Teleoperation with haptic feedback is especially useful for contact-rich manipulation. Humans can easily perceive physical interaction events and effects of hands-on manipulation. However, current measurement-based haptic rendering methods with distorting and confusing transmission limit teleoperation performance. The major problem is a mismatch between low-level modulated sensor signals and human-aware haptic stimuli. We explore a human-aware haptic feedback pipeline that renders a distinguishable and interpretable physical interaction based on the contact states. Manipulation tasks are modeled as time-series sequences of four contact states: 1)noncontact, 2)contact, 3)stick, and 4)slip. The temporal convolutional network model fuses force/torque sensor and velocity signals in real time to identify the four contact states with a 91.3% accuracy. Meanwhile, state-dependent haptic feedback, which combines transient and continuous feedback, brings more cues for physical interaction events and effects, corresponding to human fast- and slow-adapting receptors. We formulate a two-peak waveform for transient feedback based on a second-order superimposed exponentially decaying sinusoid model and adopt the orthogonal decomposition filter method for “inequable” continuous feedback. We demonstrate the effectiveness of our method through contact state and teleoperation experiments under different haptic conditions. The results indicate that the proposed method helps the operator to perceive and understand physical interaction and significantly improves teleoperation performance.

Stroke survivors often show a limited recovery in the hand function even after the recovery period (3-6 months after stroke) and at-home hand rehabilitation is common due to the long-term nature of hand rehabilitation and the limited medical resources. We designed a home-based hand rehabilitation training and compensation feedback system. A low-cost simple orthosis glove, a set of hand rehabilitation training games and a compensation detection and feedback module were designed and developed in this system. A preliminary test was carried out on the system and the results showed that the training section (the orthosis glove and the hand rehabilitation training games) of the system was friendly to the subjects and the subjects were more receptive to the system and the compensation detection and feedback module had a promising performance. This system can not only provide high intensity and incentive hand rehabilitation training, but also guide the stroke patients to correct wrong upper body postures during the training process, which can achieve better rehabilitation results. The system has the potential to become an effective home-based hand rehabilitation training and compensation feedback system.

Background To solve the problem of visualization in augmented reality (AR), for assembly process information, we report here on our study into the composition of AR assembly process information. Methods Our work led us to classify the visual elements of assembly processes into six categories, and after looking further into visual element expression characteristics used in assembly process information in the AR environment, standard assembly process elements have been identified and visual element layout principles studied. Conclusion Typical visualization elements have been presented, using an AR-based assembly instruction system.

We propose a noniterative solution for the Perspective-n-Point ({\rm P}n{\rm P}) problem, which can robustly retrieve the optimum by solving a seventh order polynomial. The central idea consists of three steps: 1) to divide the reference points into 3-point subsets in order to achieve a series of fourth order polynomials, 2) to compute the sum of the square of the polynomials so as to form a cost function, and 3) to find the roots of the derivative of the cost function in order to determine the optimum. The advantages of the proposed method are as follows: First, it can stably deal with the planar case, ordinary 3D case, and quasi-singular case, and it is as accurate as the state-of-the-art iterative algorithms with much less computational time. Second, it is the first noniterative {\rm P}n{\rm P} solution that can achieve more accurate results than the iterative algorithms when no redundant reference points can be used (n\le 5). Third, large-size point sets can be handled efficiently because its computational complexity is O(n).

Assembly related conflicts arising from collaborative product development process severely affect the downstream design and manufacturing activities. A collaborative approach to assembly sequence decision under distributed environment is presented. The product assembly is firstly decomposed into a hierarchical structure tree using an assembly representation based on connectors. The substructures are assigned to different team planners who will generate the sub assembly sequences by various assembly sequence planning systems. By combining these sequences systematically in a bottom-up manner and choosing suitable ones, the assembly sequence of each upper structure including the whole assembly in the structure tree are generated. Using a Web-based system, geographically dispersed team members can perform a 3D assembling visualization of products synchronously and the collaborative assembly sequence decisions are realized in distributed environment. Some prototype systems had been completed and a case was given to illustrate the method.