Emerging Wireless Technologies for Reliable Indoor Navigation in Industrial Environments

Reliable positioning systems are key drivers for location-based services in smart logistics and internet of things (IoT) applications amid the era of Industry 4.0. They are the foundation blocks upon which navigation applications are built for all client segments ranging from public individuals to industrial firms. This research article investigates the existing wireless radio technologies from a low-cost opportunistic perspective to provide precise positioning for dense indoor scenarios. In indoor scenarios, it is a rule of thumb that modern humans spend more than 90% of their time inside buildings, and yet, only a few indoor positioning systems are: less available, more expensive, more disruptable, and/or less accurate. One major reason is that the current indoor positioning technologies are compromising the system performance with other essential metrics such as the overall cost. For instance, the most accurate (millimeter level) indoor positioning technology -so far- is the LASER technology, however, it is massively expensive. On the other hand, some of the existing low-cost positioning technologies for indoor venues are less accurate, besides having other performance drawbacks. One prominent solution for dense indoor situations is Ultra-wideband (UWB) technology, as it provides a positive trade-off between operational costs and system performance. UWB has recently emerged to deliver precise indoor positioning solutions within a centimeter level of accuracy while being a reliable low-cost technology. It is foreseen that UWB will be embedded inside many smartphone models in the near future, some phone manufacturers have already started adopting UWB-chips in 2019 such as Apple and Samsung. Moreover, the FiRa consortium was established by giant founding companies such as Cisco, Google, Samsung, BOSCH, Apple, and Qualcomm, to promote UWB as an indoor positioning technology. Quoting from the FiRa website as follows “UWB is the most effective available technology for delivering accurate ranging and positioning in challenging real-world environments, allowing devices to add real-time spatial context and enabling new user experiences”. Previously in 2021, we conducted a thorough review study on UWB, in which we concluded that UWB is an industrial-friendly technology that can provide higher rates of accuracy while maintaining continuous service levels at low costs. In a well-established industrial laboratory in the Ostrobothnia region, Technobothnia, we performed technical experiments to deliver precise UWB positioning for individuals and mobile assets such as autonomous robots. Aided by sensitive inertial motion units (IMUs), the results showed that the UWB precision positioning in a dense challenging environment (i.e. Technobothnia) has been achieved to mean absolute error of 3.7 centimeters accuracy, and a Wi-Fi positioning accuracy of 4.5 meters. Our future objective is to further improve the positioning accuracy and reliability to facilitate autonomous operations by mobile robots in the lab. Later, our system will be made open to the public use e.g. for students, visitors, and staff as beneficiaries. Besides UWB technology, there are some additional opportunistic methods that are less accurate (ultra-meter(s) accuracy) however, can be assisted with other multisensor technologies to achieve reliable positioning solutions. The methods that are being investigated are Bluetooth low energy (BLE) and Wi-Fi positioning. The idea is based on the signal-of-opportunity (SOP) paradigm, in which the positioning solution is rendered by measuring one of the radio signal properties, that is, the received signal strength indicator (RSSI). By integrating with IMU sensors, the multisensor combinations BLE/IMU and WiFi/IMU can result in meter-level accuracy, which can be regarded as reliable positioning for certain indoor applications. Moreover, the use of both techniques is foreseen to be sufficient for laboratory mobile activities, as their typical multisensor positioning accuracy can range between 1.5 to 3 meters. The main objective of our proposed method is to refine the achieved positioning accuracy from the multisensor system using a series of algorithmic remedies. First, the positioning solutions are filtered via Bayesian filters to remove the noisy effects and DC offsets. Then, the multisensor scheme is selected as either loose or tight coupling to integrate the IMU readings with the radio-based RSSI estimations and overcome the non-line-of-sight (NLOS) effects. Afterward, the fused solution is treated with recursive Kalman smoothers to further refine the positioning traces, and remove sharpness and stationary effects. The final (optional) step is to apply some machine learning algorithms to adapt navigation routes to the real surroundings based on the collected training data. As a ground truth for all systems and also as training data, an accurate mobile robot is used to bear all sensors together during the experiments. The robot is equipped with LiDARs, ultrasounds, radars, and LASERs that can achieve millimeter accuracy, hence, the data recorded by its sensors are treated as ground truth as well as training data. We also developed an embedded system that synchronizes all data pools together coming from different sensors (UWB – WiFi – BLE – IMU) in the same time frame. In summary, this article studies the indoor navigation opportunities created by existing radio positioning technologies that are tailored for industrial use cases, using multisensor fusion methods for both precise and fingerprinting techniques. A special focus was given to UWB technology as it will be abundant in the near future by embedding smartphones, home appliances, and body wearables with commercial-grade UWB chips, which was already started in 2019. Additionally, we focused on the Wi-Fi based IPS for mobile assets (e.g., robots and humans) that are not necessarily requiring to have precise positioning, few meters of accuracy are becoming sufficient. The existing infrastructure in Technobothnia is currently being customized to embed BLE hardware into the designed IPS, however, the software part has been already implemented. Those RSSI-based indoor positioning methods are becoming trending in large indoor venues (e.g., airports, malls, and train stations) as well as being adopted by major positioning corporations. Consequently, we studied some algorithmic optimization techniques to transform both fingerprinting methods into more accurate reliable techniques. As future research, our proposed methods can be further adapted to be applied for real-time applications, and ensuring that all technologies (including BLE) are functional and up running.