In order to offer simple and convenient assistance for the elderly and disabled to take care of themselves, we propose a general learning and generalization approach for a service robot to accomplish specified tasks autonomously in an unstructured home environment. This approach firstly learns the required tasks by learning from demonstration (LfD) and represents the learned tasks with dynamic motion primitives (DMPs), so as to easily generalize them to a new environment only with little modification. Furthermore, we integrate dynamic potential field (DPF) with the above DMPs model to realize the autonomous obstacle avoidance function of a service robot. This approach is validated on the wheelchair mounted robotic arm (WMRA) by performing serial experiments of placing a cup on the table with an obstacle or without obstacle on its motion path.

As the aging of the population becomes more severe, wheelchair-mounted robotic arms (WMRAs) are gaining an increased amount of attention. Laser pointer interactions are an attractive method enabling humans to unambiguously point out objects and pick them up. In addition, they bring about a greater sense of participation in the interaction process as an intuitive interaction mode. However, the issue of human–robot interactions remains to be properly tackled, and traditional laser point interactions still suffer from poor real-time performance and low accuracy amid dynamic backgrounds. In this study, combined with an advanced laser point detection method and an improved pose estimation algorithm, a laser pointer is used to facilitate the interactions between humans and a WMRA in an indoor environment. Assistive grasping using a laser selection consists of two key steps. In the first step, the images captured using an RGB-D camera are pre-processed, and then fed to a convolutional neural network (CNN) to determine the 2D coordinates of the laser point and objects within the image. Meanwhile, the centroid coordinates of the selected object are also obtained using the depth information. In this way, the object to be picked up and its location are determined. The experimental results show that the laser point can be detected with almost 100% accuracy in a complex environment. In the second step, a compound pose-estimation algorithm aiming at a sparse use of multi-view templates is applied, which consists of both coarse- and precise-matching of the target to the template objects, greatly improving the grasping performance. The proposed algorithms were implemented on a Kinova Jaco robotic arm, and the experimental results demonstrate their effectiveness. Compared with commonly accepted methods, the time consumption of the pose generation can be reduced from 5.36 to 4.43 s, and synchronously, the pose estimation error is significantly improved from 21.31% to 3.91%.

A high speed MEMS flow sensor to enhance the reliability and accuracy of a liquid dispensing system is proposed. Benefitting from the sensor information feedback, the system can self-adjust the open time of the solenoid valve to accurately dispense desired volumes of reagent without any pre-calibration. First, an integrated high-speed liquid flow sensor based on the measurement of the pressure difference across a flow channel is presented. Dimensions of the micro-flow channel and two pressure sensors have been appropriately designed to meet the static and dynamic requirements of the liquid dispensing system. Experiments results show that the full scale (FS) flow measurement ranges up to 80 μL/s, with a nonlinearity better than 0.51% FS. Secondly, a novel closed-loop control strategy is proposed to calculate the valve open time in each dispensing cycle, which makes the system immune to liquid viscosity, pressure fluctuation, and other sources of error. Finally, dispensing results show that the system can achieve better dispensing performance, and the coefficient of variance (CV) for liquid dispensing is below 3% at 1 μL and below 4% at 100 nL.

With the growth of the world’s population, limited healthcare resources cannot provide adequate nursing services for all people in need. The wheelchair-mounted robotic arm (WMRA) with interactive technology could help to improve users’ self-care ability and relieve nursing stress. However, the users struggle to control the WMRA due to complex operations. To use the WMRA with less burden, this paper proposes an object affordance-based implicit interaction technology using a laser pointer. Firstly, a laser semantic identification algorithm combined with the YOLOv4 and the support vector machine (SVM) is designed to identify laser semantics. Then, an implicit action intention reasoning algorithm, based on the concept of object affordance, is explored to infer users’ intentions and learn their preferences. For the purpose of performing the actions about task intention in the scene, the dynamic movement primitives (DMP) and the finite state mechanism (FSM) are respectively used to generalize the trajectories of actions and reorder the sequence of actions in the template library. In the end, we verified the feasibility of the proposed technology on a WMRA platform. Compared with the previous method, the proposed technology can output the desired intention faster and significantly reduce the user’s limb involvement time (about 85%) in operating the WMRA under the same task.

In recent years, inkjet technology has played an important role in industrial materials printing and various sensors fabrication, but the mechanisms of the inkjet print head should be researched more elaborately. The steady state deformation analysis of a tubular piezoelectric print head, which can be classified as a plane strain problem because the radii of the tubes are considerably smaller than the lengths, is discussed in this paper. The geometric structure and the boundary conditions are all axisymmetric, so a one-dimensional mathematical model is constructed. By solving the model, the deformation field and stress field, as well as the electric potential distribution of the piezoelectric tube and glass tube, are obtained. The results show that the deformations are on the nanometer scale, the hoop stress is larger than the radial stress on the whole, and the potential is not linearly distributed along the radial direction. An experiment is designed to validate these computations. A discussion of the effect of the tubes’ thicknesses on the system deformation status is provided.