Robot manipulators should be able to quickly detect collisions to limit damage due to physical contact. Traditional model-based detection methods in robotics are mainly concentrated on the difference between the estimated and actual applied torque. In this paper, a model independent collision detection method is presented, based on the vibration features generated by collisions. Firstly, the natural frequencies and vibration modal features of the manipulator under collisions are extracted with illustrative examples. Then, a peak frequency based method is developed for the estimation of the vibration modal along the manipulator structure. The vibration modal features are utilized for the construction and training of the artificial neural network for the collision detection task. Furthermore, the proposed networks also generate the location and direction information about contact. The experimental results show the validity of the collision detection and identification scheme, and that it can achieve considerable accuracy.

SLAM systems are proposed to estimate mobile robot’ poses and reconstruct maps of surrounding environments. Most SLAM systems assume that their working environments are static. However, there are many dynamic objects in actual environments, which reduce the accuracy and robustness of the SLAM system. In this paper, a real-time robust visual SLAM (VSLAM) system working in dynamic environments named COEB-SLAM is proposed, which combines object detection, epipolar geometry constraint, and blur filtering to remove dynamic feature points. In reality, shooting fast-moving objects can cause image motion blur, which affects the dynamic SLAM’s ability to determine the object’s state. Therefore, we attempted to use blurring level as a constraint to identify dynamic objects. Moreover, the removal of a large number of feature points can lead to the failure of tracking. Thus, we use a feature points redistribution method for retaining enough high-quality static features to reduce this impact. Extensive evaluations in the public TUM RGB-D datasets and the real world dynamic environments demonstrate that COEB-SLAM has robustness in dynamic environments. Compared to ORB-SLAM2 system, COEB-SLAM reduces the average absolute trajectory error by about 90% in TUM RGB-D datasets. For the benefit of the community, we make public the source code at:https://github.com/biscuitzb/COEB-SLAM.

The hydrodynamic characteristics of multi-propeller autonomous underwater vehicles (AUV) is usually complicated and it is difficult to obtain an accurate mathematical model. A modeling method based on CFD calculation and maximum likelihood identification algorithm is proposed for this problem. Firstly, rough hydrodynamic parameters of AUV hull are obtained by CFD calculation. Secondly, on the basis of rough parameters, a maximum likelihood identification algorithm is proposed to adjust the parameters and improve the model precision. Besides, the method to improve the convergence of identification algorithm is analyzed by considering the characteristics of AUV model structure. Finally, the identification algorithm and identification results were validated with experimental data. It was found that this method has good convergence and adaptability. In particular, the identification results of turning force and torque parameters are highly consistent in different identification experiments, which indicates that this method can well extract the maneuvering characteristics of AUVs, thus contributing to the controller design of AUVs. The research of this paper has potential application for the modeling and control of multi-propeller AUVs.

To improve the accuracy of torque estimation and compliance control of the force sensorless, we propose a torque fusion method based on extended Kalman filter (EKF), both the data of motor current and the harmonic reducer torsional deformation are involved. First, a nonlinear EKF is designed based on the motor side dynamic model and joint deformation nonlinear model. The experiment shows that the method overcomes the interference caused by the environment and nonlinear system factors. Then, an active compliance controller is designed with a nested loop framework based on the fused torque. The inner loop computes the dynamic torque as feedforward to compensate for the system's dynamic uncertainty. The outer loop is admittance control to realize the manipulator's active compliance with the fused torque. Experiments on the robotic manipulator show that the proposed torque estimation scheme can reduce the root mean square error (RMSE) to 0.1278N $\cdot$ m and the max error of the joint estimated torque to 0.34N $\cdot$ m. In addition, the force tracking accuracy of the proposed compliant controller can reach 3.6 N, and can be extended to more redundant joints.

Industrial control systems (ICSs) are vital to critical infrastructure in energy, manufacturing, and other industries. As ICSs become increasingly interconnected, their complexity grows, making them more vulnerable to cyber attacks and system failures. This growing complexity underscores the critical need for advanced anomaly detection techniques to ensure the safe and reliable operation of ICSs. To address this need, we propose a novel method, the physical process and controller graph attention network (PCGAT), which constructs multi-level graphs based on physical process and controller information. Experiments on two real-world ICS datasets demonstrate that PCGAT achieves superior performance and enables the localization of anomalies within specific physical processes. Moreover, by leveraging graph attention networks (GATs), PCGAT enhances interpretability in anomaly detection.

A new $L_{1}$ adaptive control (L1AC) method with bound-gain-forgetting (BGF) estimator is proposed in floating-base series elastic actuators (SEAs) to enhance both transient and steady-state force control performance. The transient performance is evaluated by the output force overshoot, the convergence speed of tracking error, and the chatters effect. Meanwhile, the steady-state performance is evaluated based on static error. First, the controller stability together with the transient performance are analyzed in the controller design. Then, the BGF-based estimator is adopted to achieve a time-varying adaptation for environmental stiffness, and improve the robustness and tracking speed of the SEA. Finally, the proposed controller performances are compared with the resonance ratio control and the model reference adaptive control in experimental verification. It indicates that the L1AC results has not any overshoot and less steady-state error than the other two methods. Furthermore, the chatter amplitude of the spring deformation is only in 0.2$^\circ$ during force control.