A human in-hand motion (HIM) recognition system based on multi-modal perception information fusion is proposed in this paper, which can observe the state information between the object and the hand by using customized ten kinds of HIM manipulation in order to recognize the complex HIMs. First, combined with the characteristics of HIM capture, ten kinds of HIM sets are designed, and finger trajectory, contact force and electromyographic signal data are acquired synchronously through the multi-modal data acquisition platform; second, motion segmentation is realized through the threshold segmentation method, the multi-modal signal preprocessing is realized by Empirical Mode Decomposition (EMD), and multi-modal signal feature extraction is realized by Maximum Lyapunov Exponent (MLE); then, a detailed non-linear data analysis is carried out. A detailed analysis and discussion are presented from the results of the Random Forest (RF) recognizing HIMs, the comparison results of motion recognition rates of different subjects, the comparison results of motion recognition rates of different perceptrons, and the comparison results of the motion recognition rates of different machine learning methods. The experimental results show that the multi-modal perception information based HIM recognition system proposed in this paper can effectively recognize ten different HIMs, with an accuracy rate of 93.72%.

Algal robots were site-specifically, quantitatively assembled with manipulated objects and performed tasks collaboratively.

The bipedal robot should be able to maintain standing balance even in the presence of disturbing forces. The control schemes of bipedal robot are conventionally developed based on system models or fixed torque-ankle states, which often lack robustness. In this paper, a variable impedance control based on artificial muscle activation is investigated for bipedal robotic standing balance to address this limitation. The robustness was improved by applying the artificial muscle activation model to adjust the impedance parameters. In particular, an ankle variable impedance model was used to obtain the antidisturbance torque which combined with the ankle dynamic torque to estimate the desired ankle torque for robotic standing balance. The simulation and prototype experimentation results demonstrate that the control method improves the robustness of bipedal robotic standing balance control.

The precise control method plays a crucial role in improving the accuracy and efficiency of the micromanipulation of optically induced dielectrophoresis (ODEP). However, the unmeasurable nature of the ODEP force is a great challenge for the precise automatic manipulation of ODEP. Here, we propose a data-driven kinematic model to build an automatic control system for the precise manipulation of ODEP. The kinematic model is established by collecting the input displacement of the optical pattern and the output displacements of the manipulated object. Then, the control system based on the model was designed, and its feasibility and control precise were validated by numerical simulations and actual experiments on microsphere manipulation. In addition, the applications of ODEP manipulation in two typical scenarios further demonstrated the feasibility of the designed control system. This work proposes a new method to realize the precise manipulation of ODEP technology by establishing a kinematic model and a control system for micromanipulation, and it also provides a general approach for the improvement of the manipulation accuracy of other optoelectronic tweezers.