Safety System for an Industrial Cooperating Robot Based on Depth Cameras

Masłowski, Dawid; Czubenko, Michał

doi:10.14313/par_234/41

Cited by 1 publication

(2 citation statements)

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“…Detection of events requires the implementation of appropriate sensors and algorithms (usually expensive). The following solutions are used to predict a collision event: optical zone barriers, pressure mats and strips, safety keys, motion and distance sensors, and vision sensors [ 6 , 7 ]. The pre-collision strategies take into account what will happen when an intruder enters the robot space.…”

Section: Introductionmentioning

confidence: 99%

“…In this application, deep neural networks (e.g., YOLO—You Look Only Once—a real-time object detection system) used to detect an intruder provide good results. One can obtain even better results using neural networks (e.g., OpenPose) for detecting and estimating poselets (a poselet—positioning or position distribution—is a specific description, characteristics or structure, related to the mechanics of the human figure based on pose markers—key marker points, such as points on the feet, ankles, knees, hips, arms, elbows, wrists, neck and head—that suitably define a human pose or gesture) [ 6 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

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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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%