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- | === - José Ernesto GOMEZ BALDERAS (HDS) === | + | |
- | PhD title: ** Localisation et commande embarquée d’un drone en utilisant la vision stéréoscopique | + | |
- | Co-advisor: Rogelio LOZANO | + | ====== José Ernesto GOMEZ BALDERAS (HDS) ====== |
- | Grant from Mexicain Government | + | |
- | Date PhD finished: November, 2011 \\ | + | \\ |
- | Current position: **MdC at GIPSA Lab* | + | \\ |
+ | |||
+ | **PhD title: | ||
+ | == Localisation et commande embarquée d’un drone en utilisant la vision stéréoscopique | ||
+ | |||
+ | |||
+ | \\ | ||
+ | |||
+ | Co-advisor: Rogelio LOZANO \\ | ||
+ | Grant from __Mexicain Government__ \\ | ||
+ | Location: Heudiasyc | ||
+ | Date PhD finished: | ||
+ | \\ | ||
+ | __Current position__: **MdC at GIPSA Lab since 2013**\\ | ||
+ | |||
+ | \\ | ||
+ | |||
+ | |||
+ | |||
+ | == Abstract == | ||
+ | |||
+ | Visual servoing is a control approach based on visual information. In this thesis, | ||
+ | visual servoing schemes are proposed to control a quadrotor and an octarotor applied | ||
+ | to positioning and navigation task. Concerning the quadrotor we use a hierarchical | ||
+ | control scheme whose inner-loop (fast dynamic) focuses on attitude dynamics, while | ||
+ | outer-loop (slow dynamics) deals with translational dynamics.\\ | ||
+ | |||
+ | Also, a nonlinear controller based on separated saturations for a quadrotor is | ||
+ | proposed to stabilize it attitude. The linear position and velocity of the rotorcraft are | ||
+ | obtained by using a vision-based algorithm via a monocular caméra. The dynamic | ||
+ | model of the quadrotor is presented using the Newton-Euler formalism. | ||
+ | In other vision system, two cameras are used to estimate the translational position | ||
+ | and velocity of the vehicle. Position was obtained using a frontal camera looking | ||
+ | at a target placed on a wall. Quadrotor velocity was estimated using a camera pointing | ||
+ | vertically downwards running an optical flow algorithm. Experimental tests | ||
+ | showed that the quadrotor performed well at hover flight using the proposed vision | ||
+ | based control system.\\ | ||
+ | |||
+ | – **Quadrotor vision-based**\\ | ||
+ | |||
+ | The same system was used to estimate the 3D position of the quadrotor over | ||
+ | a trajectory using vanishing points. The performance of the vision and control | ||
+ | algorithms has been tested in a real application by a quadrotor tracking a line | ||
+ | painted in a wall. Similarly the velocity estimation is obtained using an optic | ||
+ | flow algorithm. The estimated position and velocity information obtained | ||
+ | from the vision system is combined with the angular rates and displacements | ||
+ | of the inertial measurement unit to compute the control inputs. It has been | ||
+ | shown that the proposed control scheme achieves the tracking objective of | ||
+ | the visual reference.\\ | ||
+ | |||
+ | – **Octarotor vision-based** \\ | ||
+ | |||
+ | In this thesis, it is presented a visual feedback a control of an octarotor | ||
+ | using image-based visual servoing (IBVS) with stereo vision. Autonomous | ||
+ | control of an UAV requires a precise measurements and/or estimation of the | ||
+ | vehicle’s pose and also the knowledge of its surrounding environment. In | ||
+ | order to control the orientation and the position of flying robot with respect | ||
+ | to a target, we propose to use a navigation system based on binocular vision | ||
+ | system combined with inertial sensors. This combination of sensors, allows | ||
+ | us to get a complete characterization of the state of aerial vehicle. In other | ||
+ | words, using the stereo vision system we are able to estimate the UAV’s | ||
+ | 3D position, while from the inertial sensors we obtain the orientation of | ||
+ | rotorcraft. A semi-embedded navigation system combining stereo vision with | ||
+ | inertial information is proposed.\\ | ||
+ | |||
+ | The hierarchical control approach is appropriate to stabilize the 6DOF dynamics | ||
+ | of the quadrotor, it takes advantage of the time scale separation between rotational | ||
+ | (fast) and translational (slow) dynamics. For this reason, despite the lower frequency | ||
+ | rate of vision-based measurements is able to stabilize in real-time the quadrotor | ||
+ | translational dynamics. This combination of measurement strategies has many advantages | ||
+ | because one works very well at low speeds (vision system) and the other at | ||
+ | high speeds (inertial sensors). Both work at different sample rate. Taking advantage | ||
+ | of this property we have obtained a simplified dynamical model of the rotorcraft. | ||
+ | This model is given by six independent double integrators which have been stabilized | ||
+ | using proportional-derivative (PD) control. The real-time experiments have | ||
+ | shown an acceptable performance of the flying machine applying the control law | ||
+ | and sensing system proposed.\\ | ||
+ | |||
+ | An embedded control system for the mini rotorcraft is implemented. The control | ||
+ | is validated by experimental tests. Experimental results show that the implementation | ||
+ | of the control law on an embedded control system is satisfactory for autonomous | ||
+ | hovering in indoors and outdoors with light or no wind. Real time experiences are | ||
+ | developed to validate the performance of navigation systems proposed. This work | ||
+ | highlights the potential of the computer vision based position control strategies for | ||
+ | UAV. | ||
+ | |||
+ | \\ | ||
+ | \\ | ||
+ | \\ |