Robot Arm Research Articles

Bio-inspired e-skin with integrated antifouling and comfortable wearing for self-powered motion monitoring and ultra-long-range human-machine interaction

Electronic skin (e-skin) inspired by the sensory function of the skin demonstrates broad application prospects in health, medicine, and human–machine interaction. Herein, we developed a self-powered all-fiber bio-inspired e-skin (AFBI E-skin) that integrated functions of antifouling, antibacterial properties, biocompatibility and breathability. AFBI E-skin was composed of three layers of electrospun nanofibrous films. The superhydrophobic outer layer Poly(vinylidene fluoride)-silica nanofibrous films (PVDF-SiO2 NFs) possessed antifouling properties against common liquids in daily life and resisted bacterial adhesion. The polyaniline nanofibrous films (PANI NFs) were used as the electrode layer, and it had strong “static” antibacterial capability. Meanwhile, the inner layer Polylactic acid nanofibrous films (PLA NFs) served as a biocompatible substrate. Based on the triboelectric nanogenerator principle, AFBI E-skin not only enabled self-powered sensing but also utilized the generated electrical stimulation for “dynamic” antibacterial. The “dynamic-static” synergistic antibacterial strategy greatly enhanced the antibacterial effect. AFBI E-skin could be used for self-powered motion monitoring to obtain a stable signal output even when water was splashed on its surface. Finally, based on AFBI E-skin, we constructed an ultra-long-range human-machine interaction control system, enabling synchronized hand gestures between human hand and robotic hand in any internet-covered area worldwide theoretically. AFBI E-skin exhibited vast application potential in fields like smart wearable electronics and intelligent robotics.

Consumer Attention to a Coffee Brewing Robot: An Eye‐Tracking Study

ABSTRACTDuring the COVID‐19, robots were increasingly utilized in the food service industry. This study compared perceptions of robot and human baristas by tracking participants' eye‐gaze behavior. Seventy participants viewed videos and images of both baristas, and their eye movements were tracked. Metrics such as entry time, dwell time, total fixation, hit ratio, revisits, revisitors, average fixation, first fixation, and fixation count in defined areas of interest (AOIs) were measured. Participants focused mostly on robot's body during coffee preparation and human barista's face. Attention stayed on the coffee with human baristas, while robot stimuli drew focus to the robot's hand and kettle. Participants fixated more on the robot itself, potentially due to its novelty in coffee preparation. This study can provide directions for the development of robots, including their appearance, so that they could be accepted familiarly by consumers and introduced seamlessly in the food industries.

Pain neuroscience education and therapeutic exercise for the treatment of sequelae in breast cancer survivors living with chronic pain: A pilot study

BackgroundBreast cancer represents the most common type of malignant neoplasm worldwide. Advances in diagnosis and treatment have increased the life expectancy of patients. However, the sequelae associated with the treatment of the disease such as chronic pain, kinesiophobia and loss of physical function in breast cancer survivors (BCS) are a long-term health problem. Therapeutic strategies are required for the treatment of chronic sequelae in this population. ObjectivesTo evaluate the effectiveness of a pain neuroscience education (PNE) and therapeutic exercise (TE) for the treatment of chronic sequelae in BCS. MethodsQuasi-experimental repeated measures study. The intervention lasted 9 weeks, with 3 educational and 24 exercise sessions. Pain frequency and intensity (VAS scale), neuropathic pain (DN4), kinesiophobia level (TSK-11V), central sensitization (CSI-Sp), functionality of the affected arm (ULFI-Sp) and active joint range (ROM) with goniometry were measured at baseline, 3rd, 6th and 9th week. Statistical analysis included Friedman's test and ANOVA according to normality criteria. ResultsA total sample of 26 BCS participate in the study. Significant statistical changes were found from the 3rd week of treatment in the frequency and intensity of pain, kinesiophobia and neuropathic pain (p < 0.05). All variables had significant changes at the 9th week (p = 0.001). ConclusionThe results of the present investigation suggest that the combination of PNE and TE are effective in treating sequelae at short term in BCS with chronic pain.

Robots in the kitchen: The automation of food preparation in restaurants and the compounding effects of perceived love and disgust on consumer evaluations

Restaurants are swiftly embracing automation to prepare food, experimenting with innovations from robotic arms for frying foods to pizza-making robots. While these advances promise to enhance efficiency and productivity, their impact on consumer psychology remains largely unexplored. We present four experiments that demonstrate how food service automation leads to negative downstream effects (i.e., diminished taste perceptions, decreased willingness to pay, less favorable attitudes towards food items) across multiple food categories. This stems in part from two distinct contagion effects, whereby automation appears to undermine the food’s ability to contain symbolic love (positive contagion from human contact) while simultaneously increasing feelings of disgust (negative contagion from machine contact). Moreover, we highlight how communicating the consumer-oriented benefits of automation can suppress the disgust associated with automation and subsequently mitigate the deleterious effects on consumer evaluations. Our findings suggest that service retailers should consider the psychological impact on consumers when shifting away from human involvement in a category as intimate and consequential as the production of our food.

Chinese Bayberry Detection in an Orchard Environment Based on an Improved YOLOv7-Tiny Model

The precise detection of Chinese bayberry locations using object detection technology is a crucial step to achieve unmanned harvesting of these berries. Because of the small size and easy occlusion of bayberry fruit, the existing detection algorithms have low recognition accuracy for such objects. In order to realize the fast and accurate recognition of bayberry in fruit trees, and then guide the robotic arm to carry out accurate fruit harvesting, this paper proposes a detection algorithm based on an improved YOLOv7-tiny model. The model introduces partial convolution (PConv), a SimAM attention mechanism and SIoU into YOLOv7-tiny, which enables the model to improve the feature extraction capability of the target without adding extra parameters. Experimental results on a self-built Chinese bayberry dataset demonstrate that the improved algorithm achieved a recall rate of 97.6% and a model size of only 9.0 MB. Meanwhile, the precision of the improved model is 88.1%, which is 26%, 2.7%, 4.7%, 6.5%, and 4.7% higher than that of Faster R-CNN, YOLOv3-tiny, YOLOv5-m, YOLOv6-n, and YOLOv7-tiny, respectively. In addition, the proposed model was tested under natural conditions with the five models mentioned above, and the results showed that the proposed model can more effectively reduce the rates of misdetections and omissions in bayberry recognition. Finally, the improved algorithm was deployed on a mobile harvesting robot for field harvesting experiments, and the practicability of the algorithm was further verified.

Pre-clinical training, technical adjustment and human case experience of transoral robotic surgery using Versius System

ObjectiveThis study describes the training experience with the Versius Robotic System (CMR Surgical) for Transoral Robotic Surgery (TORS), the setup of the robot and its applicability in a human case. MethodsA model mannequin with anatomical characteristics simulating the TORS working space was used for training purposes. Optimal docking, positioning of robotic arms and placement of instruments were defined using the mannequin. A human patient was selected based on the inclusion criteria, and a standardized protocol for robotic techniques was developed. The surgical procedure was conducted in accordance with ethical guidelines. ResultsThe synthetic polymer mannequin mimicking human tissues and anatomy was used to define the robot set up for TORS. The patient was indicated for bilateral tonsillectomy due to recurrent episodes of tonsillitis. Bipolar Maryland and monopolar scissors were used. The procedure was uneventful. The patient remained hospitalized for 1 day and returned to work activities within 6-days, with no complications. ConclusionThe specialized mannequin proved to be effective in the simulation, training proposal and set up standardization. Furthermore, the Versius robot was found to be safe and achieved excellent results during surgery. Despite being very promising, the Versius system is still in its developmental phase and requires larger-scale samples for a more comprehensive evaluation. Level of evidence4.

Collision Localization for a Robotic Arm Based on Fiber-Optic Sensing and Machine Learning

Collision Localization for a Robotic Arm Based on Fiber-Optic Sensing and Machine Learning

Reliability Centered Modeling of an Automated Waste Sorting Robotic Arm System

In contemporary society, proper waste management is a critical issue that needs to be addressed. Waste management encompasses the collection, transportation, segregation and disposal of waste material to minimize health risks and environment impact. Manual segregation, along with other methods of waste segregation, is often inefficient and time consuming. To address this challenge, numerous industries have embraced automation in waste sorting to expedite the process through advanced technological interventions. Therefore, the optimal functioning of the components of these automated systems is crucial for rapid and accurate segregation of waste. This work is oriented toward the study of a system which is used to segregation of waste and known as waste sorting robotic arm system (WSRAS). Author used Markov decision process, along with mathematical modeling, to evaluate the different performance measure of WSRAS. Sensitivity analysis have also been done to understand the contribution of different components failure in overall performance of WSRAS.

Open Wide for This Robot Arm

Open Wide for This Robot Arm

Real-Time Home Automation System Using BCI Technology.

A Brain-Computer Interface (BCI) processes and converts brain signals to provide commands to output devices to carry out certain tasks. The main purpose of BCIs is to replace or restore the missing or damaged functions of disabled people, including in neuromuscular disorders like Amyotrophic Lateral Sclerosis (ALS), cerebral palsy, stroke, or spinal cord injury. Hence, a BCI does not use neuromuscular output pathways; it bypasses traditional neuromuscular pathways by directly interpreting brain signals to command devices. Scientists have used several techniques like electroencephalography (EEG) and intracortical and electrocorticographic (ECoG) techniques to collect brain signals that are used to control robotic arms, prosthetics, wheelchairs, and several other devices. A non-invasive method of EEG is used for collecting and monitoring the signals of the brain. Implementing EEG-based BCI technology in home automation systems may facilitate a wide range of tasks for people with disabilities. It is important to assist and empower individuals with paralysis to engage with existing home automation systems and gadgets in this particular situation. This paper proposes a home security system to control a door and a light using an EEG-based BCI. The system prototype consists of the EMOTIV Insight™ headset, Raspberry Pi 4, a servo motor to open/close the door, and an LED. The system can be very helpful for disabled people, including arm amputees who cannot close or open doors or use a remote control to turn on or turn off lights. The system includes an application made in Flutter to receive notifications on a smartphone related to the status of the door and the LEDs. The disabled person can control the door as well as the LED using his/her brain signals detected by the EMOTIV Insight™ headset.

An experimental study of ultrasonic-knife cutting for a woven carbon fiber preform by an industrial robot

This study investigates the effect of the cutting process parameters on the cutting forces and the quality parameters of a woven carbon fiber preform during robotic ultrasonic-knife cutting. An ultrasonic cutting device, with a power of 1200 W, a frequency of 24 kHz, and an amplitude of 60 µm, mounted on the end effector of a six-axis degree of freedom industrial robot to make linear cuts. A three-level factorial experimental design was used to examine the effect of the feed rate (1 m/min to 5 m/min) and the knife’s attack angle (45° to 75°) on the cutting forces, the dimensional accuracy of the machined preform coupons, and the damage on the machined preform edges. The cutting force analysis results show that the increasing feed rate resulted in increasing feed force and thrust force. However, the increase of the attack angle increases the feed force but decreases the thrust force. The average width and damage of the ultrasonic knife cut preform coupons are highly related to the process conditions. The combination of the low feed rate, 1 m/min, and the low attack angle, 45°, resulted in dimensional errors ranging from 253 μm to 365 μm oversized from the programmed 15.0 mm width with no damage. When the feed became 3 m/min and 5 m/min at the attack angle of 75°, the preform coupons’ dimensional accuracy and damage formation worsened. In these conditions, the ultrasonic knife attached to the industrial robot arm could not cut the preform plate effectively, so the tows on the preform were unevenly cut or dislodged.

“Together with who?” Recognizing partners during Collaborative Avatar Manipulation

The development of novel computer interfaces has led to the possibility of integrating inputs from multiple individuals into a single avatar, fostering collaboration by combining skills and sharing the cognitive load. However, the collaboration dynamic and its effectiveness may vary depending on the individuals involved. Particularly in scenarios where two individuals remotely control a robotic avatar without the possibility of direct communication, understanding each other’s characteristics can result in enhanced performance. To achieve this, it is essential to ascertain if individuals can discern their partner’s characteristics within merged embodiment. This paper investigates the accuracy with which participants can distinguish between two different collaborating partners (one attempting to lead and one attempting to follow) when sharing control of a robot arm during a block pick-and-place task. The results suggested that participants who changed their role according to the different roles of the two partners achieved the highest discrimination rates. Furthermore, participants changed their movements through the trials, adapting their actions to their preferred approach. This research provides insights into the factors determining individuals’ ability to understand partner characteristics during control of collaborative avatar.

A multi-docking strategy for robotic LAR and deep pelvic surgery with the Hugo RAS system: experience from a tertiary referral center.

In June 2023, our institution adopted the Medtronic Hugo RAS system for colorectal procedures. This system's independent robotic arms enable personalized docking configurations. This study presents our refined multi-docking strategy for robotic low anterior resection (LAR) and deep pelvic procedures, designed to maximize the Hugo RAS system's potential in rectal surgery, and evaluates the associated learning curve. This retrospective analysis included 31 robotic LAR procedures performed with the Hugo RAS system using our novel multi-docking strategy. Docking times were the primary outcome. The Mann-Kendall test, Spearman's correlation, and cumulative sum (CUSUM) analysis were used to assess the learning curve and efficiency gains associated with the strategy. Docking times showed a significant negative trend (p < 0.01), indicating improved efficiency with experience. CUSUM analysis confirmed a distinct learning curve, with proficiency achieved around the 15th procedure. The median docking time was 6min, comparable to other robotic platforms after proficiency. This study demonstrates the feasibility and effectiveness of a multi-docking strategy in robotic LAR using the Hugo RAS system. Our personalized approach, capitalizing on the system's unique features, resulted in efficient docking times and streamlined surgical workflow. This approach may be particularly beneficial for surgeons transitioning from laparoscopic to robotic surgery, facilitating a smoother adoption of the new technology. Further research is needed to validate the generalizability of these findings across different surgical settings and experience levels.

Robotic Mitral Valve Surgery in the Very Thin Patient: Tips and Tricks

Robotic-assisted mitral valve surgery in very small and thin patients can be challenging given the close proximity of the ports/incisions, resulting in over-crowding of the chest and collisions of the robotic arms. To overcome this challenge, we have adopted a modified strategy that we describe here, that allows us to maintain a “triangle of attack” that is more “zoomed-out”.

Evolving Robotic Hand Morphology Through Grasping and Learning

Evolving Robotic Hand Morphology Through Grasping and Learning

Biodegradable, stretchable, and high-performance triboelectric nanogenerators through interfacial polarization in bilayer structure

The increasing demand for wearable electronics has led to the development of triboelectric nanogenerators (TENGs) as a promising energy harvesting and sensing technology. However, conventional TENGs often utilize non-biodegradable materials, contributing to environmental pollution. In this work, we present a stretchable and biodegradable TENG based on hydroxyethyl cellulose (HEC) and gelatin (HG-TENG). The HG-TENG features a bilayered structure, where the large difference in their relative permittivity between HEC and gelatin induces interfacial polarization, effectively mitigating charge recombination and enhancing triboelectric performance. The optimized HG-TENG achieves an open-circuit voltage (Voc) of 93 V, a maximum power density of 57.8 µW/cm2, and can power 38 blue light-emitting diodes. The device exhibits a stretchability of 150 % and biodegrades within 3 hours in phosphate-buffered saline. Furthermore, we demonstrate the application of the HG-TENG as a wearable sensor by modifying it with trichloro(1H, 1H, 2H, 2H-perfluorooctyl)silane (FOTS) (FHG-TENG). The FHG-TENG-based smart glove, integrated with machine learning algorithms, enables real-time monitoring of blood pressure waveforms and finger motions, showcasing its potential for human-machine interfaces. The smart glove, equipped with five FHG-TENGs on the proximal interphalangeal joints of each finger, detects diverse finger gestures and generates voltage signals that control a robotic hand in real-time, demonstrating effective human-machine interaction through synchronized motion. Moreover, the smart glove achieves a high recognition accuracy of 96.15 % for 10 different hand sign languages.

Three-Axis Flat and Lightweight Force/Torque Sensor for Enhancing Kinesthetic Sensing Capability of Robotic Hand

Three-Axis Flat and Lightweight Force/Torque Sensor for Enhancing Kinesthetic Sensing Capability of Robotic Hand

Comparative Analysis of Topology Optimization Platforms for Additive Manufacturing of Robot Arms

Recently, CAD environments have integrated topology optimization (TO) tools, enabling rapid development and manufacturing of parts with optimized mechanical properties. However, different CAD platforms incorporate TO differently, making a comparative analysis necessary. This study aims to systematically compare the efficiency, material usage, and design quality of five commonly used CAD/TO platforms when applied to the topology optimization of a six degrees of freedom robotic arm. The objective is to identify the key differences in how these platforms influence the final design and manufacturing outcomes. Practical validation of results is provided by printing and assembling optimized components into a fully functional robotic arm. Our findings indicate differences in optimization efficiency, material usage, and print time between analyzed platforms. Strengths and weaknesses of each platform are indicated and recommendations for optimization parameters are provided.

Design of an Optimal Multivariable PID Controller for a Two-Link Robot Arm

Incorporating manipulator robots into various industries has revolutionized automation and manufacturing processes. Manipulator robots are versatile machines equipped with robotic arms and end-effectors, enabling them to handle complex tasks with precision and efficiency. This research aims to design an optimal Mutivariable PID (MPID) controller that ensures precise and accurate movements of two-link robot arm. Theoretical foundations of the MPID controller are discussed together with its relevance to robotic arm systems, and the challenges associated with its implementation. The research methodology involves mathematical modelling of the two-link robot arm dynamics, and the use of an optimization algorithm to determine the optimal MPID controller coefficients. Simulation results demonstrate performance enhancement and productivity of the two-link robot arm through the improved MPID controller.

Development of a robotic wheel door opener system assistive device

Physical disabilities significantly impact individuals' ability to perform activities of daily living (ADL), leading to reduced autonomy and difficulty to accomplish daily living tasks. One such barrier is the difficulty or inability to open doors, preventing access to different rooms in their residence (bathroom, bedroom, etc.), rooms at work, or shopping areas. Existing solutions, such as robotic arms on wheelchairs and door openers mounted at the top of doors, remain expensive, complex to install, and relatively difficult for the target population to use. Addressing this challenge, this study introduces a Robotic Wheel Door Opener System (RWDOS) designed to facilitate the opening and closing of the door. The RWDOS is installed at the bottom of a door and is controlled remotely through a smartphone application. The paper presents the mechanical design and control system along with a cost analysis. It concludes with initial results and outlines the future directions for the project.