Abstract
Switched reluctance machines are gaining importance due to their low cost, simple construction, and non-use of rare earth magnets. However, for the development of advanced torque controllers, accurate torque estimation is crucial, especially under varying load conditions. There are different torque estimation methods, which fall into different well-established classes, however, the characterization of their performance and operating conditions are not well known. This paper provides a comparative study of the most significant estimation algorithms: average torque, analytical and area approximation estimators. To assess the performance of these algorithms, a set of numerical simulations is presented and their results are compared based on signal similarity criteria. Results show a better performance when using the area approximation algorithm in comparison with the other two.

Abstract

Abstract
The pose estimation of a mobile robotic system is essential in many autonomous applications. Inertial sensors provide high-frequency measurements that can be used to estimate the displacement, however, for estimating the orientation, an additional filter is required. Some of the newest Attitude and Heading Reference Systems can provide a referenced estimation of the orientation of the device, allowing it to retrieve the orientation of a robotic system. However, magnetic field perturbations caused by ferromagnetic objects or induced magnetic fields might influence these systems and, consequently, lead to the accumulation of errors over time. In this paper, the performance of the Xsens fusion filter is compared with a stateof-the-art algorithm to estimate the orientation of the system under dynamic movements and in the presence of magnetic perturbations, with the goal of finding the most suitable for an Unmanned Aerial Vehicle. The results show that both filters are robust and perform well in the target scenario, with a root mean squared error between 2 and 5 degrees; however, the Xsens fusion filter does not require an extra computer to process the data.

Abstract
The recent advances in precision agriculture are due to the emergence of modern robotics systems. For instance, unmanned aerial systems (UASs) give new possibilities that advance the solution of existing problems in this area in many different aspects. The reason is due to these platforms' ability to perform activities at varying levels of complexity. Therefore, this research presents a multiple-cooperative robot solution for UAS and unmanned ground vehicle (UGV) systems for their joint inspection of olive grove inspect traps. This work evaluated the UAS and UGV vision-based navigation based on a yellow fly trap fixed in the trees to provide visual position data using the You Only Look Once (YOLO) algorithms. The experimental setup evaluated the fuzzy control algorithm applied to the UAS to make it reach the trap efficiently. Experimental tests were conducted in a realistic simulation environment using a robot operating system (ROS) and CoppeliaSim platforms to verify the methodology's performance, and all tests considered specific real-world environmental conditions. A search and landing algorithm based on augmented reality tag (AR-Tag) visual processing was evaluated to allow for the return and landing of the UAS to the UGV base. The outcomes obtained in this work demonstrate the robustness and feasibility of the multiple-cooperative robot architecture for UGVs and UASs applied in the olive inspection scenario.

Abstract
A recent relevant innovation in last-mile delivery is to consider the possibility of goods being delivered by couriers appointed through crowdsourcing. In this paper we focus on the setting of in-store customers delivering goods, ordered by online customers, on their way home. We assume that not all the proposed delivery tasks will necessarily be accepted, and use logistic regression to model the crowd agents' willingness to undertake a delivery. This model is then used to build a novel compensation scheme that determines reward values, based on the current plan for the professional fleet's routes and on the couriers' probabilities of acceptance, by employing a direct search algorithm that seeks to minimise the expected cost.

Abstract
Purpose: Responsible innovation assessment tools (RIATs) are key instruments that can help organizations, associations and individuals measure responsible innovation. Accordingly, this study aims to review the current status of research on responsible innovation and, in particular, of studies that either present the relevance of RIATs or provide empirical evidence of their adoption. Design/methodology/approach: A systematic literature review is conducted to identify and review how RIATs are being addressed in academic research and the applications that are proposed. A systematic process is implemented using the Web of Science and Scopus bibliographic databases, aiming not only to summarize existing studies, but also to include a perspective on gaps and future research. Findings: A total of 119 publications were identified and included in the review process. The study identifies that RIATs have attracted growing interest from the scientific community, with a greater predominance of studies involving qualitative and mixed methods. A well-balanced mix of conceptual and exploratory studies is also registered, with a greater predominance of analysis of RIATs application domains in the past years, with greater incidence in the finance, water, energy, construction, manufacturing and health sectors. Originality/value: This study is pioneering in identifying 16 dimensions and 60 sub-dimensions for measuring responsible innovation. It also suggests the need to include multidimensional perspectives and individuals with interdisciplinary competencies in this process.