High-pass and Kalman filtering are two digital filtering methods for ship heave acceleration signals. However, the traditional Kalman method has dynamic model errors due to the dependence on the Fast Fourier Transform (FFT) identification model. The presence of factors such as unknown statistical properties of the noise and anomalous measurement information at the same time can cause degradation in the accuracy of the heave solution or even lead to Kalman filter divergence. This study proposes an adaptive robust cubature Kalman filter based on the Sage-Husa noise estimator (SARCKF) for ship heave motion estimation to obtain higher heave measurement accuracy. Unlike the traditional method, which adjusts the process covariance matrix by prior knowledge, the new way estimates the statistical characteristics of noise online by Sage estimator, improving the real-time filtering performance. The Huber robust method is used to correct the measurement covariance matrix to suppress the outliers in the measurement information. Then, based on the strong tracking theory, an adaptive factor is derived and combined with the Huber method to form a new cost function, effectively compensating for the model error. The simulation results show that SARCKF can still make the estimation accuracy of heave displacement reach 5% of the maximum amplitude when the statistical characteristics of noise are unknown and the model has errors. In addition, the proposed method has stronger robustness than the traditional strong tracking filter.

Aiming at the problems of “local minimum” and “unreachable target” existing in the traditional artificial potential field method in path planning, an improved artificial potential field method was proposed after analyzing the fundamental causes of the above problems. The method solved the problem of local minimum by modifying the direction and influence range of the gravitational field, increasing the virtual target and evaluation function, and the problem of unreachable targets is solved by increasing gravity. In view of the change of motion state of robot fish in amphibious environments, the improved artificial potential field method was fused with a dynamic window algorithm, and a dynamic window evaluation function of the optimal path was designed on the basis of establishing the dynamic equations of land and underwater. Then, the simulation experiment was designed under the environment of Matlab2019a. Firstly, the improved and traditional artificial potential field methods were compared. The results showed that the improved artificial potential field method could solve the above two problems well, shorten the operation time and path length, and have high efficiency. Secondly, the influence of different motion modes on path planning is verified, and the result also reflects that the amphibious robot can avoid obstacles flexibly and reach the target point accurately according to its own motion ability. This paper provides a new way of path planning for the amphibious robot.

The seam tracking operation is essential for extracting welding seam characteristics which can instruct the motion of a welding robot along the welding seam path. The chief tasks for seam tracking would be divided into three partitions. First, starting and ending points detection, then, weld edge detection, followed by joint width measurement, and, lastly, welding path position determination with respect to welding robot co-ordinate frame. A novel seam tracking technique with a four-step method is introduced. A laser sensor is used to scan grooves to obtain profile data, and the data are processed by a filtering algorithm to smooth the noise. The second derivative algorithm is proposed to initially position the feature points, and then linear fitting is performed to achieve precise positioning. The groove data are transformed into the robot’s welding path through sensor pose calibration, which could realize real-time seam tracking. Experimental demonstration was carried out to verify the tracking effect of both straight and curved welding seams. Results show that the average deviations in the X direction are about 0.628 mm and 0.736 mm during the initial positioning of feature points. After precise positioning, the average deviations are reduced to 0.387 mm and 0.429 mm. These promising results show that the tracking errors are decreased by up to 38.38% and 41.71%, respectively. Moreover, the average deviations in both X and Z direction of both straight and curved welding seams are no more than 0.5 mm, after precise positioning. Therefore, the proposed seam tracking method with four steps is feasible and effective, and provides a reference for future seam tracking research.

Robot dynamics and its parameter identification are of great significance to the realization of optimal control and human–machine interaction. The objective of this research is to address the shortcomings of establishing and identifying the self-developed six-degree-of-freedom (6-DoF) collaborative robot dynamics, which leads to a large error in the predicted torque of the proposed robot. A long short-term memory (LSTM) in an optimized recurrent neural network (RNN) is proposed to compensate the dynamic model of the proposed 6-DoF collaborative robot based on the consideration of gravity, Coriolis force, inertial force, and friction force. The analysis and experimental findings provide promising results. The compensated collaborative robot dynamic model based on LSTM in an optimized RNN displays a good prediction on the actual torque, and the root-mean-square (RMS) error between predicted and actual torques are reduced by 61.8% to 78.9% compared to the traditional dynamic model. Results of the experimental applications demonstrate the validity of the proposed systematic error compensation strategy.

Autonomous underwater vehicles (AUV) recycling in an underwater environment is particularly challenging due to the continuous exploitation of marine resources. AUV recycling via visual technology is the primary method. However, the current visual technology is limited by harsh sea conditions and has problems, such as poor tracking and detection. To solve these problems, we propose a long-term target anti-interference tracking (LTAT) method, which integrates Siamese networks, You Only Look Once (YOLO) networks and online learning ideas. Meanwhile, we propose using the cubature Kalman filter (CKF) for optimization and prediction of the position. We constructed a launch and recovery system (LARS) tracking and capturing the AUV. The system consists of the following parts: First, images are acquired via binocular cameras. Next, the relative position between the AUV and the end of the LARS was estimated based on the pixel positions of the tracking AUV feature points and binocular camera data. Finally, using a discrete proportion integration differentiation (PID) method, the LARS is controlled to capture the moving AUV via a CKF-optimized position. To verify the feasibility of our proposed system, we used the robot operating system (ROS) platform and Gazebo software to simulate the system for experiments and visualization. The experiment demonstrates that in the tracking process when the AUV makes a sinusoidal motion with an amplitude of 0.2 m in the three-dimensional space and the relative distance between the AUV and LARS is no more than 1 m, the estimated position error of the AUV does not exceed 0.03 m. In the capturing process, the final capturing error is about 28 mm. Our results verify that our proposed system has high robustness and accuracy, providing the foundation for future AUV recycling research.

Most contemporary hand robots only consider the palmar opposition as the thumb opposition, while ignoring the important role of the opposition of little finger. Consequently, the back of the hand is considered as a rigid, immovable structure. This letter investigated the influence of little finger opposition on hand grip performance through several research endeavors, including mechanical modeling and analysis of palmar opposition, optimization of the palmar-driving strategy, and prototype experiments on hand grip. First, we developed a kinematic model of palmar opposition based on the hand's anatomy and function of the transverse arch. We then employed elastic mechanics analysis methods to investigate the potential energy variations of the model during gripping. Second, we proposed an integrated palm-driving strategy and measured the deformation of the hand during the grasping process. Third, we designed and manufactured a hand rehabilitation exoskeleton that incorporates both the palmar opposition of the thumb and little finger. Finally, we conducted comparative experiments on grasping performance using the hand exoskeleton. Analysis results confirm the important role of the little finger opposition in maintaining hand grasping stability and enhancing grasping strength. These findings offer valuable insights for the research of hand function rehabilitation and high-performance robotic hands.