Robot-assisted surgery is of growing interest in the surgical and engineering communities. The use of robots allows surgery to be performed with precision using smaller instruments and incisions, resulting in shorter healing times. However, using current technology, an operator cannot directly feel the operation because the surgeon-instrument and instrument-tissue interaction force feedbacks are lost during needle insertion. Advancements in force feedback and control not only help reduce tissue deformation and needle deflection but also provide the surgeon with better control over the surgical instruments. The goal of this review is to summarize the key components surrounding the force feedback and control during robot-assisted needle insertion. The literature search was conducted during the middle months of 2017 using mainstream academic search engines with a combination of keywords relevant to the field. In total, 166 articles with valuable contents were analyzed and grouped into five related topics. This survey systemically summarizes the state-of-the-art force control technologies for robot-assisted needle insertion, such as force modeling, measurement, the factors that influence the interaction force, parameter identification, and force control algorithms. All studies show force control is still at its initial stage. The influence factors, needle deflection or planning remain open for investigation in future.

Robot-assisted cell microinjection, which is precise and can enable a high throughput, is attracting interest from researchers. Conventional probe-type cell microforce sensors have some real-time injection force measurement limitations, which prevent their integration in a cell microinjection robot. In this paper, a novel supported-beam based cell micro-force sensor with a piezoelectric polyvinylidine fluoride film used as the sensing element is described, which was designed to solve the real-time force-sensing problem during a robotic microinjection manipulation, and theoretical mechanical and electrical models of the sensor function are derived. Furthermore, an array based cell-holding device with a trapezoidal microstructure is micro-fabricated, which serves to improve the force sensing speed and cell manipulation rates. Tests confirmed that the sensor showed good repeatability and a linearity of 1.82%. Finally, robot-assisted zebrafish embryo microinjection experiments were conducted. These results demonstrated the effectiveness of the sensor working with the robotic cell manipulation system. Moreover, the sensing structure, theoretical model, and fabrication method established in this study are not scale dependent. Smaller cells, e.g., mouse oocytes, could also be manipulated with this approach.

This paper proposes a novel material recognition method for robotic tactile sensing. The method is composed of two steps. Firstly, a human-touch-inspired short-duration (1 s) slide action is conducted by the robot to obtain the tactile data. Then, the tactile data is processed with a machine learning algorithm, where 11 bioinspired features were designed to imitate the mechanical stimuli towards the four main types of tactile receptors in the skin. In this paper, a material database consisting of 144,000 tactile images is used to train seven classifiers, and the most accurate classifier is selected to recognize 12 household objects according to their properties and materials. In the property recognition, the materials are classified into 4 categories according to their compliance and texture, and the best accuracy reaches 96% in 36 ms. In the material recognition, the specific materials are recognized, and the best accuracy reaches 90% in 37 ms. The results verify the effectiveness of the proposed method.

Driving fatigue is the cause of many traffic accidents and poses a serious threat to road safety. To address this issue, this paper aims to develop a system for the early detection of driver fatigue. The system leverages heart rate variability (HRV) features and embedded machine learning to estimate the driver’s fatigue level. The driver’s HRV is derived from electrocardiogram (ECG) signals captured by a wearable device for analysis. Time- and frequency-domain HRV features are then extracted and used as the input for a machine learning classifier. A dataset of HRV features is collected from a driving simulation experiment involving 18 participants. Four machine learning classifiers are evaluated, and a backpropagation neural network (BPNN) is selected for its superior performance, achieving up to 94.35% accuracy. The optimized classifier is successfully deployed on an embedded system, providing a cost-effective and portable solution for the early detection of driver fatigue. The results demonstrate the feasibility of using HRV-based machine learning models for the early detection of driver fatigue, contributing to enhanced road safety and a reduced accident risk.

The adaptive grasp of unknown objects has been a long-term challenge in robot manipulation. It has been shown that the center of mass (CoM) plays an important role in human natural grasp. In this article, a CoM-based grasp framework with a reinforcement learning (RL) approach is proposed, where the RL agent finds the optimal grasp pose and object's CoM by using the data from vision, finger tactile sensor and wrist force-torque sensor. We generate objects with different dynamic parameters (mass, CoM position, and friction coefficient) in the PyBullet simulation environment, then stochastic and deterministic policy are, respectively, used for regrasp training. The effects of multimodal fusion perception and unimodal perception on regrasp efficiency are compared in both RL policies. The learned CoM grasping agent is transferred to real robotic hardware and evaluated on 28 types of unseen household objects, including specifically 5 objects with dynamically changing CoM. A 98.8% success rate with an average of 2.15 times of regrasp is achieved. We also demonstrate that our method improves the regrasp efficiency compared with the baseline methods.