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Monocular Visual-Inertial SLAM-based Collision Avoidance Strategy for Fail-Safe UAV using Fuzzy Logic Controllers

Journal of Intelligent & Robotic Systems
Publication Date: 

In this paper, we developed a novel Cross-Entropy Optimization (CEO)-based Fuzzy
Logic Controller (FLC) for Fail-Safe UAV to expand its collision avoidance capabilities
in the GPS-denied envrionments using Monocular Visual-Inertial SLAM-based
strategy. The function of this FLC aims to control the heading of Fail-Safe UAV to avoid
the obstacle, e.g. wall, bridge, tree line et al, using its real-time and accurate
localization information. In the Matlab Simulink-based training framework, the Scaling
Factor (SF) is adjusted according to the collision avoidance task firstly, and then the
Membership Function (MF) is tuned based on the optimized Scaling Factor to further
improve the control performances. After obtained the optimal SF and MF, 64\% of rules
has been reduced (from 125 rules to 45 rules), and a large number of real see-and-
avoid tests with a quadcopter have done. The simulation and experiment results show
that this new proposed FLC can precisely navigates the Fail-Safe UAV to
avoid the obstacle, obtaining better performances compared to only SF optimization-
based FLC. To our best knowledge, this is the first work to present the optimized FLC
using Cross-Entropy method in both SF and MF optimization, and apply it in the UAV.