The next generation of high-accuracy positioning services is required to satisfy the sub-meter accuracy level for more than 95% of the network area, including indoor, outdoor, and urban deployments. In this vein, inter-agent measurements appear to provide additional position information and, hence, have the capacity to boost localization accuracy . This paper researches cooperative positioning techniques by means of device-to-device (D2D) and reconfigurable intelligent surfaces (RIS) technologies leveraging received signal strength (RSS) based ranging. We estimate the maximum capacities of the positioning systems in terms of accuracy through the Gaussian noise model, proposed universal theoretical distance-dependent noise model, and empirical noise model. We also evaluate the positioning error achieved by combining two or more technologies. Numerical results reveal the use cases advantageous for RIS- and D2D-aided localization. Then, based on the results, valuable guidelines are derived on the optimal sensor fusion metric – median – that minimizes the mean error of the cooperative localization.

The growing need for accurate localization in increasingly interconnected industrial environments has driven research towards developing new indoor localization systems. This work analyzes and compares three asynchronous localization methods based on the Two-Way-Ranging (TWR) protocol: Single-Sided TWR (SS-TWR), Symmetric Double-Sided TWR (SDS-TWR), and Alternative Double-Sided TWR (AltDS-TWR) in addition to the Asynchronous Time Difference of Arrival (A-TDOA) system. Similar comparisons have been previously reported, however, these only take into consideration clock-associated errors and unrealistic test conditions, thus overlooking the impact of signal paths required by each method, therefore reaching conclusions far from those expected in real applications. In this paper, we propose a more complete and fair comparison among these four asynchronous systems. For this purpose, we propose a clock, noise, and multipath error characterization for each localization system to perform a realistic comparison over multiple industrial scenarios where Autonomous Mobile Robots freely navigate. In order to ensure a fair comparison, a sensor distribution optimization has been carried out for attaining the best achievable performance of each analyzed system. Results show that the selection of the best localization system may depend on the scenario and application conditions as well as the deployment budget. Nevertheless, results from the AltDS-TWR method highlight the potential of this system, yet further research should be conducted to verify the influence of moving targets for this TWR method. • The error-bounds characterization of the SS-TWR, SDS-TWR, AltDS-TWR methods and the A-TDOA localization systems under noise, multipath and clock-related errors in indoor environments. • The proposal of a realistic comparison of the attainable performance among the 4 characterized localization systems over an industrial scenario of deployment under different conditions. • The optimization of the compared sensor distributions in accordance with the respective path and clock related error-bounds for guaranteeing a fair comparison among the 4 localization systems.

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.

Local Positioning Systems (LPS) are supposing an attractive research topic over the last few years. LPS are ad-hoc deployments of wireless sensor networks for particularly adapt to the environment characteristics in harsh environments. Among LPS, those based on temporal measurements stand out for their trade-off among accuracy, robustness and costs. But, regardless the LPS architecture considered, an optimization of the sensor distribution is required for achieving competitive results. Recent studies have shown that under optimized node distributions, time-based LPS cumulate the bigger error bounds due to synchronization errors. Consequently, asynchronous architectures such as Asynchronous Time Difference of Arrival (A-TDOA) have been recently proposed. However, the A-TDOA architecture supposes the concentration of the time measurement in a single clock of a coordinator sensor making this architecture less versatile. In this paper, we present an optimization methodology for overcoming the drawbacks of the A-TDOA architecture in nominal and failure conditions with regards to the synchronous TDOA. Results show that this optimization strategy allows the reduction of the uncertainties in the target location by 79% and 89.5% and the enhancement of the convergence properties by 86% and 33% of the A-TDOA architecture with regards to the TDOA synchronous architecture in two different application scenarios. In addition, maximum convergence points are more easily found in the A-TDOA in both configurations concluding the benefits of this architecture in LPS high-demanded applications.