TAILIEUCHUNG - Adaptive Motion of Animals and Machines - Hiroshi Kimura et al (Eds) Part 5

Tham khảo tài liệu 'adaptive motion of animals and machines - hiroshi kimura et al (eds) part 5', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 76 R. Hackert H. Witte M. S. Fischer Fig. 7. Variations of the angle ulna ground with speed are small. The right scale gives the number of steps N used to calculate the mean values and the standard deviations. 12 . In humans the spring-leg and the mass CoM are well aligned. The above described results indicate that the common linear spring-point mass model may as well be applied to the situation in the pika s forelimbs. In the hindlimbs the consideration of the mass extension of the trunk seems inevitable. The variation of the CoM height found in this study is very similar to that for the dog derived from numerical integration of ground reaction forces by Cavagna et al. 7 . In that case the vertical displacement of the CoM over time showed more than two extrema. McMahon Cheng 13 calculated how the angle of attack of a spring-mass system defined as the angle which minimized the maximum of the force during the stance phase variates as a function of the horizontal and vertical velocity. The variation of this angle with horizontal velocity also is small about 7 . The reasons for an almost constancy of this angle still are poorly understood as far as the dynamics of locomotion is concerned but perhaps may find an explanation by the results of further studies on the dynamic stability of quadrupedal locomotion. Our study shows that the motion of the trunk is a determinant factor in the motion of the CoM. The model of a rigid body that jumps from one limb to the other is not able to explain the variety of the pattern of vertical motions of CoM provoked by running locomotor modes. Bending of the back is not a passive bending due to inertia of the back. For robotics the Raibert idea of minimizing dissipative energy flows in combination with the usage of intelligent self-stabilising mechanics with minimal neuronal computational control effort is attractive. Understanding of motion systems evolutively tested for longer periods in this context may be a promising directive. .

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