UAS See-and-Avoid Strategy using a Fuzzy Logic Controller Optimized by Cross-Entropy in Scaling Factors and Membership Functions.

This work aims to develop a novel Cross-Entropy (CE) optimization-based fuzzy controller for Unmanned Aerial Monocular Vision-IMU System (UAMVIS) to solve the seeand-avoid problem using its accurate autonomous localization information. The function of this fuzzy controller is regulating the heading of this system to avoid the obstacle, e.g. wall. In the Matlab Simulink-based training stages, the Scaling Factor (SF) is adjusted according to the specified task firstly, and then the Membership Function (MF) is tuned based on the optimized Scaling Factor to further improve the collison avoidance performance. 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 flight tests with a quadcopter have been done. The experimental results show that this approach precisely navigates the system to avoid the obstacle. To our best knowledge, this is the first work to present the optimized fuzzy controller for UAMVIS using Cross-Entropy method in Scaling Factors and Membership Functions optimization.

A General Purpose Configurable Navigation Controller for Micro Aerial Multirotor Vehicles

In this paper, we consider the problem of autonomous navigation of multirotor platforms in GPS-denied environments. The focus of this work is on safe navigation based on unperfect odometry measurements, such as on-board optical flow measurements. The multirotor platform is modeled as a flying object with specific kinematic constraints that must be taken into account in order to obtain successful results. A navigation controller is proposed featuring a set of configurable parameters that allow, for instance, to have a configuration setup for fast trajectory following, and another to soften the control laws and make the vehicle navigation more precise and slow whenever necessary. The proposed controller has been successfully implemented in two different multirotor platforms with similar sensoring capabilities showing the openness and tolerance of the approach. This research is focused around the Computer Vision Group's objective of applying multirotor vehicles to civilian service applications. The presented work was implemented to compete in the International Micro Air Vehicle Conference and Flight Competition IMAV 2012, gaining two awards: the Special Award on "Best Automatic Performance - IMAV 2012" and the second overall prize in the participating category "Indoor Flight Dynamics - Rotary Wing MAV". Most of the code related to the present work is available as two open-source projects hosted in GitHub.