The present research focuses in the comparison of two social robot models running the same Human-Robot Interaction (HRI) applications targeting the context of music education for children aged 9-11, with the objective of underlying the design choices favored by the target audience on the running tasks. The Guitar Tuner consists of two main functionalities: tuning process and performance evaluation, which we implemented using the NAO and Zenbo robots. User evaluation included 20 children and assessed their perceived robot embodiment preferences (e.g., shape, robot motion, displays, and emotional expressivity) and perceived usability aspects. The evaluation used an experimental remote protocol supporting collecting online feedback with users during the COVID-19 pandemic. Empirical results supported performing quantitative and qualitative evaluations of the HRI application and highlighting the perceived differences of robot embodiment features. The discussions center on improving a future version of the HRI application, plus children's considerations about their preferred robot embodiment features during the observation sessions. Finally, we propose recommendations for robot embodiment design for children and learning based on this case study and discuss protocol limitations during the social distancing context, that we believe as a valid alternative to move forward with experimental designs, particularly in robotics, becoming a great contribution to other researchers facing similar hurdles.

The application of Middle-sized Car-like Robots (MCRs) in indoor and outdoor road scenarios is becoming broader and broader. To achieve the goal of stable and efficient movement of the MCRs on the road, a motion planning algorithm based on the Hybrid Potential Field Model (HPFM) is proposed in this paper. Firstly, the artificial potential field model improved with the eye model is used to generate a safe and smooth initial path that meets the road constraints. Then, the path constraints such as curvatures and obstacle avoidance are converted into an unconstrained weighted objective function. The efficient least-squares & quasi-Newton fusion algorithm is used to optimize the initial path to obtain a smooth path curve suitable for the MCR. Finally, the speed constraints are converted into a weighted objective function based on the path curve to get the best speed profile. Numerical simulation and practical prototype experiments are carried out on different road scenes to verify the performance of the proposed algorithm. The results show that re-planned trajectories can satisfy the path constraints and speed constraints. The real-time re-planning period is 184 ms, which demonstrates the proposed approach's effectiveness and feasibility.

The quality and safety of medicinal products are related to patients' lives and health. Therefore, quality inspection takes a key role in the pharmaceutical industry. Most of the previous solutions are based on machine vision, however, their performance is limited by the RGB sensor. The pharmaceutical visual inspection robot combined with hyperspectral imaging technology is becoming a new trend in the high-end medical quality inspection process since the hyperspectral data can provide spectral information with spatial knowledge. Yet, there is no comprehensive review about hyperspectral imaging-based medicinal products inspection. This paper focuses on the pivotal pharmaceutical applications, including counterfeit drugs detection, active component analysis of tables, and quality testing of herbal medicines and other medical materials. We discuss the technology and hardware of Raman spectroscopy and hyperspectral imaging, firstly. Furthermore, we review these technologies in pharmaceutical scenarios. Finally, the development tendency and prospect of hyperspectral imaging technology-based robots in the field of pharmaceutical quality inspection is summarized.

A critical component in the public health response to pandemics is the ability to determine the spread of diseases via diagnostic testing kits. Currently, diagnostic testing kits, treatments, and vaccines for the COVID-19 pandemic have been developed and are being distributed to communities worldwide, but the spread of the disease persists. In conjunction, a strong level of social distancing has been established as one of the most basic and reliable ways to mitigate disease spread. If home testing kits are safely and quickly delivered to a patient, this has the potential to significantly reduce human contact and reduce disease spread before, during, and after diagnosis. This paper proposes a diagnostic testing kit delivery scheduling approach using the Mothership and Drone Routing Problem (MDRP) with one truck and multiple drones. Due to the complexity of solving the MDRP, the problem is decomposed into 1) truck scheduling to carry the drones and 2) drone scheduling for actual delivery. The truck schedule (TS) is optimized first to minimize the total travel distance to cover patients. Then, the drone flight schedule is optimized to minimize the total delivery time. These two steps are repeated until it reaches a solution minimizing the total delivery time for all patients. Heuristic algorithms are developed to further improve the computational time of the proposed model. Experiments are made to show the benefits of the proposed approach compared to the commonly performed face-to-face diagnosis via the drive-through testing sites. The proposed solution method significantly reduced the computation time for solving the optimization model (less than 50 minutes) compared to the exact solution method that took more than 10 hours to reach a 20% optimality gap. A modified basic reproduction rate (i.e., ) is used to compare the performance of the drone-based testing kit delivery method to the face-to-face diagnostic method in reducing disease spread. The results show that our proposed method ( = 0.002) outperformed the face-to-face diagnostic method ( = 0.0153) by reducing by 7.5 times.

Unmanned Aerial Systems (UAS) are becoming increasingly popular in the public safety sector. While some applications have so far only been envisioned, others are regularly performed in real-life scenarios. Many more fall in between and are actively investigated by research and commercial communities alike. This study reviews the maturity levels, or "market-readiness", of public safety applications for UAS. As individual assessments of all applications suggested in the literature are infeasible due to their sheer number, we propose a novel set of application categories: Remote Sensing, Mapping, Monitoring, Human-drone Interaction, Flying Ad-hoc Networks, Transportation, and Counter UAV Systems. Each category's maturity is assessed through a literature review of contained applications, using the metric of Application Readiness Levels (ARLs). Relevant aspects such as the environmental complexity and available mission time of addressed scenarios are taken into account. Following the analysis, we infer that improvements in autonomy and software reliability are the most promising research areas for increasing the usefulness and acceptance of UAS in the public safety domain.

This paper presents a comprehensive review of ground agricultural robotic systems and applications with special focus on harvesting that span research and commercial products and results, as well as their enabling technologies. The majority of literature concerns the development of crop detection, field navigation via vision and their related challenges. Health monitoring, yield estimation, water status inspection, seed planting and weed removal are frequently encountered tasks. Regarding robotic harvesting, apples, strawberries, tomatoes and sweet peppers are mainly the crops considered in publications, research projects and commercial products. The reported harvesting agricultural robotic solutions, typically consist of a mobile platform, a single robotic arm/manipulator and various navigation/vision systems. This paper reviews reported development of specific functionalities and hardware, typically required by an operating agricultural robot harvester; they include (a) vision systems, (b) motion planning/navigation methodologies (for the robotic platform and/or arm), (c) Human-Robot-Interaction (HRI) strategies with 3D visualization, (d) system operation planning & grasping strategies and (e) robotic end-effector/gripper design. Clearly, automated agriculture and specifically autonomous harvesting via robotic systems is a research area that remains wide open, offering several challenges where new contributions can be made.

The development of UA is one of the most important challenges for the future of aviation. Consequently, this is one of the major challenges for the future of aviation law, particularly for those legal regulations that aim to provide an adequate level of civil aviation safety. The main goal is to show the results of the analysis of the legal framework created in Europe and to show where Europe is going in the nearest future. The method of study comprised content analysis of existing legislation. Results of the study shows inter alia that although the analysis of the adopted solutions is necessary for a better understanding, a comprehensive assessment of these solutions will be possible at the earliest after the end of the adopted transition periods, i.e. after 2023.

This paper examines regulations which govern procedures for reporting incidents other than accidents or serious incidents related to unmanned aircraft system (UAS) operations. The regulations are discussed in the context of available data and the paper included an analysis of them from both a European and national perspective. The goal of the paper is to provide a series of recommendations with regard to the procedures for reporting and analyzing UAS incidents in order to improve the safe integration of unmanned and manned aviation. This article also explores the legal consequences that arise from the midair collision between a UAS and a manned aircraft.

In the last decades, mobile robotics has become a very interesting research topic in the field of robotics, mainly because of population ageing and the recent pandemic emergency caused by Covid-19. Against this context, the paper presents an overview on wheeled mobile robot (WMR), which have a central role in nowadays scenario. In particular, the paper describes the most commonly adopted locomotion strategies, perception systems, control architectures and navigation approaches. After having analyzed the state of the art, this paper focuses on the kinematics of three omnidirectional platforms: a four mecanum wheels robot (4WD), a three omni wheel platform (3WD) and a two swerve-drive system (2SWD). Through a dimensionless approach, these three platforms are compared to understand how their mobility is affected by the wheel speed limitations that are present in every practical application. This original comparison has not been already presented by the literature and it can be used to improve our understanding of the kinematics of these mobile robots and to guide the selection of the most appropriate locomotion system according to the specific application.

Despite their general availability, drones are not currently widely used in emergency medicine, distribution of medication and other medical products, as well as in epidemiological emergencies, in which limiting interpersonal contact is crucial for minimizing the public health risk. Given the current epidemiological situation, it is pertinent to consider, whether implementing activities with the use of drones can significantly contribute to minimizing health risks, and whether such initiatives are acceptable in the light of applicable legal regulations. The main objective is supported by an analysis of the usefulness of applicable provisions, indicating the direction of possible changes in existing legal regulations. Additionally, the article aims to demonstrate the feasibility of drone use in activities related to combating epidemics, as well as to emphasize their practical importance. Reports on the commercial use of drones in the distribution of goods and services have also been used as material for comprehensive analysis. Simultaneously, the article also includes data on quantities of equipment available to healthcare units in Poland for saving life and health. The present work uses the method of analysis of applicable legal regulations, as a criterion for the usefulness of existing solutions in the area of improving the quality of medical services, including preventive measures and combating the effects of an epidemic.

Delivery drones have always faced challenges when it comes to reliably deliver packages. This paper introduces a novel concept of a hybrid drone called "MICOPTER" to alleviate this issue. Being able to fly in three modes of aircraft, helicopter, and gyrocopter, the proposed model of the multi-identity helicopter comprises a 2DOF tilting mechanism of rotors and a folding wing system leading to better performance and controllability. To scrutinize the idea, MICOPTER is compared to other types of Unmanned Aerial Vehicles (UAVs) in terms of different performance parameters. The performance goal of the MICOPTER is the realization of a predetermined standard delivery drone mission based on Amazon Prime Air. According to the relevant literature, the corresponding conceptual design equations are formulated and the traditional matching diagram method is utilized to attain the initial design point. Afterward, a multidisciplinary-feasible design matrix is provided as well as multi-objective optimization to strive for optimal feasible configurations while maximizing cruise velocity and range. Furthermore, the configuration and performance of some of the feasible design points on the final Pareto frontier are compared with the traditional design. Finally, by simulating a typical flight profile and using robust non-linear backstepping control, the controllability of the proposed configuration is investigated. The controller performance is assessed considering its stability and tracking 8-shape trajectory. Results indicate the MICOPTER capabilities as a novel configuration in both terms of design performance and controllability.

Simulators in robotics are well-known tools for the development of new applications and training and integration of systems for remote operation or supervision. Therefore, robotics is one of the most used practices in science, technology, engineering, and mathematics-based educational frameworks, and, with COVID-19, simulators have become increasingly important. This study shows specific benefits achieved for K-12 students in an individualized family service plan/resource teachers for the gifted model based on a review. A simulator is typically adopted for undergraduates students to increase their ability to make technical-based decisions and move smoothly between the real and virtual worlds, with a strong emphasis on the feedback from both. It enables students to develop abilities to build robots without needing commercial kits. In a sim-to-real approach, early simulation allows improved team integration and reduced reliance on skills, equalizing the abilities of students, regardless of their backgrounds. Simultaneously, simulation encourages students to work harder in real implementation by equalizing their class level, resulting in competition-based learning.

Coal moisture content monitoring plays an important role in carbon reduction and clean energy decisions of coal transportation-storage aspects. Traditional coal moisture content detection mechanisms rely heavily on detection equipment, which can be expensive or difficult to deploy under field conditions. To achieve fast prediction of coal moisture content, a novel neural network model based on attention mechanism and bidirectional ResNet-LSTM structure (ABRM) is proposed in this paper. The prediction of coal moisture content is achieved by training the model to learn the relationship between changes of coal moisture content and meteorological conditions. The experimental results show that the proposed method has superior performance in terms of moisture content prediction accuracy compared with other state-of-the-art methods, and that ABRM model approaches appear to have the greatest potential for predicting coal moisture content shifts in the face of meteorological elements.

Automatic venipuncture robots are expected to replace manual venipuncture methods owing to their high control precision, steady operation, and measurable perception. However, the lack of perception of the venipuncture status in the human body leads to an increased risk and failure rate, which further restricts the development of such robots. To address this, we propose a humanoid venipuncture method guided by a biomechanical model to imitate human sensations and feedback. This method intends to perceive the venipuncture status and improve the performance of the venipuncture robot. First, this study establishes a biomechanical venipuncture model, which thoroughly considers the elastic deformation, cutting, and friction of tissues and can be applied to different venipuncture conditions. Then, venipuncture simulations and in vitro phantom experiments are performed under various settings to analyze and validate the model. Finally, to evaluate the robotic humanoid venipuncture method, we apply the method to a self-developed six-degree-of-freedom venipuncture robot via rabbit ear veins with a success rate of approximately 90%. This work demonstrates that the humanoid venipuncture method based on the biomechanical model is practical and rapid in processing simple information in venipuncture robots.

Unmanned Aerial Vehicles (UAVs) or Unmanned Aircraft Systems (UASs) commonly called drones are relatively new entrants to the airspace. The regulatory agencies, numerous States and entities are involved in creation of the safe integration with manned aviation. The so-called U-space concept announced by the European Commission is one of the approaches to achieve that goal. There is also known concept of Unmanned Traffic Management (UTM) - a tool which would enable the services needed for safe conduct of UAV flights in generally accessible airspace. There are quite a few European projects which focuses on testing UTM capabilities in order to find a solution which could enable the market and ensure safe UAV operations. One of those systems is PansaUTM - which was developed in order to coordinate drone flights in different types of airspace in Poland. The first part of the paper will present an example of the implementation of this system as a foundation for new possible applications of drones and increasing number of operations. The conclusion of the first part of the article is that, in line rapid growth of UAS flights and different applications of drone services, the European drone ecosystem should evolve even further to deploy very complex drone operations in scalable manner. In order to accommodate unmanned air taxi operations, cargo flights, medical cargo flights, automatic surveillance flights, etc. Europe is preparing towards deployment of Advanced Air Mobility (AAM). The second part of the text indicate the possibility of extensive use of drones in medical logistics as well as minimizing the epidemiological risk as a result of the use of this mean of transport. At the same time, it should be stressed out that the medical transport using drones can be used in urgent situations, where the main variable that has an impact on the success of life and health saving is the breaking of barriers to reaching difficult-to-reach places. In addition, the development of transport using drones can have a lasting impact on improving the quality of life of chronically ill patients who experience severe disease recurrence and thus on the need to implement emergency prevention or treatment measures. The second part of the article focuses as well on the U-space concept as an opportunity for UAVs to be widely used in the field of day-to-day supplies as well as health-related supplies. In the context of the spread of SARS-CoV-2 virus, drones may be used to provide diagnostic screening tests, medicinal products and septic materials, transport of samples of biological material, as well as an information campaign on how to deal with an epidemic, quarantine or isolation at home. The use of UAV for medical supplies is economically and legally justified. The U-space environment from the operational and regulatory side is a multidisciplinary approach that requires the interaction of aviation, law, medicine, robotics, mechatronics and engineering experts. The legal framework for the development of U-space should be taken into account, as well as sector-specific regulations taking into account the principles of the use of drones in strictly defined areas, including in the process of medical supply, and liability for damage caused by UAV medical supply or AI-controlled intelligent machines.

In this paper, we present a Decomposition Coordination (DC) method applied to solve the problem of safe trajectory planning for autonomous Unmanned Aerial Vehicle (UAV) in a dynamic environment. The purpose of this study is to make the UAV more reactive in the environment and ensure the safety and optimality of the computed trajectory. In this implementation, we begin by selecting a dynamic model of a fixed-arms quadrotor UAV. Then, we define our multi-objective optimization problem, which we convert afterward into a scalar optimization problem (SOP). The SOP is subdivided after that into smaller sub-problems, which will be treated in parallel and in a reasonable time. The DC principle employed in our method allows us to treat non-linearity at the local level. The coordination between the two levels is achieved after that through the Lagrange multipliers. Making use of the DC method, we can compute the optimal trajectory from the UAV's current position to a final target practically in real-time. In this approach, we suppose that the environment is totally supervised by a Ground Control Unit (GCU). To ensure the safety of the trajectory, we consider a wireless communication network over which the UAV may communicate with the GCU and get the necessary information about environmental changes, allowing for successful collision avoidance during the flight until the intended goal is safely attained. The analysis of the DC algorithm's stability and convergence, as well as the simulation results, are provided to demonstrate the advantages of our method and validate its potential.

A common issue with many commercial rehabilitative exoskeletons and orthoses are that they can be prohibitively expensive for an average individual to afford without additional financial support. Due to this a user may have limited to the usage of such devices within set rehabilitation sessions as opposed to a continual usage. The purpose of this review is therefore to find which actuator implementations would be most suitable for a simplistic, low-cost powered orthoses capable of assisting those with pathologic gait disorders by collating literature from Web of Science, Scopus, and Grey Literature. In this systematic review paper 127 papers were selected from these databases via the PRISMA guidelines, with the financial costs of 25 actuators discovered with 11 distinct actuator groups identified. The review paper will consider a variety of actuator implementations used in existing lower-limb exoskeletons that are specifically designed for the purpose of rehabilitating or aiding those with conditions inhibiting natural movement abilities, such as electric motors, hydraulics, pneumatics, cable-driven actuators, and compliant actuators. Key attributes such as technical simplicity, financial cost, power efficiency, size limitations, accuracy, and reliability are compared for all actuator groups. Statistical findings show that rotary electric motors (which are the most common actuator type within collated literature) and compliant actuators (such as elastic and springs) would be the most suitable actuators for a low-cost implementation. From these results, a possible actuator design will be proposed making use of both rotary electric motors and compliant actuators.

Incidents of hydraulic or oil spills in the oceans/seas or ports occur with some regularity during the exploitation, production and transportation of petroleum products. Immediate, safe, effective and environmentally friendly measures must be adopted to reduce the impact of the oil spill on marine life. Due to the difficulty to detect and clean these areas, semi-autonomous vehicles can make a significant contribution by implementing a cooperative and coordinated response. The paper proposes a concept study of Hybrid Monitoring Detection and Cleaning System (HMDCS-UV) for a maritime region using semi-autonomous unmanned vehicles. This system is based on a cooperative decision architecture for an unmanned aerial vehicle to monitor and detect dirty zones (i.e., hydraulic spills), and clean them up using a swarm of unmanned surface vehicles. The proposed solutions were implemented in a real cloud and were evaluated using different simulation scenarios. Experimental results show that the proposed HMDCS-UV can detect and reduce the level of hydraulic pollution in maritime regions with a significant gain in terms of energy consumption.

Swarm robotic systems comprising members with limited onboard localization capabilities rely on employing collaborative motion-control strategies to successfully carry out multi-task missions. Such strategies impose constraints on the trajectories of the swarm and require the swarm to be divided into worker robots that accomplish the tasks at hand, and support robots that facilitate the movement of the worker robots. The consideration of the constraints imposed by these strategies is essential for optimal mission-planning. Existing works have focused on swarms that use leader-based collaborative motion-control strategies for mission execution and are divided into worker and support robots prior to mission-planning. These works optimize the plan of the worker robots and, then, use a rule-based approach to select the plan of the support robots for movement facilitation - resulting in a sub-optimal plan for the swarm. Herein, we present a mission-planning methodology that concurrently optimizes the plan of the worker and support robots by dividing the mission-planning problem into five stages: division-of-labor, task-allocation of worker robots, worker robot path-planning, movement-concurrency, and movement-allocation. The proposed methodology concurrently searches for the optimal value of the variables of all stages. The proposed methodology is novel as it (1) incorporates the division-of-labor of the swarm into worker and support robots into the mission-planning problem, (2) plans the paths of the swarm robots to allow for concurrent facilitation of multiple independent worker robot group movements, and (3) is applicable to any collaborative swarm motion-control strategy that utilizes support robots. A unique pre-implementation estimator, for determining the possible improvement in mission execution performance that can achieved through the proposed methodology was also developed to allow the user to justify the additional computational resources required by it. The estimator uses a machine learning model and estimates this improvement based on the parameters of the mission at hand. Extensive simulated experiments showed that the proposed concurrent methodology improves the mission execution performance of the swarm by almost 40% compared to the competing sequential methodology that optimizes the plan of the worker robots first and, then, the plan of the support robots. The developed pre-implementation estimator was shown to achieve an estimation error of less than 5%.