A general framework for modeling and dynamic simulation of multibody systems using factor graphs
This work provides a unified and intuitive approach for modeling and simulating multibody systems, which could benefit researchers and engineers working on motion estimation and parameter identification of complex mechanical systems.
This paper introduces a novel factor graph-based framework for modeling and dynamically simulating multibody systems, addressing both kinematic and dynamic problems. The framework was tested extensively and validated against commercial multibody software, showing comparable accuracy with classical approaches for forward and inverse dynamics simulations.
In this paper, we present a novel general framework grounded in the factor graph theory to solve kinematic and dynamic problems for multi-body systems. Although the motion of multi-body systems is considered to be a well-studied problem and various methods have been proposed for its solution, a unified approach providing an intuitive interpretation is still pursued. We describe how to build factor graphs to model and simulate multibody systems using both, independent and dependent coordinates. Then, batch optimization or a fixed-lag-smoother can be applied to solve the underlying optimization problem that results in a highly-sparse nonlinear minimization problem. The proposed framework has been tested in extensive simulations and validated against a commercial multibody software. We release a reference implementation as an open-source C++ library, based on the GTSAM framework, a well-known estimation library. Simulations of forward and inverse dynamics are presented, showing comparable accuracy with classical approaches. The proposed factor graph-based framework has the potential to be integrated into applications related with motion estimation and parameter identification of complex mechanical systems, ranging from mechanisms to vehicles, or robot manipulators.