Abstract
Unmanned blimps are a safe and reliable alternative to conventional drones when flying above people. On-board real-time tracking of their pose and velocities is a necessary step toward autonomous navigation. There is a need for an easily deployable technology that is able to accurately and robustly estimate the pose and velocities of a blimp in 6 DOF, as well as unexpected applied forces and torques, in an uncontrolled environment. We present two multiplicative extended Kalman filters using ultrawideband radio sensors and a gyroscope to address this challenge. One filter is updated using a dynamics model of the blimp, whereas the other uses a constant speed model. We describe a set of experiments in which these estimators have been implemented on an embedded flight controller. They were tested and compared in accuracy and robustness in a hardware-in-loop simulation as well as on a real blimp. This approach can be generalized to any lighter than air robot to track it with the necessary accuracy, precision, and robustness to allow autonomous navigation.
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