Variational integrators and graph-based solvers for multibody dynamics in maximal coordinates

• Published in: Multibody system dynamics Vol. 61; no. 3; pp. 381 - 414
• Main Authors: Brüdigam, Jan, Sosnowski, Stefan, Manchester, Zachary, Hirche, Sandra
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.07.2024; Springer Nature B.V
• Subjects: Algorithms; Automotive Engineering; Constraints; Control; Dynamical Systems; Electrical Engineering; Engineering; Exoskeletons; Integrators; Kinematics; Mechanical Engineering; Mechanical systems; Optimization; Robotics; Robustness (mathematics); Simulators; Solvers; Vibration; Maximal coordinates; Multibody dynamics; Variational integrators; Simulation
• ISSN: 1384-5640; 1573-272X
• DOI: 10.1007/s11044-023-09949-x

Multibody dynamics simulators are an important tool in many fields, including learning and control in robotics. However, many existing dynamics simulators suffer from inaccuracies when dealing with constrained mechanical systems due to unsuitable integrators with bad energy behavior and problematic constraint violations, for example in contact interactions. Variational integrators are numerical discretization methods that can reduce physical inaccuracies when simulating mechanical systems, and formulating the dynamics in maximal coordinates allows for easy and numerically robust incorporation of constraints such as kinematic loops or contacts. Therefore, this article derives a variational integrator for mechanical systems with equality and inequality constraints in maximal coordinates. Additionally, efficient graph-based sparsity-exploiting algorithms for solving the integrator are provided and implemented as an open-source simulator. The evaluation of the simulator shows improved physical accuracy due to the variational integrator and the advantages of the sparse solvers. Comparisons to minimal-coordinate algorithms show improved numerical robustness, and application examples of a walking robot and an exoskeleton with explicit constraints demonstrate the necessity and capabilities of maximal coordinates.