Inverse Dynamics vs. Forward Dynamics in Direct Transcription Formulations for Trajectory Optimization

Henrique Ferrolho, Vladimir Ivan, Wolfgang Merkt, Ioannis Havoutis, Sethu Vijayakumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Benchmarks of state-of-the-art rigid-body dynamics libraries report better performance solving the inverse dynamics problem than the forward alternative. Those bench-marks encouraged us to question whether that computational advantage would translate to direct transcription, where calculating rigid-body dynamics and their derivatives accounts for a significant share of computation time. In this work, we implement an optimization framework where both approaches for enforcing the system dynamics are available. We evaluate the performance of each approach for systems of varying complexity, for domains with rigid contacts. Our tests reveal that formulations using inverse dynamics converge faster, require less iterations, and are more robust to coarse problem discretization. These results indicate that inverse dynamics should be preferred to enforce the nonlinear system dynamics in simultaneous methods, such as direct transcription.
Original languageEnglish
Title of host publication2021 IEEE International Conference on Robotics and Automation (ICRA)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages7
Publication statusAccepted/In press - 28 Feb 2021
Event2021 IEEE International Conference on Robotics and Automation - Xi'an, China
Duration: 30 May 20215 Jun 2021
http://www.icra2021.org/

Conference

Conference2021 IEEE International Conference on Robotics and Automation
Abbreviated titleICRA 2021
CountryChina
CityXi'an
Period30/05/215/06/21
Internet address

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