Learning the Metric of Task Constraint Manifolds for Constrained Motion Planning

Finding feasible motion for robots with high-dimensional configuration space is a fundamental problem in robotics. Sampling-based motion planning algorithms have been shown to be effective for these high-dimensional systems. However, robots are often subject to task constraints (e.g., keeping a glas...

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Veröffentlicht in:Electronics (Basel) Jg. 7; H. 12; S. 395
Hauptverfasser: Zha, Fusheng, Liu, Yizhou, Guo, Wei, Wang, Pengfei, Li, Mantian, Wang, Xin, Li, Jingxuan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.12.2018
Schlagworte:
Algorithms
Approximation
Constraint modelling
Cooperation
Efficiency
Kinematics
Machine learning
Manifolds (mathematics)
Motion planning
Planning
Robot dynamics
Robotics
Robots
Sampling
System effectiveness
ISSN:2079-9292, 2079-9292
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Zusammenfassung:Finding feasible motion for robots with high-dimensional configuration space is a fundamental problem in robotics. Sampling-based motion planning algorithms have been shown to be effective for these high-dimensional systems. However, robots are often subject to task constraints (e.g., keeping a glass of water upright, opening doors and coordinating operation with dual manipulators), which introduce significant challenges to sampling-based motion planners. In this work, we introduce a method to establish approximate model for constraint manifolds, and to compute an approximate metric for constraint manifolds. The manifold metric is combined with motion planning methods based on projection operations, which greatly improves the efficiency and success rate of motion planning tasks under constraints. The proposed method Approximate Graph-based Constrained Bi-direction Rapidly Exploring Tree (AG-CBiRRT), which improves upon CBiRRT, and CBiRRT were tested on several task constraints, highlighting the benefits of our approach for constrained motion planning tasks.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics7120395