A sampling-based optimized algorithm for task-constrained motion planning

We consider a motion planning problem with task space constraints in a complex environment for redundant manipulators. For this problem, we propose a motion planning algorithm that combines kinematics control with rapidly exploring random sampling methods. Meanwhile, we introduce an optimization str...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:International journal of advanced robotic systems Jg. 16; H. 3
Hauptverfasser: Mi, Kai, Zhang, Haojian, Zheng, Jun, Hu, Jianhua, Zhuang, Dengxiang, Wang, Yunkuan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London, England SAGE Publications 01.05.2019
Sage Publications Ltd
SAGE Publishing
Schlagworte:
Algorithms
Asymptotic properties
Constraints
Dynamic programming
Feasibility
Kinematics
Motion planning
Optimization
Random sampling
Sampling methods
Task space
Tracking errors
path planning
Redundant manipulators
task space constraints
sampling based
asymptotically optimization
ISSN:1729-8806, 1729-8814
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:We consider a motion planning problem with task space constraints in a complex environment for redundant manipulators. For this problem, we propose a motion planning algorithm that combines kinematics control with rapidly exploring random sampling methods. Meanwhile, we introduce an optimization structure similar to dynamic programming into the algorithm. The proposed algorithm can generate an asymptotically optimized smooth path in joint space, which continuously satisfies task space constraints and avoids obstacles. We have confirmed that the proposed algorithm is probabilistically complete and asymptotically optimized. Finally, we conduct multiple experiments with path length and tracking error as optimization targets and the planning results reflect the optimization effect of the algorithm.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1729-8806
1729-8814
DOI:10.1177/1729881419847378