Publication
Modular and Hybrid Numerical-Analytical Approach - A Case Study on Improving Computational Efficiency for Series-Parallel Hybrid Robots
Rohit Kumar; Shivesh Kumar; Andreas Mueller; Frank Kirchner
In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-2022), October 23-27, Kyoto, Japan, IEEE, 11/2022.
Abstract
Modeling closed loop mechanisms is a necessity
for the control and simulation of various systems and poses
a great challenge to rigid body dynamics algorithms. Solving
the forward and inverse dynamics for such systems require
resolution of loop closure constraints which are often solved
via numerical procedures. This brings an additional burden to
these algorithms as they have to stabilize and control the loop
closure errors. In order to avoid this issue, analytical solutions
are preferred for commonly studied parallel mechanisms. This
paper has two contributions. Firstly, it reports a case study on
a modular and hybrid numerical-analytical approach to model
and control series-parallel hybrid robots which are subjected to
large number of holonomic constraints. The approach exploits
the modularity in the robot design to combine analytical loop
closure for the known submechanisms and numerical loop
closure for submechanisms where analytical solutions are not
available. This offers an edge over purely numerical approach in
terms of computational efficiency. Secondly, an adaption of the
constraint embedding approach in Articulated Body Algorithm
(ABA) is presented which yields a recursive algorithm in minimal
coordinates for computing the forward dynamics of seriesparallel
hybrid systems. The proposed modification exploits
the Lie group formulations and allows easy implementation
of recursive forward dynamics of constrained systems in state
of the art multi-body solvers.