By Kimon P. Valavanis, Randal Beard, Paul Oh, Aníbal Ollero, Leslie A. Piegl, Hayong Shin
This quantity contains chosen peer reviewed papers from the 2d foreign Symposium on Unmanned Aerial autos, June 8-10, 2009, Reno Nevada, united states. The ebook specializes in UAV advances and present state of the art. the yearly assembly discusses the latest advances within the box world wide, therefore, the publication serves the aim on publishing the most recent findings. The e-book displays the most recent study findings in UAVs, targeting modeling, navigation, aim monitoring, making plans, imaginative and prescient, simulation structures, education, touchdown structures, and several other functions. it could function a reference for the newest advances in UAV learn.
Read or Download Selected papers from the 2nd International Symposium on UAVs, Reno, U.S.A. June 8-10, 2009 PDF
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Additional info for Selected papers from the 2nd International Symposium on UAVs, Reno, U.S.A. June 8-10, 2009
The full model consists of 18 states and four inputs (two motors and two servos): x = [x, y, z, u, v, w, φ, θ, ψ, p, q, r, αdw , βdw , αup , βup , u = [umot,dw , umot,up , userv1 , userv2 ] . T dw , up ] T , (19) As an overview, the system is shown as a block diagram in Fig. 6. 1. 4 Parameter Identiﬁcation After setting up the physical equations, the missing parameters have to be adjusted to the real helicopter. Without appropriate parameters, the best model is worthless. 1 Mechanical Parameters The identiﬁcation process is a non trivial task, and it is hard to ﬁnd the correct parameters, especially since most of the parameters are coupled.
6. 1. 4 Parameter Identiﬁcation After setting up the physical equations, the missing parameters have to be adjusted to the real helicopter. Without appropriate parameters, the best model is worthless. 1 Mechanical Parameters The identiﬁcation process is a non trivial task, and it is hard to ﬁnd the correct parameters, especially since most of the parameters are coupled. In order to minimize the complexity of the identiﬁcation on ﬂight data, it is necessary to measure or estimate as many parameters as possible beforehand.
Figure 7 shows the schematic view of the embedded controller using a TMS320F2812 DSP controller. 2 Vision Based Localization Figure 8 shows the scheme of the vision based localization. As the payload of the UAV is limited, red and green LED markers are attached at the bottom of the UAV, and a CCD camera is put on the ground. The CCD camera gets the image of the LED markers, and the image processor analyzes the color distribution of the markers, extract the center of each marker and ﬁnds the position and orientation of the markers as shown in Fig.
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