论文标题
使用图神经网络减少基于抽样的运动计划的碰撞检查
Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks
论文作者
论文摘要
基于抽样的运动计划是在机器人技术中的一种流行方法，用于在连续配置空间中找到路径。在此过程中，与障碍物检查碰撞是主要的计算瓶颈。我们提出了新的基于学习的方法来减少碰撞检查以通过训练图神经网络（GNN）加速运动计划，以执行路径探索和路径平滑。给定从批处理采样产生的随机几何图（RGGS），路径探索组件迭代可预测无碰撞的边缘以优先考虑其勘探。然后，路径平滑成分优化了从勘探阶段获得的路径。这些方法受益于从RGG通过批处理采样从RGG捕获几何模式的能力，并更好地概括到看不见的环境。实验结果表明，在挑战高维运动计划任务中，学习的组件可以显着降低碰撞检查并提高整体计划效率。
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.
