SIMPNet: Spatial-Informed Motion Planning Network

TL;DR

SIMPNet introduces a graph neural network-based sampling heuristic that encodes workspace and manipulator structure to improve motion planning efficiency in complex environments.

Contribution

It presents a novel GNN-based informed sampling method for motion planning that outperforms traditional heuristics in high-dimensional spaces.

Findings

SIMPNet achieves higher success rates in complex environments.

It reduces planning time compared to baseline methods.

The approach effectively encodes workspace and kinematic information.

Abstract

Current robotic manipulators require fast and efficient motion-planning algorithms to operate in cluttered environments. State-of-the-art sampling-based motion planners struggle to scale to high-dimensional configuration spaces and are inefficient in complex environments. This inefficiency arises because these planners utilize either uniform or hand-crafted sampling heuristics within the configuration space. To address these challenges, we present the Spatial-informed Motion Planning Network (SIMPNet). SIMPNet consists of a stochastic graph neural network (GNN)-based sampling heuristic for informed sampling within the configuration space. The sampling heuristic of SIMPNet encodes the workspace embedding into the configuration space through a cross-attention mechanism. It encodes the manipulator's kinematic structure into a graph, which is used to generate informed samples within the framework of sampling-based motion planning algorithms. We have evaluated the performance of SIMPNet using a UR5e robotic manipulator operating within simple and complex workspaces, comparing it against baseline state-of-the-art motion planners. The evaluation results show the effectiveness and advantages of the proposed planner compared to the baseline planners.