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Register a new userJoint Indoor Localization and Navigation of UAVs for Network Formation Control
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In this paper, we propose a joint indoor localization and navigation algorithm to enable a swarm of unmanned aerial vehicles (UAVs) to deploy in a specific spatial formation in indoor environments. In the envisioned scenario, we consider a static user acting as a central unit whose main task is to acquire all the UAV measurements carrying position-dependent information and to estimate the UAV positions when there is no existing infrastructure for positioning. Subsequently, the user exploits the estimated positions as inputs for the navigation control with the aim of deploying the UAVs in a desired formation in space (formation shaping). The user plans the trajectory of each UAV in real time, guaranteeing a safe navigation in the presence of obstacles. The proposed algorithm guides the UAVs to their desired final locations with good accuracy.
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In this paper, we propose a distributed solution to the navigation of a population of unmanned aerial vehicles (UAVs) to best localize a static source. The network is considered heterogeneous with UAVs equipped with received signal strength (RSS) sensors from which it is possible to estimate the distance from the source and/or the direction of arrival through ad-hoc rotations. This diversity in gathering and processing RSS measurements mitigates the loss of localization accuracy due to the adoption of low-complexity sensors. The UAVs plan their trajectories on-the-fly and in a distributed fashion. The collected data are disseminated through the network via multi-hops, therefore being subject to latency. Since not all the paths are equal in terms of information gathering rewards, the motion planning is formulated as a minimization of the uncertainty of the source position under UAV kinematic and anti-collision constraints and performed by 3D non-linear programming. The proposed analysis takes into account non-line-of-sight (NLOS) channel conditions as well as measurement age caused by the latency constraints in communication.
While multiple studies have proposed methods for the formation control of unmanned aerial vehicles (UAV), the trajectories generated are generally unsuitable for tracking targets where the optimum coverage of the target by the formation is required at all times. We propose a path planning approach called the Flux Guided (FG) method, which generates collision-free trajectories while maximising the coverage of one or more targets. We show that by reformulating an existing least-squares flux minimisation problem as a constrained optimisation problem, the paths obtained are $1.5 times$ shorter and track directly toward the target. Also, we demonstrate that the scale of the formation can be controlled during flight, and that this feature can be used to track multiple scattered targets. The method is highly scalable since the planning algorithm is only required for a sub-set of UAVs on the open boundary of the formations surface. Finally, through simulating a 3d dynamic particle system that tracks the desired trajectories using a PID controller, we show that the resulting trajectories after time-optimal parameterisation are suitable for robotic controls.
The existing localization systems for indoor applications basically rely on wireless signal. With the massive deployment of low-cost cameras, the visual image based localization become attractive as well. However, in the existing literature, the hybrid visual and wireless approaches simply combine the above schemes in a straight forward manner, and fail to explore the interactions between them. In this paper, we propose a joint visual and wireless signal feature based approach for high-precision indoor localization system. In this joint scheme, WiFi signals are utilized to compute the coarse area with likelihood probability and visual images are used to fine-tune the localization result. Based on the numerical results, we show that the proposed scheme can achieve 0.62m localization accuracy with near real-time running time.
Localization is important for a large number of Internet of Things (IoT) endpoint devices connected by LoRaWAN. Due to the bandwidth limitations of LoRaWAN, existing localization methods without specialized hardware (e.g., GPS) produce poor performance. To increase the localization accuracy, we propose a super-resolution localization method, called Seirios, which features a novel algorithm to synchronize multiple non-overlapped communication channels by exploiting the unique features of the radio physical layer to increase the overall bandwidth. By exploiting both the original and the conjugate of the physical layer, Seirios can resolve the direct path from multiple reflectors in both indoor and outdoor environments. We design a Seirios prototype and evaluate its performance in an outdoor area of 100 m $times$ 60 m, and an indoor area of 25 m $times$ 15 m, which shows that Seirios can achieve a median error of 4.4 m outdoors (80% samples < 6.4 m), and 2.4 m indoors (80% samples < 6.1 m), respectively. The results show that Seirios produces 42% less localization error than the baseline approach. Our evaluation also shows that, different to previous studies in Wi-Fi localization systems that have wider bandwidth, time-of-fight (ToF) estimation is less effective for LoRaWAN localization systems with narrowband radio signals.
Indoor localization has drawn much attention owing to its potential for supporting location based services. Among various indoor localization techniques, the received signal strength (RSS) based technique is widely researched. However, in conventional RSS based systems where the radio environment is unconfigurable, adjacent locations may have similar RSS values, which limits the localization precision. In this paper, we present MetaRadar, which explores reconfigurable radio reflection with a surface/plane made of metamaterial units for multi-user localization. By changing the reflectivity of metamaterial, MetaRadar modifies the radio channels at different locations, and improves localization accuracy by making RSS values at adjacent locations have significant differences. However, in MetaRadar, it is challenging to build radio maps for all the radio environments generated by metamaterial units and select suitable maps from all the possible maps to realize a high accuracy localization. To tackle this challenge, we propose a compressive construction technique which can predict all the possible radio maps, and propose a configuration optimization algorithm to select favorable metamaterial reflectivities and the corresponding radio maps. The experimental results show a significant improvement from a decimeter-level localization error in the traditional RSS-based systems to a centimeter-level one in MetaRadar.
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