In this paper, we propose an algorithm based on fuzzy control to ensure the stability of the quadrotor. After giving the nonlinear model of the robot, its representation by a Takagi-Sugeno (T-S) fuzzy model is first discussed. Next, a fuzzy controller are synthesized which guarantee desired control performances. The given controller is designed using numerical tools (Linear Matrix Inequalities-LMI). The simulation results show effectiveness and robustness of the proposed method.
Attitude is one of the most important parameters for a UAV during a flight. Attitude computation methods based vision generally use the horizon line as reference. However, the horizon line becomes an inadequate feature in urban environment. We then propose in this paper an omnidirectional vision system based on straight lines (very frequent in urban environment) that is able to compute the roll and pitch angles. The method consists in finding bundles of horizontal and vertical parallel lines in order to obtain an absolute reference for the attitude computation. We also develop here a new and efficient method for line extraction and bundle of parallel line detection. An original method of horizontal and vertical plane detection is also provided. We show experimental results on different images extracted from video sequences.
Attitude (roll and pitch) is an essential data for the navigation of a UAV. Rather than using inertial sensors, we propose a catadioptric vision system allowing a fast, robust and accurate estimation of these angles. We show that the optimization of a sky/ground partitioning criterion associated with the specific geometric characteristics of the catadioptric sensor provides very interesting results. Experimental results obtained on real sequences are presented and compared with inertial sensor measures.
In this paper, we propose an application of an algorithm, based on the T-S (Takagi-Sugeno) technique, to stabilize a quadrotor helicopter. After giving the nonlinear model of the vehicle, its representation by a T-S fuzzy model is discussed. Following this, a fuzzy controller is synthesized, which will guarantee the stability of the quadrotor. The proposed T-S controller is designed with measurable premise variables and the conditions of stability are given in terms of linear matrix inequality (LMI). Simulations and real-time experiments using a test-bed platform prove the performance of a PDC control algorithm to stabilize the vehicle robustly at a desired set point.
In this paper we present a dynamic localization system, allowing a mobile robot ro evolve autonomously in a structured environment. Our system is based on the use of two sensors : an odometer and an omnidirectional vision system which gives a reference in connection with a set of natural beacoiw. Our navigation algorithm gives a reliable position estimation thank to a systematic dynamic resetting. To merge our data, we use the Extended Kalrnan Filter ( E m ) . Our proposed method allows us to treat eficiently the noise problems linked to the primitive extraction, which contributes to the robustness of our system. Thus, we get a reliable and quick navigation system which can answer the constraints of security and real time linked to the moving of the robots in an industrial environment. We give the experimental results obtainedRom a mission realized in an a priori known environment.
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