Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks-猫眼课题宝

DOI：

10.48550/arxiv.2210.08864

发表时间：

2022-10

期刊：

ArXiv

影响因子：

通讯作者：

中科院分区：

其他

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作者：

Chen-Ping Yu;Sicun Gao

基于采样的运动规划是机器人技术中在连续配置空间中寻找路径的流行方法。碰撞检测是该过程中的主要计算瓶颈。我们提出了新的基于学习的方法，通过训练执行路径探索和路径平滑的图神经网络（GNNs）来减少碰撞检查以加速运动规划。给定批量采样生成的随机几何图（RGG），路径探索组件迭代预测无冲突边以优先考虑它们的探索。路径平滑组件然后优化从探索阶段获得的路径。该方法受益于GNN通过批量采样从RGG捕获几何图案的能力，并更好地推广到不可见的环境。实验结果表明，学习组件可以显着减少碰撞检查，提高整体规划效率，在具有挑战性的高维运动规划任务。

Sampling-based motion planning is a popular approach in robotics for finding paths in continuous configuration spaces. Checking collision with obstacles is the major computational bottleneck in this process. We propose new learning-based methods for reducing collision checking to accelerate motion planning by training graph neural networks (GNNs) that perform path exploration and path smoothing. Given random geometric graphs (RGGs) generated from batch sampling, the path exploration component iteratively predicts collision-free edges to prioritize their exploration. The path smoothing component then optimizes paths obtained from the exploration stage. The methods benefit from the ability of GNNs of capturing geometric patterns from RGGs through batch sampling and generalize better to unseen environments. Experimental results show that the learned components can significantly reduce collision checking and improve overall planning efficiency in challenging high-dimensional motion planning tasks.

Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks

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