This book presents an algorithmic solution for the state estimation problem of unmanned aerial vehicles, or UAVs.
The necessary usage of multiple miniaturized low-cost and low-performance sensors integrated into mini-RPAS, which are subjected to hard space requirements or power consumption constraints, has led to a need to design nonlinear observers for data fusion, unmeasured systems state estimation and/or flight path reconstruction. Exploiting the capabilities of nonlinear observers makes it possible to extend the ways mini-RPAS can be controlled while enhancing their intrinsic flight handling qualities. Numerous studies related to RPAS certification and integration into civil airspace therefore focus on highly robust estimation algorithms. The development of reliable and effective aided-INS for nonlinear dynamic systems is thus an important research topic and a major concern in the aerospace engineering community.
This book proposes a novel approach regarding nonlinear state estimation, called IUKF (Invariant Unscented Kalman Filter), which is based on both invariant filter estimation and UKF theoretical principles. The IUKF estimation algorithm is described and its performances are evaluated by exploiting real flight test data. The whole approach has been implemented onboard using a data logger based on the well-known Paparazzi system. The results show promising perspectives and demonstrate that nonlinear state estimation converges on a much bigger set of trajectories than the more traditional approaches.
1. Introduction to Aerial Robotics.
2. The State of the Art.
3. Inertial Navigation Models.
4. The IUKF and pi-IUKF Algorithms.
5. Methodological Validation, Experiments and Results.
Jean-Philippe Condomines is Assistant Professor in Guidance Navigation and Control in the UAV team at the French National Civil Aviation University (ENAC) in Toulouse, France.
Table of Contents
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