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Visual Control

Autonomous Landing of an Unmanned Aerial Vehicle using Image-Based Fuzzy Control

Abstract: This paper presents a vision based autonomous landing control approach for unmanned aerial vehicles (UAV). The 3D position of an unmanned helicopter is estimated based on the homographies estimated of a known landmark. The translation and altitude estimation of the helicopter against the helipad position are the only information that is used to control the longitudinal, lateral and descend speeds of the vehicle. The control system approach consists in three Fuzzy controllers to manage the speeds of each 3D axis of the aircraft's coordinate system. The 3D position estimation was proven

autonomous landing:
GPS reconstruction

Fuzzy Control Optimization using Cross-Entropy: Tuning the Membership Functions and Rules’ Weight to Control a UAV for See and Avoid

Define and tune a controller is a hard task to accomplish. Soft-Computing based controllers are little bit easier because they can be defined without knowing the model of the system to control. These type of controllers are defined using heuristic information, or based on expert knowledge. Classical method of optimization and definition are presented in the literature, as neural networks or genetic algorithms. The recently developed Cross-Entropy (CE) optimization method is a general Monte-Carlo approach to combinatorial and continuous multi-extremal optimization and importance sampling. This work presents a novel approach of this method to tune Fuzzy Logic Controllers (FLC). The control system to optimize is a vision based heading controller of an unmanned aerial vehicle (UAV) for See and avoid. The optimization process is focused on modify the size and the position of the membership functions’ sets of each variable and the rules’ weight to adapt the control system for this specific task. This has done in a simulated environment developed using Matlab. Two optimized controllers at different stages were tested in indoor tests with an AR.Drone at different speeds. The obtained results show the powerful performance of this technique to be apply for Fuzzy controller optimization. Not only the behavior of the controller was improved in few iterations, but also a big reduction of the Fuzzy controller’s rules was obtained

 

Cross-Entropy Optimized Fuzzy Logic Controllers for UAVs

Cross-Entropy Optimized Fuzzy Logic Controllers for UAVs 1. Scaling Factors 2. Membership Functions 3. Rule Weights