Predicting the metabolic energy costs of bipedalism using evolutionary robotics
W. I. Sellers1,*,
L. A. Dennis2 and
R. H. Crompton3
1 Department of Human Sciences, Loughborough University, Loughborough LE11
3TU, UK
2 School of Computer Science and Information Technology, University of
Nottingham, Jubilee Campus, Nottingham, NG8 1BB, UK
3 Department of Human Anatomy and Cell Biology, University of Liverpool, The
Sherrington Buildings, Ashton Street, Liverpool L69 3GE, UK
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Fig. 1. Diagram illustrating the process of encoding and solving a problem using a
genetic algorithm.
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Fig. 2. Diagram illustrating the 7-segment model used
(Delp et al., 1990 ). The
segments are: 1, head, arms and torso (HAT), origin at the centre of the hip
joint; 2, 3 right and left upper leg, extending from the centre of the hip
joint to the centre of the knee joint; 4, 5 right and left lower leg,
extending from the centre of the knee joint to the centre of the tibio-talar
joint; 6, 7 right and left foot, extending from the tibio-talar joint to the
head of the 1st metatarsal.
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Fig. 3. Diagram illustrating the generic muscle model used. F, force vector;
P, position vector. See text for explanation.
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Fig. 4. Diagram illustrating the encoding used for the genome. Each value in the
list of 35 values was a floating point number between –1 and +1.
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Fig. 5. Graph showing the fitness of the best genome when starting with a randomly
generated population of 100 individuals and a metabolic energy cutoff of 5000
J.
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Fig. 6. Overlay image of the animation generated by the best genome when starting
with a randomly generated population (see
movie1.mov).
The images are 0.2 s apart.
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Fig. 7. Graph showing the fitness of the best genome when starting with a screened
population of 100 individuals and a metabolic energy cutoff of 5000 J.
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Fig. 8. Overlay image of the animation generated by the best genome when starting
with a screened random population (see
movie2.mov).
The images are 0.2 s apart.
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Fig. 9. Graph showing the fitness of the best genome when starting with a
population of 50 individuals, derived from a hand-crafted genome and a
metabolic energy cutoff of 3000 J.
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Fig. 10. Overlay image of the animation generated by the best genome when starting
with a population derived from a hand-crafted genome (see
movie3.mov).
The images are 0.2 s apart.
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Fig. 11. Graph showing the forward displacement of the torso with respect to
time.
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Fig. 12. Graph showing the total metabolic energy cost of the muscles with respect
to time.
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Fig. 13. Graph showing the total metabolic cost of the muscles with respect to the
forward distance traveled.
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Fig. 14. Experimentally derived data on the metabolic energy cost of human
locomotion. Based on data from Alexander
(1992b).
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© The Company of Biologists Ltd 2003
