A Force-Sensing System on Legs for Biomimetic Hexapod Robots Interacting with Unstructured Terrain

Zhang, He; Wu, Rui; Li, Changle; Zang, Xizhe; Zhang, Xuehe; Jin, Hongzhe; Zhao, Jie

doi:10.3390/s17071514

Cited by 20 publications

(7 citation statements)

References 31 publications

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“…From a robotics perspective, Wu et al [3] proposed a small legged robot that used an array of miniature capacitive tactile sensors to directly measure ground reaction forces (GRF) and used them to classify terrains. Zhang et al [14] also sensed ground forces using force/torque sensor for biomimetic hexapod robots walking on unstructured terrains. Similarly, Giguere et al [4] described a tactile probe for surface classification utilizing mobile robots, with single-axis accelerometer.…”

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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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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“…In grasping and manipulating situations, the information pertaining to such a force is useful so that it can be successfully employed [4]. Well-known sensing methods are implemented through electromagnetic induction [5] to determine the small power for an interaction force and the torque sensor [6] for a large interaction force. Meanwhile, the sense of sight can be easily implemented using a camera device.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Three-Dimensional Convolutional Neural Network for Inferring Physical Interaction Force from Video

Kim

Cho

Lim

et al. 2019

Sensors

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Interaction forces are traditionally predicted by a contact type haptic sensor. In this paper, we propose a novel and practical method for inferring the interaction forces between two objects based only on video data—one of the non-contact type camera sensors—without the use of common haptic sensors. In detail, we could predict the interaction force by observing the texture changes of the target object by an external force. For this purpose, our hypothesis is that a three-dimensional (3D) convolutional neural network (CNN) can be made to predict the physical interaction forces from video images. In this paper, we proposed a bottleneck-based 3D depthwise separable CNN architecture where the video is disentangled into spatial and temporal information. By applying the basic depthwise convolution concept to each video frame, spatial information can be efficiently learned; for temporal information, the 3D pointwise convolution can be used to learn the linear combination among sequential frames. To validate and train the proposed model, we collected large quantities of datasets, which are video clips of the physical interactions between two objects under different conditions (illumination and angle variations) and the corresponding interaction forces measured by the haptic sensor (as the ground truth). Our experimental results confirmed our hypothesis; when compared with previous models, the proposed model was more accurate and efficient, and although its model size was 10 times smaller, the 3D convolutional neural network architecture exhibited better accuracy. The experiments demonstrate that the proposed model remains robust under different conditions and can successfully estimate the interaction force between objects.

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“…In particular, the main physical property that a robot grasping and interacting with objects needs to sense is the interaction force. For measuring this interaction force during the robot’s interaction with the environment, a tactile sensor [ 8 , 9 ] is used to sense a small force, such as a human skin sensation, and a force/torque sensor [ 10 , 11 ] to sense a larger force, such as a human kinesthetic force. Operations involving picking up an object by hand require a richer tactile and kinesthetic sense than that which the current systems provide in order to achieve human-level performance [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Inferring Interaction Force from Visual Information without Using Physical Force Sensors

Hwang

Lim

2017

Sensors

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In this paper, we present an interaction force estimation method that uses visual information rather than that of a force sensor. Specifically, we propose a novel deep learning-based method utilizing only sequential images for estimating the interaction force against a target object, where the shape of the object is changed by an external force. The force applied to the target can be estimated by means of the visual shape changes. However, the shape differences in the images are not very clear. To address this problem, we formulate a recurrent neural network-based deep model with fully-connected layers, which models complex temporal dynamics from the visual representations. Extensive evaluations show that the proposed learning models successfully estimate the interaction forces using only the corresponding sequential images, in particular in the case of three objects made of different materials, a sponge, a PET bottle, a human arm, and a tube. The forces predicted by the proposed method are very similar to those measured by force sensors.

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A Force-Sensing System on Legs for Biomimetic Hexapod Robots Interacting with Unstructured Terrain

Cited by 20 publications

References 31 publications

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

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

An Efficient Three-Dimensional Convolutional Neural Network for Inferring Physical Interaction Force from Video

Inferring Interaction Force from Visual Information without Using Physical Force Sensors

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