Navigating unknown underwater environments is a significant challenge for autonomous underwater vehicles (AUVs), especially those with torpedo-like shapes. Lacking a prior map, these vehicles rely on real-time sensor data for perception. Although online motion planning addresses this challenge, many existing methods are primarily tested on more maneuverable robots, such as multicopters and ground vehicles, and do not account for the unique kinematics of torpedo-shaped AUVs, such as limited lateral movement, or the need for 3D motion planning. In this paper, we propose an online motion planning system specifically designed for torpedo-shaped AUVs to navigate 3D underwater terrain without prior environmental knowledge. The system employs a receding horizon planning framework to ensure safe navigation by replanning the trajectory when collisions are detected or the planning horizon is reached. For trajectory generation, a search-based method is used and utilizes a 3D Dubins curve heuristic to guide the generation of an optimal 3D trajectory that adheres to the AUV’s kinematic constraints. To further enhance safety and smoothness, gradient-based optimization is applied to refine the trajectory. Experiments in simulated environments validate the proposed method, demonstrating its ability to generate safe trajectories for AUVs in complex and unknown environments. We release our code as an open-source package1.

For effective image segmentation, it is crucial to employ constraints informed by prior knowledge about the characteristics of the areas to be segmented to yield favorable segmentation outcomes. However, the existing methods have primarily focused on priors of specific properties or shapes, lacking consideration of the general global shape similarity from a Contour Flow perspective. Furthermore, naturally integrating this contour flow prior image segmentation model into the activation functions of deep convolutional networks through mathematical methods is currently unexplored. In this paper, we establish a concept of global shape similarity based on the premise that two shapes exhibit comparable contours. Furthermore, we mathematically derive a contour flow constraint that ensures the preservation of global shape similarity. We propose two implementations to integrate the constraint with deep neural networks. Firstly, the constraint is converted to a shape loss, which can be seamlessly incorporated into the training phase for any learning-based segmentation framework. Secondly, we add the constraint into a variational segmentation model and derive its iterative schemes for solution. The scheme is then unrolled to get the architecture of the proposed CFSSnet. Validation experiments on diverse datasets are conducted on classic benchmark deep network segmentation models. The results indicate a great improvement in segmentation accuracy and shape similarity for the proposed shape loss, showcasing the general adaptability of the proposed loss term regardless of specific network architectures. CFSSnet shows robustness in segmenting noise-contaminated images, and inherent capability to preserve global shape similarity.