Safety during physical human–robot interaction is the most basic requirement for robots. Collision detection without additional sensors is an economically feasible way to ensure it. In contrast, current collision detection approaches have an unavoidable trade-off between sensitivity to collisions, signal smoothness, and immunity to measurement noise. In this paper, we present a novel sliding mode momentum observer (NSOMO) for detecting collisions between robots and humans, including dynamic and quasistatic collisions. The collision detection method starts with a dynamic model of the robot and derives a generalized momentum-based state equation. Then a new reaching law is devised, based on which NSOMO is constructed by fusing momentum, achieving higher bandwidth and noise immunity of observation. Finally, a time-varying dynamic threshold (TVDT) model is designed to distinguish between collision signals and the estimated lumped disturbance. Its coefficients are obtained through offline data recognition. The TVDT with NSOMO enables fast and reliable collision detection and allows collision position assessment. Simulation experiments and hardware tests of the 7-DOF collaborative robot are implemented to illustrate this proposed method’s effectiveness.

This paper investigates the output consensus problem in heterogeneous multi-agent systems. To address the challenges of traditional analytical methods in handling unknown dynamics and disturbances, a control framework is proposed that combines known model structures with a data-driven adaptive mechanism. The framework uses a distributed internal model to compensate for system heterogeneity and incorporates an event-triggered mechanism to reduce communication burden. To improve transient tracking performance, a reinforcement learning strategy based on centralized training and decentralized execution is introduced to adaptively optimize local feedback gains. Simulation results show that the proposed method effectively bounds closed-loop signals, achieves relatively fast convergence, and demonstrates some robustness and communication efficiency under process noise.

Diabetic patients need to test their blood glucose levels (BGL) frequently; however, traditional methods of blood collection and testing cause great pain to patients. In order to improve the quality of life of patients, this paper develops a noninvasive, portable, and continuous monitoring blood glucose detection system, which uses the latest bioimpedance integrated circuit to obtain the bioimpedance spectrum (BIS) of the inner forearm of the human body. The obtained BIS covers most of the frequencies up to 1 MHz. A BGL estimation model is developed using sparse group least absolute shrinkage and selection operator combined with a Gaussian kernel function support vector regression to select the optimal frequencies and features for BIS. The correlations between different frequencies and features and BGL are investigated. We test our system on a collected dataset of clinical subjects, and the results show that the average mean absolute relative difference for all subjects is 9.90%, the root mean square error is 14.81 mg/dl, and the mean absolute error is 11.75 mg/dl. 100% of the estimates fall in zones A and B of the Clarke error grid. Preliminary results show that the use of BIS integrated circuits in combination with machine learning techniques promises to enable portable, noninvasive, continuous monitoring of BGLs.