東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Self-Collision Detection and Avoidan...

Sabetian, Saba.

FindBook

Google Book

Amazon

博客來

Self-Collision Detection and Avoidance for Dual-Arm Concentric Tube Robots.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Self-Collision Detection and Avoidance for Dual-Arm Concentric Tube Robots./
作者:	Sabetian, Saba.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	101 p.
附註:	Source: Masters Abstracts International, Volume: 81-04.
Contained By:	Masters Abstracts International81-04.
標題:	Biomedical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13427123
ISBN:	9781085758611

Self-Collision Detection and Avoidance for Dual-Arm Concentric Tube Robots.
Sabetian, Saba.

Self-Collision Detection and Avoidance for Dual-Arm Concentric Tube Robots. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 101 p.

Source: Masters Abstracts International, Volume: 81-04.

Thesis (M.A.S.)--University of Toronto (Canada), 2019.

This item must not be sold to any third party vendors.

Recent studies on concentric tube robots (CTRs) have shown that they are well-suited for minimally invasive surgeries. However, typical surgical procedures require the use of multiple tools simultaneously which has led to the development of dual-arm CTRs that are susceptible to self-collision. In this research, a real-time closed-loop control system for dual-arm CTRs is proposed which detects and avoids inter-collision between arms along their entire body. The proposed controller is developed with three prioritized tasks; physical constraints, self-collision avoidance, and end-effector tracking. To avoid self-collision, the minimum distance between the manipulators in the Cartesian space is kept at a pre-set threshold. The experimental results demonstrated the efficacy of the controller in handling self-collision to reach 97.69% of all possible pairs of points in the overlapping workspace of two arms. Further, the maximum RMS tip position error in collision cases, 0.278 mm, was less than 1% of the total robot's length.

ISBN: 9781085758611Subjects--Topical Terms:

535387
Biomedical engineering.
Subjects--Index Terms:

Collision avoidance

Self-Collision Detection and Avoidance for Dual-Arm Concentric Tube Robots.
LDR:02313nmm a2200421 4500 001 2272358
005 20201105110030.5
008 220629s2019 ||||||||||||||||| ||eng d
020 $a 9781085758611
035 $a (MiAaPQ)AAI13427123
035 $a AAI13427123
040 $a MiAaPQ $c MiAaPQ
100 1 $a Sabetian, Saba. $3 3549791
245 1 0 $a Self-Collision Detection and Avoidance for Dual-Arm Concentric Tube Robots.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 101 p.
500 $a Source: Masters Abstracts International, Volume: 81-04.
500 $a Advisor: Drake, James;Diller, Eric.
502 $a Thesis (M.A.S.)--University of Toronto (Canada), 2019.
506 $a This item must not be sold to any third party vendors.
520 $a Recent studies on concentric tube robots (CTRs) have shown that they are well-suited for minimally invasive surgeries. However, typical surgical procedures require the use of multiple tools simultaneously which has led to the development of dual-arm CTRs that are susceptible to self-collision. In this research, a real-time closed-loop control system for dual-arm CTRs is proposed which detects and avoids inter-collision between arms along their entire body. The proposed controller is developed with three prioritized tasks; physical constraints, self-collision avoidance, and end-effector tracking. To avoid self-collision, the minimum distance between the manipulators in the Cartesian space is kept at a pre-set threshold. The experimental results demonstrated the efficacy of the controller in handling self-collision to reach 97.69% of all possible pairs of points in the overlapping workspace of two arms. Further, the maximum RMS tip position error in collision cases, 0.278 mm, was less than 1% of the total robot's length.
590 $a School code: 0779.
650 4 $a Biomedical engineering. $3 535387
650 4 $a Robotics. $3 519753
650 4 $a Health sciences. $3 3168359
650 4 $a Surgery. $3 707153
650 4 $a Artificial intelligence. $3 516317
653 $a Collision avoidance
653 $a Concentric tube robots
653 $a Medical robotics
653 $a Dual-arm CTRs
653 $a Self-collision
653 $a Physical constraints
653 $a End-effector tracking
690 $a 0541
690 $a 0771
690 $a 0576
690 $a 0566
690 $a 0800
710 2 $a University of Toronto (Canada). $b Biomedical Engineering. $3 2101094
773 0 $t Masters Abstracts International $g 81-04.
790 $a 0779
791 $a M.A.S.
792 $a 2019
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13427123