A Cosine Similarity Based Multitarget Path Planning Algorithm for Cable-Driven Manipulators

Many path planning algorithms are proposed and employed for cable-driven manipulators (CDMs). However, most of them only consider single-target-point tasks. For multitarget-point tasks, CDMs need to repeat the planning and following of single point tasks. This is feasible but not optimal in terms of...

Full description

Saved in:
Bibliographic Details
Published in:IEEE/ASME transactions on mechatronics Vol. 30; no. 3; pp. 2379 - 2388
Main Authors: Zhang, Dong, Gai, Yan, Ju, Renjie, Zhou, MengChu, Cao, Zhengcai
Format: Journal Article
Language:English
Published: New York IEEE 01.06.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
A algorithm
Algorithms
cable-driven manipulator
Cables
Cost function
Costs
Energy consumption
Heuristic algorithms
heuristic search
Kinematics
Mechatronics
multitarget points
Path planning
Planning
Probabilistic logic
rapidly exploring random tree algorithm Cosine similarity
Similarity theory
Symbols
ISSN:1083-4435, 1941-014X
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Many path planning algorithms are proposed and employed for cable-driven manipulators (CDMs). However, most of them only consider single-target-point tasks. For multitarget-point tasks, CDMs need to repeat the planning and following of single point tasks. This is feasible but not optimal in terms of the distance and time needed by CDMs to complete such tasks. To solve this problem, this work designs a novel two-stage multitarget-point path planning (MPP) method. In the first stage, an improved rapidly exploring random tree (RRT)-A* algorithm that considers CDMs' features is used to preplan passable paths between each target and a start point. In the second one, in order to avoid CDM's repetitively moving along similar preplanned paths, a cosine similarity theory is used, for the first time, to integrate these paths. Furthermore, an indicator named path cost is defined to evaluate paths. This indicator takes into account CDMs' constraints, paths' lengths, and energy consumption. Simulations are conducted to compare MPP with some classical and recently developed algorithms. The results shows that it well outperform them in terms of path length and tracking time. Furthermore, the proposed method is verified by experiments in a 17 degrees-of-freedom CDM prototype.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1083-4435
1941-014X
DOI:10.1109/TMECH.2024.3502317