environmental conditions (such as illumination), variations in appearance or cover (such as leaves). Alternatively, the interactive methods, which can be realized by means of acoustics [13,14,15], haptics [16,17,18] and vibration [19,20,21], are robust to changes in appearance and illumination, and have better computational efficiency. Therefore, the interactive methods have shown increasing potential in robotic environment perception.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Study of Different Methods in Vibration-Based Terrain Classification for Wheeled Robots with Shock Absorbers

Mei

Jin-yi

et al. 2019

Sensors

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Autonomous robots that operate in the field can enhance their security and efficiency by accurate terrain classification, which can be realized by means of robot-terrain interaction-generated vibration signals. In this paper, we explore the vibration-based terrain classification (VTC), in particular for a wheeled robot with shock absorbers. Because the vibration sensors are usually mounted on the main body of the robot, the vibration signals are dampened significantly, which results in the vibration signals collected on different terrains being more difficult to discriminate. Hence, the existing VTC methods applied to a robot with shock absorbers may degrade. The contributions are two-fold: (1) Several experiments are conducted to exhibit the performance of the existing feature-engineering and feature-learning classification methods; and (2) According to the long short-term memory (LSTM) network, we propose a one-dimensional convolutional LSTM (1DCL)-based VTC method to learn both spatial and temporal characteristics of the dampened vibration signals. The experiment results demonstrate that: (1) The feature-engineering methods, which are efficient in VTC of the robot without shock absorbers, are not so accurate in our project; meanwhile, the feature-learning methods are better choices; and (2) The 1DCL-based VTC method outperforms the conventional methods with an accuracy of 80.18%, which exceeds the second method (LSTM) by 8.23%.

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