Path planning research for mobile robots in dynamic environments

At present, due to the COVID-19, China's social and economic development has slowed down. Some life service e-commerce platforms have successively launched "contactless delivery" services, which can effectively curb the spread of the epidemic. Robot distribution is the current mainstream, but robots are different from people and need to have accurate program settings. Both path planning and obstacle avoidance are currently top issues. This requires the mobile robot to successfully arrive at the destination while minimizing the impact on the surrounding environment and pedestrians, and avoiding encroachment on the movement space of pedestrians. Therefore, the mobile robot needs to be able to actively avoid moving pedestrians in a dynamic environment, in addition to avoiding static obstacles, and safely and efficiently integrate into the pedestrian movement environment. In this paper, the path planning problem of unmanned delivery robot is studied, and the path of mobile robot in the crowd is determined by global planning and local planning, and the matlab simulation is used for verification. © The Authors. Published under a Creative Commons Attribution CC-BY 3.0 License.

A decomposition of the effects of the COVID-19 pandemic on changes in the motor vehicle collision related mortality in Alabama.

BACKGROUND/OBJECTIVE: Motor vehicle collisions are the leading cause of unintentional injury death in Alabama and at various points during the COVID-19 pandemic there were documented increases in the following risk driving behaviors: speeding, driving under the influence, and seat belt citations. Thus, the objective was to characterize the overall motor vehicle collision (MVC)-related mortality rate in Alabama and the contribution of each component over the first two years of the pandemic compared to before the pandemic by three different road classes: urban arterials, rural arterials, and all other road classes. METHODS: MVC data were derived from the Alabama eCrash database, an electronic crash reporting system used by police officers across the state. Data on vehicle miles traveled each year were collected from the U.S. Department of Transportation's Federal Highway Administration estimates of traffic volume trends. MVC-related mortality in Alabama was the primary outcome and year of MVC was the exposure. The novel decomposition method broke down population mortality rate into four parts: deaths per MVC injury, injury per MVC, MVC per vehicle miles traveled (VMT), and VMT per population. Poisson models with scaled deviance were used to estimate rate ratios of each component. Relative contribution (RC) of each component was calculated by taking the absolute value of the component's beta coefficient and dividing by the sum of the absolute values of all components' beta coefficients. Models were stratified by road class. RESULTS: Across all road classes combined, there were no significant changes to the overall MVC-related mortality rate (per population) and its components when comparing 2020-2022 to 2017-2019; this was due to the increased case fatality rate (CFR) being offset by decreases in the VMT rate and MVC injury rate. In 2020, among rural arterials a non-significant increased mortality rate was offset by a decreased VMT rate (RR 0.91, 95% CI 0.84-0.98, RC 19.2%) and MVC injury rate (RR: 0.89, 95% CI: 0.82-0.97, RC: 22.2%) when compared to 2017-2019. For non-arterials, a non-significant decreased MVC mortality rate was observed in 2020 when compared to 2017-2019 (RR 0.86, 95% CI 0.71-1.03). When considering 2021-2022 versus 2020, the only significant component for any road class was a decreased MVC injury rate for non-arterials (RR: 0.90,95% CI: 0.89-0.93) but this was offset by an increased MVC rate and CFR, resulting in no significant change to the mortality rate (per population). CONCLUSIONS: In a state with one of the highest MVC-related mortality rates in the country, despite decreases in VMTs per population and injuries per MVC, the MVC mortality rate per population did not change during the pandemic due in part to the contributions of an increase in the case fatality rate. Future research should determine whether the increase in CFR was associated with risky driving behaviors during the pandemic.

PhD Candidature During the Pandemic: Hansel and Gretel's Trip Through the Woods

The COVID-19 pandemic has significantly influenced various educational systems on a global scale since 2020, and such impact will extend into the future. As a PhD student who started her candidature shortly after the outbreak of the pandemic, I reflect on my own experiences regarding how I struggled, persevered, and survived throughout the period. I do this through two illustrations from Hansel and Gretel's journey through the woods in the Brothers Grimm's story: (1) Dropping breadcrumbs—starting a PhD in lockdown: I felt a series of negative emotions and doubted my choice of PhD after starting the academia journey in social isolation. (2) Surviving at the gingerbread house—continuing PhD in pandemic: the unstable pandemic situation nationally and locally caused repeated lockdowns that led to a delay in my research project. Since the start of the pandemic, voluntary online networking socially and academically with other academia and the support and understanding from my supervisors were key to build my persistence. By joining volunteer organisations at the university, I benefited from social connections that supported my mental health and academic development while at the meantime helping other higher degree by research students. In doing so, I harvested academic accomplishments and developed friendships. Such reciprocal relationships in return have supported my academic identity and persistence in the PhD journey. These experiences contributed to the relocation of my original motives for undertaking my PhD research, the self-reassurance of my academic identity, and the reinforcement of my aspirations and expectations for the PhD journey. This chapter adopts the theoretical concepts from cultural-historical theory to analyse my psychological development in relation to the constantly changing social environment. Perezhivanie and the social situation of development are taken as a dyad of theoretical concepts to analyse my autoethnography. This chapter aims to encourage and empower people experiencing a similar situation. What matters is the inner attitude we hold toward the challenging environment, on which we act as agents to manage the conflict, just like Hansel and Gretel, managing to beat the witch and obtain treasures in the end. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.

Enhanced Single-Particle Collision Electrochemistry at Polysulfide-Functionalized Microelectrodes for SARS-CoV-2 Detection.

Single-particle collision electrochemistry (SPCE) has shown great promise in biosensing applications due to its high sensitivity, high flux, and fast response. However, a low effective collision frequency and a large number of interfering substances in complex matrices limit its broad application in clinical samples. Herein, a novel and universal SPCE biosensor was proposed to realize sensitive detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) based on the collision and oxidation of single silver nanoparticles (Ag NPs) on polysulfide-functionalized gold ultramicroelectrodes (Ps-Au UMEs). Taking advantage of the strong interaction of the Ag-S bond, collision and oxidation of Ag NPs on the Ps-Au UME surface could be greatly promoted to generate enhanced Faraday currents. Compared with bare Au UMEs, the collision frequency of Ps-Au UMEs was increased by 15-fold, which vastly improved the detection sensitivity and practicability of SPCE in biosensing. By combining magnetic separation, liposome encapsulation release, and DNAzyme-assisted signal amplification, the SPCE biosensor provided a dynamic range of 5 orders of magnitude for spike proteins with a detection limit of 6.78 fg/mL and a detection limit of 21 TCID50/mL for SARS-CoV-2. Furthermore, SARS-CoV-2 detection in nasopharyngeal swab samples of infected patients was successfully conducted, indicating the potential of the SPCE biosensor for use in clinically relevant diagnosis.

The relationship between ESG and firm value. Case study of the automotive industry

The automotive industry is set to face a series of fundamental changes in the following years. Along with the transition to electric vehicles or production of autonomous cars, companies are also expected to better address sustainability issues, usually divided into environmental, social and governance (ESG) aspects. The present paper aims to explore the relationship between non-financial sustainability, measured by ESG scores, and firm value in the automotive industry, where empirical evidence is scarce. A structural equation modelling (SEM) approach has been taken on a novel dataset of 131 listed companies worldwide across 6 years. Our results indicate a mixed influence of the E, S, G scores on firm value in the analyzed period, with some inconclusive effects, especially from the social score. The findings are beneficial for investors, fund managers and automotive companies' executives. Further research directions are also provided.

A Dual Forward–Backward Algorithm to Solve Convex Model Predictive Control for Obstacle Avoidance in a Logistics Scenario

In recent years, the logistics sector expanded significantly, leading to the birth of smart warehouses. In this context, a key role is represented by autonomous mobile robots, whose main challenge is to find collision-free paths in their working environment in real-time. Model Predictive Control Algorithms combined with global path planners, such as the A* algorithm, show great potential in providing efficient navigation for collision avoidance problems. This paper proposes a Dual Forward–Backward Algorithm to find the solution to a Model Predictive Control problem in which the task of driving a mobile robotic platform into a bi-dimensional semi-structured environment is formulated in a convex optimisation framework.

Bidirectional Data-Driven Trajectory Prediction for Intelligent Maritime Traffic

Intelligent maritime transportation is one of the most promising enabling technologies for promoting trade efficiency and releasing the physical labor force. The trajectory prediction method is the foundation to guarantee collision avoidance and route optimization for ship transportation. This article proposes a bidirectional data-driven trajectory prediction method based on Automatic Identification System (AIS) spatio-temporal data to improve the accuracy of ship trajectory prediction and reduce the risk of accidents. Our study constructs an encoder-decoder network driven by a forward and reverse comprehensive historical trajectory and then fuses the characteristics of the sub-network to predict the ship trajectory. The AIS historical trajectory data of US West Coast ships are employed to investigate the feasibility of the proposed method. Compared with the current methods, the proposed approach lessens the prediction error by studying the comprehensive historical trajectory, and 60.28% has reduced the average prediction error. The ocean and port trajectory data are analyzed in maritime transportation before and after COVID-19. The prediction error in the port area is reduced by 95.17% than the data before the epidemic. Our work helps the prediction of maritime ship trajectory, provides valuable services for maritime safety, and performs detailed insights for the analysis of trade conditions in different sea areas before and after the epidemic.

Substance use in rural trauma patients admitted for motor vehicle injuries before and during the COVID-19 pandemic.

BACKGROUND: Urban trauma centers reported increased substance use among individuals injured in motor vehicle collisions (MVC) after the start of the COVID-19 pandemic. Little is known about individuals admitted to rural trauma centers during this time. This study's purpose was to describe substance use trends before and during the pandemic among individuals injured in MVC and treated at a rural Level-1 trauma center in West Virginia. METHODS: A cross-sectional analysis was performed using patients' medical records. The study population included individuals ≥ 18 years of age who received treatment for a motor vehicle-related injury between September 1, 2018, and September 30, 2021, and were tested for drugs and alcohol upon admittance. The pre-COVID-19 period was defined as September 1, 2018-March 15, 2020. The COVID-19 period was March 16, 2020-September 30, 2021. The primary dependent variable was the patients' drug test results. The primary independent variable was the time period. The data were analyzed using Chi-square tests, logistic regression, and proportional odds models. RESULTS: During this time, 1465 patients received treatment. On average, patients were 45 years ± 20 of age and male (57%). During COVID-19, 17% of patients tested positive for alcohol and 58% tested positive for non-alcohol drugs. After adjusting for patients' sex and age, the number of drugs that patients tested positive for was 31% higher during COVID-19 (aOR 1.31; 95% CI 1.08, 1.58). The proportion of patients testing positive for cannabinoids (p = 0.05), opioids (p = 0.001), and stimulants (p = 0.010) increased from pre-COVID-19 to COVID-19 periods. CONCLUSIONS: Drug and alcohol use increased among trauma patients admitted to a rural trauma center during COVID-19. Significant increases were seen in the number of drugs and for cannabinoids, opioids, and stimulants.

Influence of High School Socioeconomic Status on Athlete Injuries during the COVID-19 Pandemic: An Ecological Study.

Background: It is presently unclear how the cessation of high school sport has affected injury incidence at different socioeconomic levels. The COVID-19 pandemic may have disproportionately affected athletes of lower socioeconomic status, potentially increasing injury risk in this population. Purpose: To 1) Describe athlete injury incidence prior to and during the 2019-2020 and 2020-2021 school years in high school athletes by socioeconomic status; 2) Investigate the association between socioeconomic status and injury incidence in high school athletes. Study Design: Ecological Study. Methods: High schools were matched between the 2019-2020 and 2020-2021 school years. All athletes from all sports were included. High school socioeconomic status was determined by the school district median household income. Socioeconomic strata were defined as <$30,000, $30,000-50,000, $50,001-100,000, and >$100,000. Injury incidence proportion with 95% confidence interval (95% CI) was calculated for each academic year. Mixed effects negative binomial models with robust errors were performed to assess the association between the incidence proportion ratio and high school median household income. Six states and 176 high schools were included (2019-2020: 98,487 athletes; 2020-2021: 72,521 athletes). Results: Injury incidence increased in three of four socioeconomic strata during the 2020-2021 year (<$30,000: 2019-2020: 15.6 (13.1-18.1), 2020-2021: 26.3 (23.1-29.6); $30,000-50,000: 2019-2020: 7.8 (7.1-8.6), 2020-2021: 14.9 (13.8-15.9); $50,001-100,000: 2019-2020: 15.1 (14.7-15.4), 2020-2021: 21.3 (20.9-21.8); >$100,000: 2019-2020: 18.4 (18.1-18.8), 2020-2021: 17.3 (16.8-17.7)). An association was observed between injury incidence ratio and log median high school household income in 2019-2020 [1.6 (1.1-2.5)] but not 2020-2021 [1.1 (0.8-1.6)] school years. Conclusions: Athletes from lower socioeconomic high schools reported increased injury incidence compared to higher socioeconomic high schools during the 2020-2021 academic school year. These results highlight the increased COVID-19 pandemic vulnerability in athletes from lower socioeconomic high schools. High school sport stakeholders should consider how abrupt sport stoppage can affect lower socioeconomic athletes. Level of Evidence: 2.

Navigating Robots in Dynamic Environment With Deep Reinforcement Learning

In the fight against COVID-19, many robots replace human employees in various tasks that involve a risk of infection. Among these tasks, the fundamental problem of navigating robots among crowds, named robot crowd navigation, remains open and challenging. Therefore, we propose HGAT-DRL, a heterogeneous GAT-based deep reinforcement learning algorithm. This algorithm encodes the constrained human-robot-coexisting environment in a heterogeneous graph consisting of four types of nodes. It also constructs an interactive agent-level representation for objects surrounding the robot, and incorporates the kinodynamic constraints from the non-holonomic motion model into the deep reinforcement learning (DRL) framework. Simulation results show that our proposed algorithm achieves a success rate of 92%, at least 6% higher than four baseline algorithms. Furthermore, the hardware experiment on a Fetch robot demonstrates our algorithm’s successful and convenient migration to real robots. IEEE

Simulating and Optimizing Nasopharyngeal Swab Insertion Paths for use in Robotics

The nasopharyngeal swab is the standardized method of collecting specimens for diagnosing COVID-19, among numerous other respiratory illnesses. While there has been interest from the robotics community in the design of robots and manipulators for performing swab collections, detailed simulation and planning for swab insertion trajectories through the nasal cavity is less studied. In this work, we propose a simulation environment with the swab modelled as an Euler-Bernoulli beam, subject to linear elastic collisions coming from the nasal cavity. We evaluate the impact of inserting the swab with different amounts of force. We also leverage the simulation environment to pose an optimization problem that finds trajectories that minimize strain on the swab during the insertion. We find that the optimized trajectories adhere to qualitative clinical advice. © 2022 IEEE.

Bidirectional Data-Driven Trajectory Prediction for Intelligent Maritime Traffic

Intelligent maritime transportation is one of the most promising enabling technologies for promoting trade efficiency and releasing the physical labor force. The trajectory prediction method is the foundation to guarantee collision avoidance and route optimization for ship transportation. This article proposes a bidirectional data-driven trajectory prediction method based on Automatic Identification System (AIS) spatio-temporal data to improve the accuracy of ship trajectory prediction and reduce the risk of accidents. Our study constructs an encoder-decoder network driven by a forward and reverse comprehensive historical trajectory and then fuses the characteristics of the sub-network to predict the ship trajectory. The AIS historical trajectory data of US West Coast ships are employed to investigate the feasibility of the proposed method. Compared with the current methods, the proposed approach lessens the prediction error by studying the comprehensive historical trajectory, and 60.28% has reduced the average prediction error. The ocean and port trajectory data are analyzed in maritime transportation before and after COVID-19. The prediction error in the port area is reduced by 95.17% than the data before the epidemic. Our work helps the prediction of maritime ship trajectory, provides valuable services for maritime safety, and performs detailed insights for the analysis of trade conditions in different sea areas before and after the epidemic.

Evaluation of Forensic Medical Cases During COVID-19 Pandemic

Objective: Investigating the effects of the coronavirus disease pandemic on forensic medical cases admitted to emergency department (ED). Methods: This was a retrospective study conducted between March and August 2019 (prepandemic period), and March and August 2020 (pandemic period). All the patients admitted to the ED that required a forensic medical examination were investigated. Results: A total of 4527 forensic cases were investigated (prepandemic period: 2937, pandemic period: 1590). There was a male predominance among the patients (prepandemic period: 62.1%, pandemic period: 67.7%). During both periods, the most common forensic complaints were the same, and assaults, motor vehicle accidents, and penetrating injuries were in the top three, respectively. During pandemic number of motor vehicle accidents decreased significantly (p <0.001);on the other hand number of suicide attempts did not change (p= 0.83). Conclusion: When compared to the prepandemic period, the number of forensic cases decreased by half during the pandemic period. Number of motor vehicle accidents significantly decreased during the pandemic, which might have been a consequence of the stay-at-home orders. There was no increase in the in the number of suicide attempts;however, the psychological effects of the pandemic can be long-lasting and might lead to increases in rate of suicide attempts in the long-run.

Prevention of ribosome collision-induced neuromuscular degeneration by SARS CoV-2-encoded Nsp1.

An overarching goal of aging and age-related neurodegenerative disease research is to discover effective therapeutic strategies applicable to a broad spectrum of neurodegenerative diseases. Little is known about the extent to which targetable pathogenic mechanisms are shared among these seemingly diverse diseases. Translational control is critical for maintaining proteostasis during aging. Gaining control of the translation machinery is also crucial in the battle between viruses and their hosts. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the ongoing COVID-19 pandemic. Here, we show that overexpression of SARS-CoV-2-encoded nonstructural protein 1 (Nsp1) robustly rescued neuromuscular degeneration and behavioral phenotypes in Drosophila models of Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. These diseases share a common mechanism: the accumulation of aberrant protein species due to the stalling and collision of translating ribosomes, leading to proteostasis failure. Our genetic and biochemical analyses revealed that Nsp1 acted in a multipronged manner to resolve collided ribosomes, abort stalled translation, and remove faulty translation products causative of disease in these models, at least in part through the ribosome recycling factor ABCE1, ribosome-associated quality-control factors, autophagy, and AKT signaling. Nsp1 exhibited exquisite specificity in its action, as it did not modify other neurodegenerative conditions not known to be associated with ribosome stalling. These findings uncover a previously unrecognized mechanism of Nsp1 in manipulating host translation, which can be leveraged for combating age-related neurodegenerative diseases that are affecting millions of people worldwide and currently without effective treatment.

Formal Modeling of IoT-Based Drone Network for Combating COVID-19 Pandemic

Coronavirus biologically named COVID-19 is a disease that is circulating throughout the world due to its viral nature. The interaction of people is a source of spreading of coronavirus. Millions of people have been affected by this virus, and most of them have lost their lives. At present, this viral disease has grown into a worldwide pandemic which is a troubling spot for the whole world. Few technologies are supporting to manage and solve the COVID-19 crisis. In this paper, unified modeling language (UML) will be used to describe requirements and behavior of the proposed system. Unmanned aerial vehicle (UAV) drones are flying mechanical devices without any human pilot that is efficient to reduce the spreading rate of COVID-19. In the proposed IoT-based model, a cluster-based drones’ network will be used to monitor and perform required actions to tackle the violations of standard operating procedures (SOPs). The drones will gather all data through embedded cameras and sensors and will communicate with the control room to operate the actions as required. In this model, a well-maintained and collision-free network of drones will be designed using graph theory. Drones’ network will observe the violation of SOPs in the targeted area and make decisions such as produce alarm sound to alert persons and through communications by sending people warning messages on their smartphones. Further, the persons having COVID symptoms such as high temperature and unbalance respiratory rates will be identified using wearable sensors that are deployed to the targeted area and will send information to the control room to perform required actions. Drones will be able to provide medical kits to the patients’ residences that are identified using wearable sensors to reduce interaction of people. The model will be specified using Vienna Development Method-Specification language (VDM-SL) and validated through the VDM-SL toolbox.

Video analysis of one-dimensional motion and collision for distance learning laboratory

Video analysis is carried out for one-dimensional motion and collision experiments using Tracker. Physical variables, i.e. displacement, time, and velocity, can be deducted from the analysis and allows one to connect them to the equation of motion and energy conservation. Corresponding equations of motions are given without neglecting the effect of friction and air resistance. Their solutions are used as models for data-fitting purposes. The fitting results allow one to find the surface friction coefficient and the air resistance parameter for the first experiment and friction for the second experiment. These experiments and analyses can be implemented for at least sophomore-level physics in a university. However, if simplified by removing all resistant parts, they can be used for high-school or first-year students in a university. The video files can be distributed to students for online courses that are prevalently ongoing worldwide due to the COVID pandemic. Students can observe the experiments and analyse data at home. With scheduled online consulting sessions, students can conduct the analysis with helps from instructors. [ FROM AUTHOR] Copyright of Physics Education is the property of IOP Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Development and validation of a paper spray mass spectrometry method for the rapid quantitation of remdesivir and its active metabolite, GS-441524, in human plasma.

Introduction: Remdesivir (GS-5734) is a nucleoside analog prodrug with antiviral activity against several single-stranded RNA viruses, including the novel severe respiratory distress syndrome virus 2 (SARS-CoV-2). It is currently the only FDA-approved antiviral agent for the treatment of individuals with COVID-19 caused by SARS-CoV-2. However, remdesivir pharmacokinetics/pharmacodynamics (PK/PD) and toxicity data in humans are extremely limited. It is imperative that precise analytical methods for the quantification of remdesivir and its active metabolite, GS-441524, are developed for use in further studies. We report, herein, the first validated anti-viral paper spray-mass spectrometry (PS-MS/MS) assay for the quantification of remdesivir and GS-441524 in human plasma. We seek to highlight the utility of PS-MS/MS technology and automation advancements for its potential future use in clinical research and the clinical laboratory setting. Methods: Calibration curves for remdesivir and GS-441524 were created utilizing seven plasma-based calibrants of varying concentrations and two isotopic internal standards of set concentrations. Four plasma-based quality controls were prepared in a similar fashion to the calibrants and utilized for validation. No sample preparation was needed. Briefly, plasma samples were spotted on a paper substrate contained within pre-manufactured plastic cassette plates, and the spots were dried for 1 h. The samples were then analyzed directly for 1.2 min utilizing PS-MS/MS. All experiments were performed on a Thermo Scientific Altis triple quadrupole mass spectrometer utilizing automated technology. Results: The calibration ranges were 20 - 5000 and 100 - 25000 ng/mL for remdesivir and GS-441524, respectively. The calibration curves for the two antiviral agents showed excellent linearity (average R2 = 0.99-1.00). The inter- and intra-day precision (%CV) across validation runs at four QC levels for both analytes was less than 11.2% and accuracy (%bias) was within ± 15%. Plasma calibrant stability was assessed and degradation for the 4 °C and room temperature samples were seen beginning at Day 7. The plasma calibrants were stable at -20 °C. No interference, matrix effects, or carryover was discovered during the validation process. Conclusions: PS-MS/MS represents a useful methodology for rapidly quantifying remdesivir and GS-441524, which may be useful for clinical PK/PD, therapeutic drug monitoring (TDM), and toxicity assessment, particularly during the current COVID-19 pandemic and future viral outbreaks.

Implementation of Omni-D Tele-Presence Robot Using Kalman Filter and Tricon Ultrasonic Sensors

The tele-presence robot is designed to set forth an economic solution to facilitate day-to-day normal activities in almost every field. There are several solutions to design tele-presence robots, e.g., Skype and team viewer, but it is pretty inappropriate to use Skype and extra hardware. Therefore, in this article, we have presented a robust implementation of the tele-presence robot. Our proposed omnidirectional tele-presence robot consists of (i) Tricon ultrasonic sensors, (ii) Kalman filter implementation and control, and (iii) integration of our developed WebRTC-based application with the omnidirectional tele-presence robot for video transmission. We present a new algorithm to encounter the sensor noise with the least number of sensors for the estimation of Kalman filter. We have simulated the complete model of robot in Simulink and Matlab for the tough paths and critical hurdles. The robot successfully prevents the collision and reaches the destination. The mean errors for the estimation of position and velocity are 5.77% and 2.04%. To achieve efficient and reliable video transmission, the quality factors such as resolution, encoding, average delay and throughput are resolved using the WebRTC along with the integration of the communication protocols. To protect the data transmission, we have implemented the SSL protocol and installed it on the server. We tested three different cases of video resolutions (i.e., 320×280, 820×460 and 900×590) for the performance evaluation of the video transmission. For the highest resolution, our TPR takes 3.5 ms for the encoding, and the average delay is 2.70 ms with 900 × 590 pixels.

Assessment of Pain Onset and Maximum Bearable Pain Thresholds in Physical Contact Situations.

With the development of robot technology, robot utilization is expanding in industrial fields and everyday life. To employ robots in various fields wherein humans and robots share the same space, human safety must be guaranteed in the event of a human-robot collision. Therefore, criteria and limitations of safety need to be defined and well clarified. In this study, we induced mechanical pain in humans through quasi-static contact by an algometric device (at 29 parts of the human body). A manual apparatus was developed to induce and monitor a force and pressure. Forty healthy men participated voluntarily in the study. Physical quantities were classified based on pain onset and maximum bearable pain. The overall results derived from the trials pertained to the subjective concept of pain, which led to considerable inter-individual variation in the onset and threshold of pain. Based on the results, a quasi-static contact pain evaluation method was established, and biomechanical safety limitations on forces and pressures were formulated. The pain threshold attributed to quasi-static contact can serve as a safety standard for the robots employed.

Vulnerable Road User Protection from Heavy Goods Vehicles Using Direct and Indirect Vision Aids

In Europe, heavy goods vehicles (HGVs) are disproportionately involved in serious and fatal collisions with vulnerable road users (VRUs). An interrogation of 2019 national crash data for Great Britain (Stats19) suggested that detection of cyclists and pedestrians in the nearside and front blind spots of HGVs is still a significant problem during forward or left-turn manoeuvres of the HGV. To improve detection, Transport for London introduced Direct Vision and Safe System Standards in 2021 for HGVs entering the Greater London area. This research assessed the efficacy of one of the Safe System requirements—the fitment of sensors to detect vulnerable road users on the nearside of the vehicle. A physical testing procedure was developed to determine the performance of a sensor system meeting the Transport for London Safe System requirements. Overall, the Safe System compliant sensor system missed 52% of expected detection nodes on the nearside of the vehicle. A total of 56% of the “stop vehicle” nodes, 45% of the “slow down” and 48% of the “proceed with caution” nodes were not recognised. The most forward sensor did not fully cover the front-left corner blind spot, missing 70% of the desired detection nodes. Nearside sensor systems fitted to Safe System requirements may cover a reasonable area but could still leave many undetected zones to the left and front of the vehicle. Standardising sensor range and location could help to eliminate sensor blind spots. Mandating additional front sensors would help cover the blind spot at the front-left corner of the HGV.