Sheng Bao

Rust removal on pipe surfaces is hazardous and labor-intensive, yet it is a prerequisite for inspection and recoating in maintenance. Existing solutions rely on industrial robots or specialized robots with limited obstacle-negotiation capability and cable entanglement risks. This work presents a Robotic Pipe Grinding System (RPGS) for straight pipes and focuses on the latest generation, RPGS-III. Driven by a single motor, RPGS-III integrates cutter self-rotation, revolution about the pipe axis, and axial translation through planetary-gear and bevel-gear transmissions. Passive mechanisms enable adaptation to different pipe diameters, obstacle negotiation, and dust containment during grinding. Kinematic and static analyses, motion simulations, and prototype experiments were conducted to validate the system, with feasibility demonstrated through in situ maintenance tests. The validated design demonstrates a practical solution for automated pipe maintenance in field environments, reducing actuation complexity while maintaining operational effectiveness.

To improve the adaptability and adhesion of wall-climbing robots on complex curved surfaces, a self-adaptive spherical magnetic wheel robot is proposed for inspecting three-dimensional variable-curvature structures. The robot employs a bilateral wheeled design with passive magnetic modules that automatically adjust to contact conditions, ensuring efficient adhesion without active control. A Halbach-array magnetic circuit further enhances adhesion without increasing size or weight. Simulations analyze the effect of swing angle on adhesion and determine the minimum adaptable curvature radius. Experiments show stable climbing on surfaces with radii of 100–350 mm, obstacle-crossing up to 7 mm, and a payload capacity of 16.63 kg. Compared with existing designs, the robot offers improved curvature adaptability and load capacity under similar size and weight constraints.

Dual-arm robots are widely used to cooperatively manipulate large, heavy objects that exceed single-arm payload limits. However, conventional dual-arm planners typically assume that the object is already positioned within the reachable and graspable workspace of both end-effectors. When the object lies outside this workspace or in an awkward orientation, such planners often fail or require additional hardware (e.g. a mobile base or overhead cranes) to reposition the robot or the object first. To address this limitation, we present 1D2L (One-Arm Drag and Two-Arm Lift), a task-and-motion planning framework for dual-arm tabletop manipulation. The framework extends manipulation capability by first dragging the object with one arm into a “liftable zone” on a horizontal planar surface, and then performing a cooperative two-arm lift. The liftable zone contains pre-computed set of tabletop poses derived from kinematic reachability, grasp feasibility, and dynamic stability requirements for the subsequent dual-arm lift. Beyond lifting, 1D2L provides as a general paradigm for large-object tabletop manipulation, where one-arm dragging for pre-positioning to enable subsequent two-arm tasks. We validate the 1D2L framework experimentally, demonstrating a successful framework that enables robots to handle large or heavy objects that are initially unreachable.

Clustered-object environments challenge robotic grasp planning and implementation mainly for two reasons: (i) the limited inter-object clearance leaves insufficient space for conventional gripper fingers to approach and wrap the target object without collisions, and (ii) perception-induced position uncertainty makes it difficult to place the fingers precisely into such narrow spaces. Thus, in this letter, we propose a novel robotic finger based on pin-array mechanisms to passively adapt to object clearance between adjacent objects and tolerate pose uncertainty. The tiny pins can be inserted into constrained spaces and thus construct stable grasping pairs. Unlike conventional pin-array mechanisms used in gripper design, the proposed approach utilizes the pin-array for finger design, enabling a gripper equipped with a pair of these fingers to pick objects in significantly narrower spaces than conventional grippers. This gripper also inherits the advantages of the pin-array mechanisms to facilitate shape and error adaptation. Furthermore, we conducted finite element analysis (FEA) to validate the structural design and explore the miniaturization limit, and explain the planning method for autonomous operation. The experiments quantitatively explored the adaptable clearance and validated the feasibility and robustness in real scenarios.

Real-time and accurate state estimation and map reconstruction are crucial for unmanned systems. However, existing LiDAR-inertial-visual odometry (LIVO) methods typically rely on short-term data association, making it difficult to maintain stable operation in LiDAR or visual degenerated environments. In this work, we present Voxel-LIVO, a precise and robust LiDAR-inertial-visual odometry and mapping system that leverages a unified adaptive voxel map for short-term, midterm and long-term data associations. For LiDAR-inertial-visual odometry, we employ iterated error-state Kalman filter (IESKF) to fuse LiDAR, inertial, and visual measurements for efficient state estimation. To enhance the precision of image alignment, we propose a LiDAR map assisted visual patch association method (LM-VPA), which employs LiDAR planar features to perform affine transformations for image patches. For local mapping, we propose a novel sequential LiDAR-visual local bundle adjustment (BA) approach, which facilitates mid-term data association to further enhance the precision of the local map and mitigate state drift. To maintain accuracy while minimizing memory overhead, we propose a hybrid map-management scheme that combines a keyframe-based sparse long-term voxel map with a densely updated sliding-window voxel map. We conducted extensive experiments on public benchmark datasets and our private datasets, and the results demonstrate that our proposed system significantly outperforms other state-of-the-art odometry systems in terms of accuracy and robustness, particularly under highly degenerated environments (see attached video).

Uncertainty awareness in geometric observations is crucial for LiDAR-based simultaneous localization and mapping (SLAM) since it determines the direction of optimization and ultimately affects the accuracy of localization. In this paper, we present a tightly-coupled LiDAR-inertial odometry system with geometric uncertainty modeling framework for accurate and robust state estimation. The proposed LIO system is designed by an Iterative Error State Kalman Filter (IESKF) to effectively fuse LiDAR and IMU measurement, and employs the voxel structure to query and update LiDAR features. To observe the reliability of geometric measurements, we propose novel uncertainty models of LiDAR point, line and plane features, and apply them to construct point-to-line and point-to-plane constraints. In the maximization of posterior state estimation, we propagate the uncertainties of geometric constraints to the iterative error state Kalman filter, further consider the weights of geometric constraints, and enrich the comprehensiveness of the geometric residuals. To reduce the memory consumption of the system, we propose a cumulative update method with uncertainty parameters to avoid repeated parameter computations. We extensively evaluate our method in diverse public datasets. The results validate the high precision and robustness of the proposed method when compared to other state-of-the-art methods in challenge scenes with high perceptual uncertainties.

The accurate estimation of the end-effector load force is essential in dynamic robotic scenarios, especially when the end-effector payload varies, to ensure safe and stable physical interaction among humans, robots, and environments. Currently, most applications still rely on payload calibration schemes, but existing calibration techniques often struggle to balance efficiency and accuracy. Moreover, current-based payload estimation methods, which are a commonly used and low-cost technique, face practical challenges such as non-negligible noise. To handle these issues, we propose a sensorless scheme based on a modified disturbance Kalman filter for accurately estimating the load force exerted on robots. Specifically, we introduce the dynamic model of robots that incorporates the nonlinear friction related to velocity and load. Subsequently, a generalized disturbance observer for the robot dynamics is adopted to avoid the measurement noise of joint acceleration. Considering the influence of friction and velocity on the noise parameters in the Kalman filter, a variable-parameter noise model is established. Finally, experimental results demonstrate that the proposed method achieves better performance in terms of accuracy, response, and overshoot suppression compared to the existing methods.

Accurate dynamic model is essential for the model-based control of robotic systems. However, on the one hand, the nonlinearity of the friction is seldom treated in robot dynamics. On the other hand, few of the previous studies reasonably balance the calculation time-consuming and the quality for the excitation trajectory optimization. To address these challenges, this article gives a Lie-theory-based dynamic modeling scheme of multi-degree-of-freedom (DoF) serial robots involving nonlinear friction and excitation trajectory optimization. First, we introduce two coefficients to describe the Stribeck characteristics of Coulomb and static friction and consider the dependency of friction on load torque, so as to propose an improved Stribeck friction model. Whereafter, the improved friction model is simplified in a no-load scenario, a novel nonlinear dynamic model is linearized to capture the features of viscous friction across the entire velocity range. Additionally, a new optimization algorithm of excitation trajectories is presented considering the benefits of three different optimization criteria to design the optimal excitation trajectory. On the basis of the above, we retrieve a feasible dynamic parameter set of serial robots through the hybrid least square algorithm. Finally, our research is supported by simulation and experimental analyses of different combinations on the seven-DoF Franka Emika robot. The results show that the proposed friction has better accuracy performance, and the modified optimization algorithm can reduce the overall time required for the optimization process while maintaining the quality of the identification results.

In this letter, we present an innovative, tightly-coupled LiDAR-Visual-Inertial Odometry and Mapping framework, termed LVIO-Fusion, which achives robust and precise state estimation and map construction in environments characterized by LiDAR-degenerated and texture-less. The LVIO-Fusion is comprised of two subsystems: a LiDAR-inertial odometry (LIO) and visual-inertial odometry (VIO). The LIO subsystem employs a dynamic voxel mapping method by utilizing a Hash table and octrees for building and updating point cloud map directly and efficiently. The map points are directly projected to images to build the optical flow matching, which couples LiDAR and camera measurements in a deeper level. Then, the VIO subsystem proposes a coarse-to-fine state estimation that minimizes the frame-to-frame reprojection errors and photometric errors to align images. Furthermore, the VIO subsystem leverages online photometric calibration to acquire the true radiometric information from the environment, consequently refining the precision of the system. We extensively evaluate our method in challenge environments. The results validate the high accuracy and robustness of our method when compared to other state-of-the-art methods in LiDAR-degenerated and texture-less scenarios.