國立虎尾科技大學 |

Modified Bug Algorithm with Supervisory Control Using Machine Learning to Reduce Human-Cobot Collisions.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Modified Bug Algorithm with Supervisory Control Using Machine Learning to Reduce Human-Cobot Collisions./
Author:	Li, Anran.
Description:	1 online resource (57 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-01.
Contained By:	Masters Abstracts International85-01.
Subject:	Mechanical engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798379904111

Modified Bug Algorithm with Supervisory Control Using Machine Learning to Reduce Human-Cobot Collisions.
Li, Anran.

Modified Bug Algorithm with Supervisory Control Using Machine Learning to Reduce Human-Cobot Collisions. - 1 online resource (57 pages)

Source: Masters Abstracts International, Volume: 85-01.

Thesis (M.S.)--Washington State University, 2023.

Includes bibliographical references

A collaborative robot, or cobot, is an efficient robot that can work alongside human workers in the same work cell. Cobots are popular in industry because they allow close collaboration between humans and robots. Although a cobot can stop when a collision occurs, it is not safe or pleasant for the human worker to be hit by the cobot multiple times a day. Therefore, it is important to reduce or eliminate collisions between cobots and human workers. This research uses inexpensive proximity sensors on the cobot to detect obstacles and generate motion decisions. First, a modified bug algorithm is utilized to maneuver the cobot to achieve a pick-and-place task. This algorithm applies a wrist yaw angle to move around the human workers and avoid collisions in the same work cell. Second, a long short-term memory (LSTM)-based artificial neural network was trained using a collision-free path dataset. The network model generates corrections when abnormal movements in the modified algorithm are detected due to interference in sensor readings.Experiments of pick-and-place tasks considered different locations and orientations of human workers, as well as dynamic scenarios. The results show a significant reduction in collisions using the proposed bug algorithm and machine learning supervisory control strategy. The supervisory control method has the ability to find additional solutions to reach the target and reduce the number of steps needed to explore potential collision-free paths.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379904111Subjects--Topical Terms:

557493
Mechanical engineering.
Subjects--Index Terms:

Machine learningIndex Terms--Genre/Form:

554714
Electronic books.

Modified Bug Algorithm with Supervisory Control Using Machine Learning to Reduce Human-Cobot Collisions.
LDR:02861ntm a22003977 4500 001 1148285
005 20240924101840.5
006 m o d
007 cr bn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798379904111
035 $a (MiAaPQ)AAI30318041
035 $a AAI30318041
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Li, Anran. $3 1474222
245 1 0 $a Modified Bug Algorithm with Supervisory Control Using Machine Learning to Reduce Human-Cobot Collisions.
264 0 $c 2023
300 $a 1 online resource (57 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 85-01.
500 $a Advisor: Gurocak, Hakan.
502 $a Thesis (M.S.)--Washington State University, 2023.
504 $a Includes bibliographical references
520 $a A collaborative robot, or cobot, is an efficient robot that can work alongside human workers in the same work cell. Cobots are popular in industry because they allow close collaboration between humans and robots. Although a cobot can stop when a collision occurs, it is not safe or pleasant for the human worker to be hit by the cobot multiple times a day. Therefore, it is important to reduce or eliminate collisions between cobots and human workers. This research uses inexpensive proximity sensors on the cobot to detect obstacles and generate motion decisions. First, a modified bug algorithm is utilized to maneuver the cobot to achieve a pick-and-place task. This algorithm applies a wrist yaw angle to move around the human workers and avoid collisions in the same work cell. Second, a long short-term memory (LSTM)-based artificial neural network was trained using a collision-free path dataset. The network model generates corrections when abnormal movements in the modified algorithm are detected due to interference in sensor readings.Experiments of pick-and-place tasks considered different locations and orientations of human workers, as well as dynamic scenarios. The results show a significant reduction in collisions using the proposed bug algorithm and machine learning supervisory control strategy. The supervisory control method has the ability to find additional solutions to reach the target and reduce the number of steps needed to explore potential collision-free paths.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Mechanical engineering. $3 557493
650 4 $a Robotics. $3 561941
653 $a Machine learning
653 $a Cobots
653 $a Sensor readings
653 $a Modified algorithm
653 $a Bug algorithm
655 7 $a Electronic books. $2 local $3 554714
690 $a 0548
690 $a 0800
690 $a 0771
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Washington State University. $b School of Engineering and Computer Science, Vancouver. $3 1464643
773 0 $t Masters Abstracts International $g 85-01.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30318041 $z click for full text (PQDT)