SIMPNet: Spatial-Informed Motion Planning Network
<|reference_start|>SIMPNet: Spatial-Informed Motion Planning Network: 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.<|reference_end|>
@article{soleymanzadeh2024simpnet:,
    title={SIMPNet: Spatial-Informed Motion Planning Network},
    author={Davood Soleymanzadeh, Xiao Liang, Minghui Zheng},
    journal={arXiv preprint arXiv:2408.12831},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.12831},
    primaryClass={cs.RO cs.SY eess.SY}
}