Mobile manipulators collision-free trajectory planning with regard to end-effector vibrations elimination

A sub-optimal point-to-point trajectory planning method for mobile manipulators operating in the workspace including obstacles taking into account the damping of the end-effector vibrations is presented. The proposed solution is based on extended Jacobian approach and redundancy resolution at the acceleration level. Fulfilment of the condition stopping the mobile manipulator at the destination point is guaranteed, which leads to elimination of the end-effector vibrations and significantly increases positioning accuracy. The effectiveness of the presented method is shown and compared to the classical Jacobian pseudo inverse approach. A computer example involving a mobile manipulator consisting of a nonholonomic platform (2, 0) class and SCARA-type holonomic manipulator operating in two-dimensional task space including obstacle is also presented

Analysis of Coinfections with A/H1N1 Strain Variants among Pigs in Poland by Multitemperature Single-Strand Conformational Polymorphism

Monitoring and control of infections are key parts of surveillance systems and epidemiological risk prevention. In the case of influenza A viruses (IAVs), which show high variability, a wide range of hosts, and a potential of reassortment between different strains, it is essential to study not only people, but also animals living in the immediate surroundings. If understated, the animals might become a source of newly formed infectious strains with a pandemic potential. Special attention should be focused on pigs, because of the receptors specific for virus strains originating from different species, localized in their respiratory tract. Pigs are prone to mixed infections and may constitute a reservoir of potentially dangerous IAV strains resulting from genetic reassortment. It has been reported that a quadruple reassortant, A(H1N1)pdm09, can be easily transmitted from humans to pigs and serve as a donor of genetic segments for new strains capable of infecting humans. Therefore, it is highly desirable to develop a simple, cost-effective, and rapid method for evaluation of IAV genetic variability. We describe a method based on multitemperature singlestrand conformational polymorphism (MSSCP), using a fragment of the hemagglutinin (HA) gene, for detection of coinfections and differentiation of genetic variants of the virus, difficult to identify by conventional diagnostic

Working toward Solving Safety Issues in Human–Robot Collaboration: A Case Study for Recognising Collisions Using Machine Learning Algorithms

The monitoring and early avoidance of collisions in a workspace shared by collaborative robots (cobots) and human operators is crucial for assessing the quality of operations and tasks completed within manufacturing. A gap in the research has been observed regarding effective methods to automatically assess the safety of such collaboration, so that employees can work alongside robots, with trust. The main goal of the study is to build a new method for recognising collisions in workspaces shared by the cobot and human operator. For the purposes of the research, a research unit was built with two UR10e cobots and seven series of subsequent of the operator activities, specifically: (1) entering the cobot’s workspace facing forward, (2) turning around in the cobot’s workspace and (3) crouching in the cobot’s workspace, taken as video recordings from three cameras, totalling 484 images, were analysed. This innovative method involves, firstly, isolating the objects using a Convolutional Neutral Network (CNN), namely the Region-Based CNN (YOLOv8 Tiny) for recognising the objects (stage 1). Next, the Non-Maximum Suppression (NMS) algorithm was used for filtering the objects isolated in previous stage, the k-means clustering method and Simple Online Real-Time Tracking (SORT) approach were used for separating and tracking cobots and human operators (stage 2) and the Convolutional Neutral Network (CNN) was used to predict possible collisions (stage 3). The method developed yields 90% accuracy in recognising the object and 96.4% accuracy in predicting collisions accuracy, respectively. The results achieved indicate that understanding human behaviour working with cobots is the new challenge for modern production in the Industry 4.0 and 5.0 concept

Inertial Sensor-Based Sport Activity Advisory System Using Machine Learning Algorithms

The aim of this study was to develop a physical activity advisory system supporting the correct implementation of sport exercises using inertial sensors and machine learning algorithms. Specifically, three mobile sensors (tags), six stationary anchors and a system-controlling server (gateway) were employed for 15 scenarios of the series of subsequent activities, namely squats, pull-ups and dips. The proposed solution consists of two modules: an activity recognition module (ARM) and a repetition-counting module (RCM). The former is responsible for extracting the series of subsequent activities (so-called scenario), and the latter determines the number of repetitions of a given activity in a single series. Data used in this study contained 488 three defined sport activity occurrences. Data processing was conducted to enhance performance, including an overlapping and non-overlapping window, raw and normalized data, a convolutional neural network (CNN) with an additional post-processing block (PPB) and repetition counting. The developed system achieved satisfactory accuracy: CNN + PPB: non-overlapping window and raw data, 0.88; non-overlapping window and normalized data, 0.78; overlapping window and raw data, 0.92; overlapping window and normalized data, 0.87. For repetition counting, the achieved accuracies were 0.93 and 0.97 within an error of ±1 and ±2 repetitions, respectively. The archived results indicate that the proposed system could be a helpful tool to support the correct implementation of sport exercises and could be successfully implemented in further work in the form of web application detecting the user’s sport activity

Inertial Sensor-Based Sport Activity Advisory System Using Machine Learning Algorithms

The aim of this study was to develop a physical activity advisory system supporting the correct implementation of sport exercises using inertial sensors and machine learning algorithms. Specifically, three mobile sensors (tags), six stationary anchors and a system-controlling server (gateway) were employed for 15 scenarios of the series of subsequent activities, namely squats, pull-ups and dips. The proposed solution consists of two modules: an activity recognition module (ARM) and a repetition-counting module (RCM). The former is responsible for extracting the series of subsequent activities (so-called scenario), and the latter determines the number of repetitions of a given activity in a single series. Data used in this study contained 488 three defined sport activity occurrences. Data processing was conducted to enhance performance, including an overlapping and non-overlapping window, raw and normalized data, a convolutional neural network (CNN) with an additional post-processing block (PPB) and repetition counting. The developed system achieved satisfactory accuracy: CNN + PPB: non-overlapping window and raw data, 0.88; non-overlapping window and normalized data, 0.78; overlapping window and raw data, 0.92; overlapping window and normalized data, 0.87. For repetition counting, the achieved accuracies were 0.93 and 0.97 within an error of ±1 and ±2 repetitions, respectively. The archived results indicate that the proposed system could be a helpful tool to support the correct implementation of sport exercises and could be successfully implemented in further work in the form of web application detecting the user’s sport activity

Multifaceted Strategy for the Synthesis of Diverse 2,2'-Bithiophene Derivatives

New catalytically or high pressure activated reactions and routes, including coupling, double bond migration in allylic systems, and various types of cycloaddition and dihydroamination have been used for the synthesis of novel bithiophene derivatives. Thanks to the abovementioned reactions and routes combined with non-catalytic ones, new acetylene, butadiyne, isoxazole, 1,2,3-triazole, pyrrole, benzene, and fluoranthene derivatives with one, two or six bithiophenyl moieties have been obtained. Basic sources of crucial substrates which include bithiophene motif for catalytic reactions were 2,2'-bithiophene, gaseous acetylene and 1,3-butadiyne

Multifaceted Strategy for the Synthesis of Diverse 2,2'-Bithiophene Derivatives

New catalytically or high pressure activated reactions and routes, including coupling, double bond migration in allylic systems, and various types of cycloaddition and dihydroamination have been used for the synthesis of novel bithiophene derivatives. Thanks to the abovementioned reactions and routes combined with non-catalytic ones, new acetylene, butadiyne, isoxazole, 1,2,3-triazole, pyrrole, benzene, and fluoranthene derivatives with one, two or six bithiophenyl moieties have been obtained. Basic sources of crucial substrates which include bithiophene motif for catalytic reactions were 2,2'-bithiophene, gaseous acetylene and 1,3-butadiyne

Erratum to: Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition) (Autophagy, 12, 1, 1-222, 10.1080/15548627.2015.1100356

non present