Sampling-based path planning algorithms, such as Rapidly-exploring Random Tree (RRT), are widely used for motion planning in high degree-of-freedom robotic systems due to their efficiency in exploring high-dimensional spaces. However, traditional methods rely on binary collision detection, which only determines whether a sampled configuration is in a collision without quantifying its safety, often resulting in trajectories that are overly close to obstacles and reducing planning success rates, especially in complex environments with narrow passages. To address this issue, we propose Safety-Guided RRT* (SG-RRT*), which integrates a quantitative safety metric based on signed distance functions (SDFs) with a hyperoctant sampling strategy, enabling the planner to prioritize safer configurations and steer tree expansion toward collision-free regions. This approach significantly improves path planning success rates while generating safer trajectories with greater clearance from obstacles. Extensive simulations and real-world experiments demonstrate that SG-RRT* outperforms state-of-the-art methods, including RRT*, Informed-RRT*, TRRT, and Bi-TRRT, by achieving higher success rates and reducing collision risks, with only a slight increase in trajectory length.

This paper presents a Hybrid-feature Geometric Descriptor Registration (HGDR) framework to improve point cloud registration accuracy in challenging architectural environments. Traditional methods relying on single feature types—points, lines, or planes—often encounter limitations in accuracy and robustness, especially in noise-prone or low-overlap settings. In contrast, HGDR simultaneously extracts corner, line, and plane features in one step using a lightweight network and a novel Local Curvature Space Descriptor (LCSD). A two-layer graph matching algorithm leverages global geometric relations to improve correspondence reliability, followed by a two-stage non-iterative transformation estimation that avoids initialization sensitivity. HGDR was rigorously evaluated on indoor and outdoor datasets, outperforming eleven state-of-the-art rotation, translation, and RMSE accuracy methods, including uniform-feature-based and learning-based approaches. It achieved centimeter-level precision generally, with statistically lower error (mean rotation error of 0.79° and translation error of 0.173m) and significantly lower standard deviations than the competing methods. The improved robustness of the hybrid feature method is further validated through ablation experiments conducted under various mixed feature conditions.

This study draws inspiration from the locomotion and adaptability of aquatic snakes to develop an innovative soft-bodied, hydraulic-driven untethered underwater snake robot “BaiLong.” The robot consists of a segmented soft structure and embeds actuation, control, and power modules in the head. Featuring the self-shape perception capability, it leverages an online iterative learning control method to effectively mitigate body shape deformation errors and attain precise gait movements. As a result, the soft robot has achieved movements emulating the serpentine motion of real snakes with locomotion consistency equivalent to rigid robots. Extensive experiments in both artificial and natural aquatic environments have presented improved swimming speed among soft snakes with promising turning agility, and revealed the gait parameter influence on the linear velocity described by a near-constant Strouhal number. The reported investigation sufficiently demonstrates the swimming feasibility and performance of underwater soft snake robots and significantly advances their capabilities for long-range applications.

This letter proposes Depth-Aided Incremental Image Stitching (DAIIS), an algorithm tailored for low-altitude unmanned aerial vehicle (UAV) images in urban environments. DAIIS integrates single-view depth estimation and plane fitting to identify ground feature points, enabling precise computation of transformation matrices and significantly reducing stitching errors caused by large depth variations. Unlike traditional methods, DAIIS effectively addresses misalignments and artifacts, producing seamless and accurate stitched images that closely resemble the real scene. Evaluated on three distinct datasets, DAIIS demonstrated superior performance, achieving a 70% reduction in mean square error (mse) and notable improvements in peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) compared to other methods. The results validate DAIIS as a robust solution for high-precision image stitching, enhancing accuracy and visual quality in challenging low-altitude scenarios, which offers reliable and high-quality results for various urban applications.

This article presents a multi-sensor calibration and fusion work for a dense reconstruction and colorization system. Unlike previous research, it explicitly addresses the edge effect in LiDAR measurements in both calibration and sensor fusion. The proposed calibration method can accomplish LiDAR-motor and LiDAR-camera calibration at a one-time experiment. The feature extraction algorithm for the point cloud automatically eliminates the edge-effect-induced noises in the calibration process. The experiment results show that it can achieve even better results than work without edge effect elimination in terms of calibration errors. As a result, it has achieved a calibration accuracy superior to previous researches. The system is testified by an indoor scene, which shows a desirable color point cloud generation with dense and clean data.

Auto-exploration is a task for self-driving robots to explore unknown environments, which becomes much complicated when they move on irregular outdoor terrains. To improve the situation, a new frontier-based exploration algorithm is presented in this paper. It starts from original 3D cloud points of the environment to analyze the traversability of the scanned area, and further provides a reachability map to mark all map grid cells as reachable, dangerous or unknown. Frontier candidates are obtained from the reachable map, then clustered and reduced using an improved K-means. Finally, the target of next exploration step is selected from the frontiers left by evaluating their travel cost. The algorithm is validated on an irregular outdoor terrain and shows the capability for a field robot to explore on an irregular terrain.

This article introduces a frequency domain approach for designing a data-driven controller. Unlike the model-based control, it does not require a parametric model but only needs the input-output open loop test data for the purpose of spectral analysis. The resulting discrete frequency response is then utilized to give an optimal parameter design for a pre-defined structure controller, which is formulated as a non-convex optimization problem. The design approach is applied on a case study, an end-driving flexible beam with highly nonlinear dynamics. Simulations in finite element analysis (FEA) software show that the proposed controller can achieve similar performance as a model-based mixed sensitivity controller under the same design setup. In addition, it has the design flexibility of choosing the structure of the designated controller, which can largely decrease the controller order for implementation merits.