Motion Planning for Closed-Chain Constraints Based on Probabilistic Roadmap With Improved Connectivity

Multiarm systems can perform complex and difficult tasks, such as manipulating a heavy or large object, that cannot be accomplished by a single manipulator owing to workspace and payload limitations. However, the motion planning problem for performing such a task is challenging because of the need t...

Full description

Saved in:
Bibliographic Details
Published in:IEEE/ASME transactions on mechatronics 2022-08, Vol.27 (4), p.2035-2043
Main Authors: Jang, Keunwoo, Baek, Jiyeong, Park, Suhan, Park, Jaeheung
Format: Article
Language:English
Subjects:
Chains
Closed-chain constraint
Collision avoidance
Configurations
End effectors
Inverse kinematics
inverse kinematics (IK)
Kinematics
Manifolds
Motion planning
Nodes
Planning
Probabilistic logic
probabilistic roadmap (PRM)
Probability theory
Sampling
Task analysis
Task complexity
Trajectory
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Multiarm systems can perform complex and difficult tasks, such as manipulating a heavy or large object, that cannot be accomplished by a single manipulator owing to workspace and payload limitations. However, the motion planning problem for performing such a task is challenging because of the need to consider the closed-chain constraint. This article proposes an efficient motion planner that considers the closed-chain constraint based on a probabilistic roadmap. The proposed planner utilizes the following strategies. First, the planner obtains feasible nodes by randomly sampling the object pose, followed by computing the inverse kinematics (IK) solution of the multiarm. This can directly find a collision-free node satisfying the closed-chain constraint. Second, the planner repeatedly updates the new IK solution of the multiarm for the start and goal object pose. The IK solution is computed as close as possible to the joint configuration of the neighbor node. Consequently, the planner is more efficient than the existing methods that generate a node by sampling the joint configuration with projection method and have one pair of the start and goal node. Therefore, the planner can efficiently compute the path for object manipulation using a multiarm under a closed-chain constraint. The effectiveness of the proposed planner is validated by comparison with the existing planners in several scenarios. A video clip of the experiments in various scenarios can be found at https://youtu.be/PR9aFf3juu4 .
ISSN:1083-4435
1941-014X
DOI:10.1109/TMECH.2022.3175260