Learning-Based Motion Planning Leveraging Multivariate Deep Evidential Regression

Learning-based motion planning methods have shown significant promise in enhancing the efficiency of traditional algorithms. However, they often face performance degradation in novel environments with drastic scene changes due to the limited generalization ability of deep neural networks (DNNs). This paper introduces a confidence-driven motion planning network (CDMPNet), comprising a feature extraction autoencoder and a confidence-driven sampling network (CDSNet). The autoencoder compresses point clouds into latent vectors. The CDSNet is a closed-form continuous-time neural network, which predicts hyperparameters of an evidential distribution over the subsequent state's mean and covariance for robot configuration sampling. We also present a CDMPNet-based neural planner and a CDMPNet-guided RRTConnect algorithm. Simulations and ablation studies are conducted on 2-D, 3-D, and 7-D planning tasks to validate the generalization ability of our method. Furthermore, we transfer the approach to a 7-DOF Sawyer robotic arm to demonstrate the potential for real-world deployment.