Piezoelectric Polymer-Based Collision Detection Sensor for Robotic Applications

Wooten, James; Bevly, David M.; Hung, John Y.

doi:10.3390/electronics4010204

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…Yet another approach is external proprioception based on an Integrated Measurement Unit (IMU) sensor [ 39 ]. Many other methods based on external complex physical or virtual sensors, such as cameras, 3d cameras, vibration analyzers and other [ 40 , 41 , 42 ], are also heading in this direction.…”

Section: Problem Formulationmentioning

confidence: 99%

A Simple Neural Network for Collision Detection of Collaborative Robots

Czubenko

Kowalczuk

2021

Sensors

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Due to the epidemic threat, more and more companies decide to automate their production lines. Given the lack of adequate security or space, in most cases, such companies cannot use classic production robots. The solution to this problem is the use of collaborative robots (cobots). However, the required equipment (force sensors) or alternative methods of detecting a threat to humans are usually quite expensive. The article presents the practical aspect of collision detection with the use of a simple neural architecture. A virtual force and torque sensor, implemented as a neural network, may be useful in a team of collaborative robots. Four different approaches are compared in this article: auto-regressive (AR), recurrent neural network (RNN), convolutional long short-term memory (CNN-LSTM) and mixed convolutional LSTM network (MC-LSTM). These architectures are analyzed at different levels of input regression (motor current, position, speed, control velocity). This sensor was tested on the original CURA6 robot prototype (Cooperative Universal Robotic Assistant 6) by Intema. The test results indicate that the MC-LSTM architecture is the most effective with the regression level set at 12 samples (at 24 Hz). The mean absolute prediction error obtained by the MC-LSTM architecture was approximately 22 Nm. The conducted external test (72 different signals with collisions) shows that the presented architecture can be used as a collision detector. The MC-LSTM collision detection f1 score with the optimal threshold was 0.85. A well-developed virtual sensor based on such a network can be used to detect various types of collisions of cobot or other mobile or stationary systems operating on the basis of human-machine interaction.

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Section: Problem Formulationmentioning

confidence: 99%

A Simple Neural Network for Collision Detection of Collaborative Robots

Czubenko

Kowalczuk

2021

Sensors

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“…At present, piezoelectric materials are widely used in the sensor hardware and energy collection field [32][33][34][35]. The traditional sensors, based on the piezoelectric effect, such as piezoelectric ceramics, lead zirconate titanate, and niobate lead magnesium, have not been widely used as wearable human motion monitoring sensors, due to brittleness, deleteriousness, complex manufacturing, and tendency to corrosion [36][37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

A Flexible and Stretchable Self-Powered Nanogenerator in Basketball Passing Technology Monitoring

et al. 2021

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The rapid development of the fifth generation technology poses more challenges in the human motion inspection field. In this study, a nanogenerator, made by PVDF, ionic hydrogel, and PDMS, is used. Furthermore, a transparent, stretchable, and biocompatible PENG (TSB-PENG) is presented, which can be used as a self-powered sensor attached to the athlete’s joints, which helps to monitor the training and improve the subject’s performance. This device shows the ability to maintain a relatively stable output, under various external environments (e.g., inorganic salt, organic matter and temperature). Additionally, TSB-PENG can supply power to small-scale electronic equipment, such as Bluetooth transmitting motion data in real time. This study can provide a new approach to designing lossless, real-time, portable, and durable self-powered sensors in the sports motoring field.

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