By Edward Y. L. Gu
This booklet offers readers with a pretty good set of assorted and crucial instruments for the theoretical modeling and keep an eye on of complicated robot platforms, in addition to for electronic human modeling and sensible movement iteration. Following a accomplished advent to the basics of robot kinematics, dynamics and keep watch over structures layout, the writer extends robot modeling systems and movement algorithms to a far higher-dimensional, higher scale and extra subtle examine sector, particularly electronic human modeling. lots of the equipment are illustrated by means of MATLAB codes and pattern graphical visualizations, supplying a distinct closed loop among conceptual figuring out and visualization.
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Extra info for A Journey from Robot to Digital Human: Mathematical Principles and Applications with MATLAB Programming
IEEE Intelligent Systems Journal 15(4), 5763 (2000) 17. org/wiki/Robonaut 18. : On Simulating Human Reach Motions for Ergonomics Analysis. Human Factors and Ergonomics in Manufacturing 12(3), 235–247 (2002) 19. : Digital Human Modeling for Workspace Design. In: Reviews of Human Factors and Ergonomics, vol. 4, p. 41. 1518/155723408X342844 20. : Digital Human Models: An Overview of Development and Applications in Product and Workplace Design. In: Proceedings of Tools and Methods of Competitive Engineering (TMCE) 2010 Symposium, Ancona, Italy, April 12-16, pp.
In this case, your axis k becomes negative accordingly. The two picks are obviously equivalent, because rotating a frame about an axis by an angle will always have the same destination as rotating the original frame about the opposite axis by the negative angle in 3D space. The second practical example is to ﬁnd a new frame 1 such that its z1 axis is along a vector z01 = (1 − 1 − 1)T that is referred to the base frame 0. While it may have a few diﬀerent approaches to ﬁnding the orientation R01 of frame 1, we just try the k − φ procedure to do so.
This example will be revisited again in Chapter 3. 8), we immediately obtain Rk = k + sin φKk + (1 − cos φ)K 2 k = k because Kk = k × k ≡ 0. This result shows that the unit vector k in the k–φ procedure is actually the eigenvector of the rotation matrix R corresponding to its eigenvalue +1. 11) to determine K to avoid the possible singularity. 0000 >> As you can see clearly, once the internal function eig(R) is called, the element “val” prints the three eigenvalues along the diagonal of the output matrix, while “vec” prints the three column eigenvectors for the input matrix R.