Algorithmic differentiation improves the computational efficiency of OpenSim-based trajectory optimization of human movement


Autoři: Antoine Falisse aff001;  Gil Serrancolí aff002;  Christopher L. Dembia aff003;  Joris Gillis aff004;  Friedl De Groote aff001
Působiště autorů: Department of Movement Sciences, KU Leuven, Leuven, Belgium aff001;  Department of Mechanical Engineering, Universitat Politècnica de Catalunya, Barcelona, Catalunya, Spain aff002;  Department of Mechanical Engineering, Stanford University, Stanford, California, United States of America aff003;  Department of Mechanical Engineering, KU Leuven, Leuven, Belgium aff004;  DMMS Lab, Flanders Make, Leuven, Belgium aff005
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0217730

Souhrn

Algorithmic differentiation (AD) is an alternative to finite differences (FD) for evaluating function derivatives. The primary aim of this study was to demonstrate the computational benefits of using AD instead of FD in OpenSim-based trajectory optimization of human movement. The secondary aim was to evaluate computational choices including different AD tools, different linear solvers, and the use of first- or second-order derivatives. First, we enabled the use of AD in OpenSim through a custom source code transformation tool and through the operator overloading tool ADOL-C. Second, we developed an interface between OpenSim and CasADi to solve trajectory optimization problems. Third, we evaluated computational choices through simulations of perturbed balance, two-dimensional predictive simulations of walking, and three-dimensional tracking simulations of walking. We performed all simulations using direct collocation and implicit differential equations. Using AD through our custom tool was between 1.8 ± 0.1 and 17.8 ± 4.9 times faster than using FD, and between 3.6 ± 0.3 and 12.3 ± 1.3 times faster than using AD through ADOL-C. The linear solver efficiency was problem-dependent and no solver was consistently more efficient. Using second-order derivatives was more efficient for balance simulations but less efficient for walking simulations. The walking simulations were physiologically realistic. These results highlight how the use of AD drastically decreases computational time of trajectory optimization problems as compared to more common FD. Overall, combining AD with direct collocation and implicit differential equations decreases the computational burden of trajectory optimization of human movement, which will facilitate their use for biomechanical applications requiring the use of detailed models of the musculoskeletal system.

Klíčová slova:

Algorithms – Musculoskeletal system – Optimization – Skeletal joints – Source code – Walking – Pendulums – Mumps


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Článek vyšel v časopise

PLOS One


2019 Číslo 10