Accurately steering a robot to a target configuration is fundamental in engineering, yet remains challenging for nonholonomic mobile robots. Vector fields (VFs) provide a natural framework by specifying desired motion directions throughout the workspace and enabling direct integration with feedback control. However, most existing VF-based methods cannot explicitly generate trajectories satisfying curvature constraints. Actuator limits are therefore often enforced by input saturation, which may invalidate stability guarantees and degrade closed-loop performance when not considered in controller design. In addition, these methods usually ensure only asymptotic convergence without an explicit settling-time bound. To address these issues, we propose a generalized motion planning and control framework consisting of a finite-time curvature-constrained vector field (FT-C2VF) and a saturation-free control law. Depending on the motion objective, the framework drives the robot to the target configuration in finite time or through it periodically. First, the FT-C2VF is constructed using complementary gains to achieve finite-time convergence while ensuring that the curvature of its integral curves is continuous, bounded, and monotonically decreasing with the radial ratio. Second, an almost globally C1-smooth, saturation-free controller is developed to track the FT-C2VF without Jacobian information, while keeping all control inputs within prescribed actuator limits. Third, dynamical-systems analysis establishes almost-global finite-time stability of the target equilibrium. Numerical simulations show improved performance over representative VF-based methods, and outdoor experiments on an Ackermann-steered vehicle confirm the effectiveness and robustness of the proposed approach.
Zhou-Ru Xiao, Sha Luo, Yang Lu et al.· arXiv.org· 0 citations
Open-vocabulary 3D scene graph generation aims to predict 3D objects and their predicates beyond the annotated label space. Compared to closed-set 3D scene graph generation methods, the open-vocabulary approach is more general, practical, and less dependent on labor-intensive ground truth annotations. Existing open-vocabulary 3D scene graph generation methods rely on learning individual object and predicate features in the representation space while ignoring higher-level 3D scene representations, leading to overfitting and suboptimal performance. In this work, we propose a hyperbolic learning-based approach to address this problem by leveraging hyperbolic geometry to learn hierarchical 3D scene representations in the form of scene-region-instance, where the scene represents the complete 3D environment, a region contains related instances, and an instance corresponds to an individual object or predicate. Specifically, our method decomposes a 3D scene into a discrete hierarchy consisting of scene, region, and instance nodes, and embeds this hierarchy into a learned hyperbolic representation space. The learned hyperbolic embeddings are optimized in a bottom-up manner, where higher-level nodes are derived from their corresponding child nodes. The learned hierarchical 3D scene representations are incorporated as structural and semantic guidance for open-vocabulary 3D scene graph generation. We further observe that outliers in the form of erroneous hyperbolic embeddings can negatively impact hierarchical reasoning. To mitigate their negative impact, we present an enhancement strategy that learns an adaptive distance metric robust to the outliers over the learned hyperbolic representation space and subsequently improves overall performance. Extensive experiments on 3DSSG and ScanNet datasets demonstrate the effectiveness of our method in 3D scene graph generation under closed-set, open-vocabulary, and zero-shot settings.
Haoran Hou, Mingtao Feng, Qing Zhu et al.· IEEE Transactions on Pattern...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
