Ultra-wideband-based navigation for unmanned aerial vehicles
Date of Issue2018-12-31
School of Electrical and Electronic Engineering
Micro unmanned aerial vehicles (UAVs) play more and more important roles in both civilian and military applications. Currently, the navigation and control of UAVs is critically dependent on the localization service provided by the Global Positioning System (GPS), which suffers from the multipath effect and blockage of line-of-sight, and fails to work in an indoor, forest or urban environment. Therefore, how to achieve positioning in a spatially restricted area or dense environment, say woods, multi-functional office or urban canyons, with sufficient localization accuracy that can support autonomous flight is a worthy research topic. This thesis concentrates on GPS-denied localization for UAVs by leveraging a radio frequency (RF) technology - ultra-wideband (UWB) signal. We shall devise algorithms and develop systems for localization and apply the location estimates to multi-UAV navigation and formation control. In the first part of this thesis, we establish a localization system for UAVs based on UWB ranging measurements to enable each UAV to estimate its own global position in a customized reference frame. To achieve the localization, a UWB module is installed on the UAV to actively send ranging requests to some fixed UWB modules at known positions (a.k.a., anchor nodes). Once a distance is obtained, it is calibrated first and then goes through outlier detection before being fed to a localization algorithm. The localization algorithm is initialized by trilateration and sustained by the extended Kalman filter (EKF). Afterwards, the position and velocity estimates produced by the algorithm will be further fed to the control loop to aid the navigation of the UAV. Flight tests in different environments have been conducted to validate the performance of our UWB-based localization system with anchor nodes. However, in many operation environments, it is impractical to have fixed anchor nodes due to large operational scale and area. Moreover, the accuracy of anchors' positions will greatly affect the positioning accuracy of UAVs. To reduce human labor and human-induced errors, in the second part of this thesis, we study an active relative localization (RL) with single landmark problem by using UAV's self-displacements and UWB ranging measurements. Note that in practice, a UAV may only be allowed to travel a small distance to fulfill the RL due to the environment or task constraint. To achieve an accurate RL estimate, we find a lower bound of mean square error in terms of this small distance and the sample size. As revealed from the lower bound, UAV only needs to enlarge the ranging span from the starting point to the ending point and increase the number of rangings to reduce the estimation error of the initial relative position, which is related to and extends the existing results of unconstrained optimal sensor placement. In this light, we design an algorithm to actively reduce the localization error by incrementally enlarging the ranging span, and apply EKF to account for the noise in the displacement measurements. Simulations and flight experiments have been conducted to validate our proposed RL strategy. With the booming development of low-cost micro UAV such as quadcopter, multi-UAV systems and relevant applications have been extensively studied recently, and most of the proposed multi-agent controllers rely on inter-agent relative positions which are assumed to be measurable. However, in practice, the relative position is difficult to obtain and so far there exists no commercial product for UAV to measure this information. In addition, most of existing formation experiment results including both simulation and actual flight still depend on some external infrastructure for positioning. Therefore, the third and fourth part of this thesis puts forth a simultaneous infrastructure-free cooperative RL and distributed formation control strategy for UAVs in GPS-denied environments. Instead of estimating relative coordinates by detecting specific patterns using image processing methods, an onboard UWB ranging and communication (RCM) network is adopted to sense both the inter-UAV distance and exchange information for RL estimation. Without any external infrastructures prepositioned, each UAV cooperatively performs the proposed consensus-based fusion method, which fuses the developed direct and indirect RL estimates, to generate the relative positions to its neighbors in real time despite the fact that some UAVs may not have direct range measurements to their neighbors. The RL estimates together with the relative velocity and inter-UAV distance measurements are used to control a UAV swarm. Both the cooperative RL and the formation control are implemented in a distributed fashion. Extensive real-world flight tests corroborate the merits of the developed simultaneous RL and formation control system.
DRNTU::Engineering::Electrical and electronic engineering