KG-Planner: Knowledge-Informed Graph Neural Planning for Collaborative Manipulators
IEEE Transactions on Automation Science and Engineering(2024)
Abstract
This paper presents a novel knowledge-informed graph neural planner
(KG-Planner) to address the challenge of efficiently planning collision-free
motions for robots in high-dimensional spaces, considering both static and
dynamic environments involving humans. Unlike traditional motion planners that
struggle with finding a balance between efficiency and optimality, the
KG-Planner takes a different approach. Instead of relying solely on a neural
network or imitating the motions of an oracle planner, our KG-Planner
integrates explicit physical knowledge from the workspace. The integration of
knowledge has two key aspects: (1) we present an approach to design a graph
that can comprehensively model the workspace's compositional structure. The
designed graph explicitly incorporates critical elements such as robot joints,
obstacles, and their interconnections. This representation allows us to capture
the intricate relationships between these elements. (2) We train a Graph Neural
Network (GNN) that excels at generating nearly optimal robot motions. In
particular, the GNN employs a layer-wise propagation rule to facilitate the
exchange and update of information among workspace elements based on their
connections. This propagation emphasizes the influence of these elements
throughout the planning process. To validate the efficacy and efficiency of our
KG-Planner, we conduct extensive experiments in both static and dynamic
environments. These experiments include scenarios with and without human
workers. The results of our approach are compared against existing methods,
showcasing the superior performance of the KG-Planner. A short video
introduction of this work is available (video link provided in the paper).
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Key words
Motion planning,graph neural network,collaborative robot,human-robot collaboration
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