The non-contact monitoring of vital signs without body-attached sensors is a promising home healthcare technique. Recent studies have shown that millimeter-wave (mmW) radar can be used to continuous daily monitoring of vital signs such as heart rate (HR) and respiratory rate (RR). To minimize disruption to the user’s daily life and accommodate for home environments, low-power radar or radar with antennas in package (AiP) is becoming increasingly popular. However, the lower transmitting power of these systems can lead to decreased Signal-to-Noise Ratio (SNR) of the radar signal, making established measurement methods ineffective. The vital signs, particularly HR, are no longer accurately measured due to the small amplitude associated with heartbeats. In this work, we present HeRe for low-power radar, a deep learning approach for heartbeat signal reconstruction. The proposed method utilizes a neural network to identify patterns in the signal, leading to a significant improvement in the SNR of the heartbeat signal. By leveraging historical data, the current HR estimation is further refined, yielding improved precision in the results. Experiments have demonstrated the efficacy of signal reconstruction for improving the heartbeat signal in both the time and frequency domains. A comparison of the proposed approach to related works on various HR distributions, sensing ranges, and subjects showed significantly higher accuracy. To further assess the robustness of the proposed approach, extended continuous monitoring was performed and 97.5% accuracy was achieved. This article highlights the advantages of deep learning techniques for non-contact, radar-based HR monitoring, leading to accurate HR monitoring with the lowest power consumer-grade millimeter wave radar currently available on the market.

High-precision robotic grasping is necessary for extensive grasping applications in the future. Most previous grasp detection methods fail to pay enough attention to learn grasp-related features and the detection accuracy is limited. In this letter, a novel attention-augmented grasp detection network (AAGDN) is presented to generate accurate grasp poses for unknown objects. The proposed AAGDN has three elaborate designs making it achieve higher accuracy than existing methods. First, we construct a coordinate attention residual module to extract positional information and improve the spatial sensitivity of features. Then, we propose an effective feature fusion module to bridge the resolution and semantic gaps of different-level features and obtain efficient feature representations. Lastly, a feature augmentation pyramid module is developed to enhance grasp-related features as needed and reduce the loss of information. Extensive experiments on three public datasets and various real-world scenes prove that the proposed AAGDN achieves better performance than current methods. Our model obtains the state-of-the-art 99.3% and 96.2% grasp detection accuracy on the Cornell and Jacquard dataset, respectively. Moreover, in physical grasping experiments, the AAGDN attains the 94.6% success rate for unseen objects in cluttered scenes, which further demonstrates the accuracy and robustness of our method to grasp novel objects.

Industrial bin picking is a challenging task as lots of highly reflective and textureless objects are stacked. Previous methods generally predict gripper configurations or 6D object pose by expensive industrial 3D camera, ignoring the role of consumer-grade RGB-D cameras in this task. For highly reflective objects, depth maps of consumer-grade RGB-D cameras are generally of a lower quality with much stronger noise, making it challenging to detect robust grasp poses in stacked industrial scenarios. To address these challenges, we propose a novel grasp detection framework including a synthetic data generation pipeline, a novel local grasp detection method, and a global search algorithm. Specifically, in the training phase, we utilize the Pybullet and OpenGL to generate synthetic data by approximate 3D models. We introduce a novel grasp representation and propose MixStyle-ResUnet to predict and achieve sim2real transfer for this representation. In the testing phase, the Object Singleness Metric (OSM) is proposed for the global search block to evaluate the graspable probability. In addition, the RGB image enhanced by edge feature is utilized as the input of the network, which can bridge the domain shift. Extensive experiments in real scenes demonstrate that our method can achieve competitive results in grasping disordered metal parts. In over 2400 robotic grasp trials, our method achieves an average success rate of 91.1% in dense stack scenarios. We also confirm that the proposed method can be applied to unseen objects which are not included in the training dataset. Code and videos are available at https://github.com/sunhan1997/IndusGrasp.

Multi‐robots are used in hospital for the delivery service. To increase the delivery efficiency, an appropriate multi‐robots path planning algorithm is required. This study explains a mixed path planning algorithm for multi‐robots in structured hospital environment which combines the ‘corridor path planning’ and ‘room path planning’, ‘corridor path planning’ uses graph search algorithm to plan the path in corridor for multiple hospital robots, and it uses the strongly connected digraph to describe corridor environment, so it could simplify the multi‐robots path planning problem. While the ‘room path planning’ uses the artificial potential field method to plan the path in room for multiple hospital robots, it makes the hospital robot move flexibly in the room, so the robot could avoid the obstacle and other robots more flexibly. The algorithm is tested by simulation, and the result shows that mixed path planning algorithm makes the robot move fast in the corridor environment and move flexibly in the room environment.

Purpose: Robotic systems for laparoscopic minimally invasive surgery (MIS) always end up with highly sophisticated mechanisms and control schemes – making it a long and hard development process with a steep price. This paper aims to propose and realize a new, efficient and convenient strategy for building effective control systems for surgical and even other complex robotic systems. Design/methodology/approach: A novel method that takes advantage of the modularization concept by integrating two middleware technologies (robot operating system and robotic technology middleware) into a common architecture based on the strengths of both was designed and developed. Findings: Tests of the developed control system showed very low time-delay between the master and slave sides; good movement representation on the slave manipulator; and high positional and operational accuracy. Moreover, the new development strategy trial came with much higher efficiency and lower costs. Research limitations/implications: This method results in a modularized and distributed control system that is amenable to collaboratively develop; convenient to modify and update; componentized and easy to extend; mutually independent among subsystems; and practicable to be running and communicating across multiple operating systems. However, experiments show that surgical training and updates of the robotic system are still required to achieve better proficiency for completing complex minimally invasive surgical operations with the proposed and developed system. Originality/value: This research proposed and developed a novel modularization design method and a novel architecture for building a distributed teleoperation control system for laparoscopic MIS.