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.

The nonlinearity and uncertainty of dynamics pose significant challenges to ensuring the tracking performance of joint trajectories, especially time-varying effects on the load and temperature. In this paper, we present an adaptive neural computed torque control scheme to improve the tracking accuracy of the robot joint towards various tasks, which is a novel semiparametric model including a parametric friction model and a nonparametric compensator trained with multiple radial basis function neural networks $(\rm{MRBFNNs})$ . Specifically, the asymmetric model considers velocity-, load-, and temperature-dependent friction phenomena. The computed torque controller integrates the sliding mode method and the proposed friction model to reduce the boundary layer of fluctuated disturbances and achieve globally asymptotic convergence. MRBFNNs are trained separately to further compensate for the unmodeled nonlinearity and parameter uncertainty in real time during the trajectory tracking process. The comparative experiments were carried out on a robot joint, validating that our asymmetric model significantly improves correspondence to reality in terms of friction; the proposed control strategy exhibits the superior tracking performance of joints with variable payloads.

This article presents a wearable soft robotic supporter with the function of changeable stiffness for supporting human upper limbs. We employ chain mails as the jamming media to achieve a low-weight design and provide low resistance for free motion and high stiffness for holding arms. Structurally, two stacked chain mails are enveloped by a bellow-like airbag, which has two embedded belts for anchoring. The bellow-like profile increases the flexibility at the normal pressure, making it comfortable to wear. In this article, we present the modeling methods for the simplified analysis of mobility, propose the computer-aided design (CAD) model generator of chain mail to automate the modeling process, and propose the fabrication method of casting airtight silicone airbags with complex profiles enveloping chain mails inside. In the experiments, we quantitatively evaluated the model of mobility and measured the bending modulus, validating the accuracy of the model and the advantages of chain mail jamming. We used a dummy arm to measure the supportable payload and test the performance on holding daily life objects. We also demonstrated the support of human upper limbs, showing the potential in practical application.

Continuum robots have good adaptability in unstructured and complex environments. However, affected by their inherent nature of flexibility and slender structure, there are challenges in high-precision motion and load. Thus, stiffness adjustment for continuum robots has consistently attracted the attention of researchers. In this paper, a stiffness adjustment mechanism (SAM) is proposed and built in a growth-controllable continuum robot (GCCR) to improve the motion accuracy in variable scale motion. The self-stiffness adjustment is realized by antagonism through cable force transmission during the length change of the continuum robot. With a simple structure, the mechanism has a scarce impact on the weight and mass distribution of the robot and required no independent actuators for stiffness adjustment. Following this, a static model considering gravity and end load is established. The presented theoretical static model is applicable to predict the shape deformations of robots under different loads. The experimental validations showed that the maximum error ratio is within 5.65%. The stiffness of the robot can be enhanced by nearly 79.6%.

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.