Monocular Visual-Inertial SLAM-Based Collision Avoidance Strategy for Fail-Safe UAV Using Fuzzy Logic Controllers

  • Authors:

  • Changhong Fu;Miguel A. Olivares-Mendez;Ramon Suarez-Fernandez;Pascual Campoy

  • Affiliations:

  • Computer Vision Group (CVG), Centro de Automática y Robótica (CAR), UPM-CSIC, Universidad Politécnica de Madrid, Madrid, Spain 28006;Automation Research Group, Interdisciplinary Centre for Security, Reliability and Trust, SnT-University of Luxembourg, Weicker, Luxembourg 2721;Computer Vision Group (CVG), Centro de Automática y Robótica (CAR), UPM-CSIC, Universidad Politécnica de Madrid, Madrid, Spain 28006;Computer Vision Group (CVG), Centro de Automática y Robótica (CAR), UPM-CSIC, Universidad Politécnica de Madrid, Madrid, Spain 28006

  • Venue:
  • Journal of Intelligent and Robotic Systems
  • Year:
  • 2014

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Abstract

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 environments 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.