The trade-off between computation time and path optimality is a key consideration in motion planning algorithms. While classical sampling based algorithms fall short of computational efficiency in high dimensional planning, learning based methods have shown great potential in achieving time efficient and optimal motion planning. The SOTA learning based motion planning algorithms utilize paths generated by sampling based methods as expert supervision data and train networks via regression techniques. However, these methods often overlook the important multimodal property of the optimal paths in the training set, making them incapable of finding good paths in some scenarios. In this paper, we propose a Multimodal Neuron Planner (MNP) based on the mixture density networks that explicitly takes into account the multimodality of the training data and simultaneously achieves time efficiency and path optimality. For environments represented by a point cloud, MNP first efficiently compresses the point cloud into a latent vector by encoding networks that are suitable for processing point clouds. We then design multimodal planning networks which enables MNP to learn and predict multiple optimal solutions. Simulation results show that our method outperforms SOTA learning based method MPNet and advanced sampling based methods IRRT* and BIT*.

翻译：在运动规划算法中，计算时间与路径最优性之间的权衡是一个关键考量。尽管经典的基于采样的算法在高维规划中计算效率不足，但基于学习的方法在实现高效时间与最优运动规划方面展现出巨大潜力。当前最优的学习型运动规划算法利用基于采样的方法生成的路径作为专家监督数据，并通过回归技术训练网络。然而，这些方法常忽略训练集中最优路径的重要多模态特性，导致在某些场景下无法找到优秀路径。本文提出了一种基于混合密度网络的多模态神经元规划器（MNP），该规划器明确考虑训练数据的多模态性，同时实现时间效率与路径最优性。对于以点云表示的环境，MNP首先通过适合处理点云的编码网络将点云高效压缩为潜在向量，随后设计多模态规划网络，使MNP能够学习并预测多个最优解。仿真结果表明，我们的方法优于当前最优的学习型方法MPNet以及先进的基于采样的方法IRRT*和BIT*。