Lu Zhou

Most of the existing LiDAR-inertial odometry (LIO) methods are based on frame-to-map constraints, which may lead to inconsistency in state estimation. Approaches relying on frame-to-frame could address the inconsistency, but easily fail during association loss. In this article, HCF-LIO, a LIO algorithm based on a voxel-map-based hybrid constraint framework is proposed. The approach synergistically integrates single-frame absolute pose constraints (frame-to-map) with multiframe relative constraints (frame-to-frame) within a filtering architecture, effectively balancing short-term accuracy and long-term consistency. In addition, an implicit multiframe constraint approach based on probabilistic voxel map enables plane feature association without explicit tracking. Furthermore, a secondary motion compensation module is implemented after state update, enhancing the accuracy of the LiDAR point cloud map and the quality of multiframe association. To evaluate the proposed framework, extensive experiments and ablation studies are conducted on public datasets and private self-collected dataset. The experimental results demonstrate that HCF-LIO outperforms the state-of-the-art approaches in terms of accuracy and consistency while maintaining real-time operation.

This paper proposes a novel multisensor fusion ground SLAM system that integrates LiDAR, visual, IMU, and wheel encoder data to enhance localization and mapping accuracy in complex environments. The proposed system consists of a LiDAR subsystem and a visual subsystem. To fully leverage the complementary advantages of LiDAR and visual sensors, the two subsystems provide each other with initial values for optimization. LiDAR supplies high-precision depth information to the visual subsystem, while the visual subsystem assists the LiDAR module in motion distortion correction, thereby enhancing the overall accuracy of state estimation. In addition, a novel plane extraction method is proposed based on adaptive dynamic distance thresholds and normal vector consistency verification. And the planar factor is introduced into the LiDAR subsystem for joint optimization. Constraining continuous planar structures (such as the ground and walls) reduces matching ambiguity, which further decreases local map noise and improves mapping accuracy. The proposed method has been extensively evaluated in complex urban campus scenarios, and the results show the effectiveness and accuracy of the algorithm.

3-D rotation of the biological cell is essential for localizing subcellular structure in various cell manipulations. However, visual detection errors of the subcellular structure affect the rotation performance in automatic cell manipulations. In this article, we enhance the robustness of out-of-plane rotation through visual detection evaluation. At first, we developed a kinematic model and simulation environment for cell rotation. We then predicted the presence and reliability of polar body detection using a posteriori probability analysis from 4 million simulations. We finally presented a robust out-of-plane rotation strategy based on polar body orientation prediction. Both simulation and experimental results with porcine oocytes demonstrated the efficiency and robustness of the proposed cell rotation method across various abnormal situations, improving the success rate of cell rotation. This work contributes a universal framework applicable to other vision-based cell rotation problems, offering a significant advancement in the field of cell manipulation.

Collaborative double boom cranes (CDBCs) play a crucial role in modern industries, offering superior hoisting capabilities and adaptability. However, the intricate dynamic characteristics of CDBCs, combined with demanding working requirements, pose significant challenges for control safety and efficiency. The existing control methods for CDBCs primarily focus on two-dimensional space and lack theoretical guarantees for rapid error convergence, which limits working efficiency. Furthermore, safety concerns arise when collision-free reference trajectories are unavailable or incomplete during dynamic operations. To this end, this article proposes a novel fixed-time tracking control method with obstacle avoidance and prescribed performance for three-dimensional (3-D) CDBCs. As the first collision-free tracking method for 3-D CDBCs, the proposed method stands as a noteworthy contribution aimed at improving safety, accuracy, and efficiency. By simultaneously considering pitch and rotation motions, the proposed method expands the working space and efficiency of 3-D CDBCs. Elaborately designed sliding surfaces ensure fixed-time convergence, thereby improving response speed. For safe complete trajectory tracking, the proposed method can limit transient tracking errors within a prescribed performance function, preventing collisions from unexpected errors. In scenarios with unavailable or incomplete safe reference trajectories, autonomous obstacle avoidance is achieved through potential function design, thereby enhancing operation safety. Additionally, a thorough closed-loop stability analysis is provided based on Lyapunov methods. Finally, experimental results on a built CDBC prototype offer validation for the tracking and obstacle avoidance performance of the proposed method under various working conditions.

A “planning and control” scheme with hysteresis compensation is developed in this article for a compliant grinding device (CGD) driven by a pneumatic actuator with asymmetric full-state constraints. First, in the planning part, we propose an improved hysteresis model and construct its inverse model through the modified inverse multiplicative structure method to deal with the hysteresis nonlinearity between air pressure and contact force in CGD. In the control part, a neural state observer (SO) is developed to observe the rate of change of air pressure by combining a neural network (NN) and an SO. By fusing an NN and a disturbance observer (DO), a neural DO is presented to observe external disturbances. Asymmetric full-state constraints are realized without the feasibility conditions (FCs) of the virtual controller by the fusion of the backstepping design process and nonlinear state-dependent transformations. It is proven that the air pressure closely tracks the planned desired air pressure, as the tracking error of the air pressure is uniformly ultimately bounded. Finally, hardware experiments show that the force control accuracy of the proposed method is within 2N, which achieves a satisfactory force control effect and verifies the effectiveness and robustness of the presented method.

In biological micromanipulation, cell penetration is a typical procedure that precedes cell injection or oocyte enucleation. During this procedure, cells usually undergo significant deformation, which leads to cell damage. In this paper, we focus on modeling and evaluating the cell penetration process to reduce cell deformation and stress, thereby reducing cell damage. Initially, a finite element model (FEM) is established to simulate the cell penetration process. The effectiveness of the model is then verified through visual detection and comparison of cell deformation with experimental data. Next, various mechanical responses are analyzed, considering the influence of parameters, such as the radius and shape of the injection micropipettes, material properties, and size of the cells. Finally, the relationship between the intracellular stress and the cell penetration depth of biological cells is obtained. The evaluation results will be applied to develop optimized operation plans, enhancing the efficiency and safety of the cell penetration process.

This paper proposes a “planning and control” scheme for a compliant mechanism (CM) based on a pneumatic actuator (PAC) with hysteresis nonlinearity and full-state constraints. In the planning part, a novel direct inverse model is presented to compensate for hysteresis nonlinearity, enabling more accurate planning of the desired air pressure based on the desired contact force. In the control part, by fusing fuzzy logic systems (FLSs) and an extended state observer (ESO), a fuzzy ESO is developed to observe the external disturbance and the rate of change of the air pressure. Additionally, the challenges in the controller design caused by full-state constraints are overcome by constructing barrier Lyapunov functions (BLFs). It is proved that all signals of the closed-loop system are bounded, and the tracking error of the air pressure can converge to a small neighborhood of the origin. Finally, the effectiveness and robustness of the proposed method are verified by hardware experiments, which also show that the root mean square errors of force control accuracies are within 2N, achieving satisfactory force control effects.

Traditional industrial robots often face challenges in achieving a perfectly polished surface on a workpiece because of their high mechanical rigidity. The active compliance force control device installed at the robotic arm’s end enables high-precision contact force control between the grinding tool and the workpiece. However, the complex hysteresis nonlinearity between cylinder air pressure and output force, as well as various random disturbances during the grinding process, can affect the accuracy of the contact force and potentially impact the grinding effect of the workpiece, even causing irreversible damage to the surface of the workpiece. Given the complex random variation of cylinder output force in the actual grinding process, a rate-dependent hysteresis model based on diagonal recurrent neural network and Pradtl–Ishlinskii models named dRNN-PI is designed to compensate for the complex nonlinear hysteresis of the cylinder and calculate the desired air pressure to maintain a steady contact force on the workpiece. The proxy-based sliding mode control (PSMC) is utilized to quickly track the desired air pressure without overshooting. This paper also proves the controller’s stability using the Lyapunov-based methods. Finally, the accuracy of the proposed hysteresis compensation model and the effectiveness and robustness of the PSMC are verified by experiment results.