Autonomous Mobile Robots (AMRs) are essential for automating internal transportation in Industry 4.0, relying on visual-based positioning systems to generate maps and compare their location with mapped reference obstacles. However, enhancing positioning accuracy is crucial for demanding industrial tasks. Combining vision-based systems with Ultra-Wideband (UWB) time-based positioning systems has gained research interest. In this sense, UWB based localization systems are commonly categorized into synchronous and asynchronous configurations. Synchronous systems, such as the Time of Arrival (TOA) and the Time Difference of Arrival (TDOA) systems, require clock synchronization but reduce signal path lengths. In contrast, asynchronous systems, such as Asynchronous Time Difference of Arrival (A-TDOA) and Single-Sided Two-Way Ranging (SS-TWR), eliminate synchronization needs but may involve longer signal paths. To address these differences, in this paper, we propose a methodology to fairly compare these configurations. The approach includes characterizing the error sources in each system and analyzing the optimal spatial arrangement of sensors under incremental time reply conditions. Results reveal the superiority of asynchronous configurations in low sensor density deployments, achieving up to a 54.8% reduction in localization error compared to synchronous systems. In addition, as the number of deployed nodes decreases, synchronous systems show up to a 28.9% increase in localization error to the analysis performed, whereas the asynchronous systems achieved a 24.8% increase for the A-TDOA, and only a 9.2% increase in the case of the SS-TWR method. These findings prompt further investigation into the suitability of synchronous positioning for minimizing errors in large-scale industrial applications. • Error bounds of TOA, TDOA, A-TDOA, and SS-TWR with noise, multipath, and clock errors • Comparison of synchronous and asynchronous methods in a sensor deployment via BWO • Consideration of discontinuous node regions into BWO to improve representability

Ultra-Wide-Band (UWB) technology allows for partially mitigating the NLOS and multipath effects of time-based localization in low-range applications. Thus, it has been widely proposed for indoor navigation, reaching very promising results with mature technology already available. However, an analysis of the suitability of different synchronous and asynchronous time-based architectures can provide valid conclusions for the future development of this field. For this reason, we perform in this paper a fair comparison of two traditional synchronous architectures (TOA and TDOA) and one asynchronous architecture (A-TDOA) defining the lowest error bounds for each architecture in an indoor industrial scenario devised for UAV navigation. Results have shown that although current industrial time-based localization software is mainly based on TDOA (synchronous) and Two-Way-Range (asynchronous) architectures, asynchronous localization can statistically provide more accurate and stable positioning services in indoor industrial environments. These results encourage further experimentation in other different asynchronous architectures in the coming years.

Unmanned-Aerial-Vehicles (UAVs) represent an active research topic over multiple fields for performing inspection, delivery and surveillance applications among other operations. However, achieving the utmost efficiency requires drones to perform these tasks without the need of human intervention, which demands a robust and accurate localization system for achieving a safe and efficient autonomous navigation. Nevertheless, currently used satellite-based localization systems like GPS are insufficient for high-precision applications, especially in harsh scenarios like indoor and deep urban environments. In these contexts, Local Positioning Systems (LPS) have been widely proposed for satisfying the localization requirements of these vehicles. However, the performance of LPS is highly dependent on the actual localization architecture and the spatial disposition of the deployed sensor distribution. Therefore, before the deployment of an extensive localization network, an analysis regarding localization architecture and sensor distribution should be taken into consideration for the task at hand. Nonetheless, no actual study is proposed either for comparing localization architectures or for attaining a solution for the Node Location Problem (NLP), a problem of NP-Hard complexity. Therefore, in this paper, we propose a comparison among synchronous LPS for determining the most suited system for localizing UAVs over urban scenarios. We employ the Cràmer-Rao-Bound (CRB) for evaluating the performance of each localization system, based on the provided error characterization of each synchronous architecture. Furthermore, in order to attain the optimal sensor distribution for each architecture, a Black-Widow-Optimization (BWO) algorithm is devised for the NLP and the application at hand. The results obtained denote the effectiveness of the devised technique and recommend the implementation of Time Difference Of Arrival (TDOA) over Time of Arrival (TOA) systems, attaining up to 47% less localization uncertainty due to the unnecessary synchronization of the target clock with the architecture sensors in the TDOA architecture.

TV sports filming has improved over the years offering the spectator novel forms of following their preferred sport from home. In this sense, Unmanned Aerial Vehicles (UAVs) are introducing novel aerial perspectives of live action that are very attractive to the fans. However, the deployment of UAVs is compromised by their navigation to the exact location from where these images can be taken. This is an even more challenging problem in indoor environments where GNSS signals are significantly degraded. For this purpose, we propose in this paper the deployment of an asynchronous optimized sensor network that can attain the required accuracy for the indoor accurate navigation of UAVs for TV filming purposes. The results attained have proven the effectiveness of our proposal in an indoor pavilion where university sporting competitions take place.

The efficient design of the aerodynamic surfaces in the aircraft allows the optimal performance of the vehicle and the reduction of the fuel consumption. Among these surfaces, the wing is the main contributor to the force which lifts the aircraft enabling the flight. However, the application of this force out of the center of gravity generates a moment that must be balanced through a force applied in the Horizontal-Tail-Plane (HTP) reaching the longitudinal trim of the aircraft. Traditionally, the design of the wing and HTP have been performed iteratively attaining suboptimal or time ineffective results. In this paper, we solve this problem through the application of a Genetic Algorithm for the combined optimization of the wing and the HTP by adjusting the aspect ratio, the taper ratio, the twist angle and the incidence angle of both surfaces to produce the optimal balance of lift adjusting its distribution to an elliptical configuration and enabling the longitudinal trim. Results show the automatic adjustment of the parameters of the aerodynamic surfaces thus fulfilling the objectives of this paper.