Whole-Body Control of Series-Parallel Hybrid Robots

Parallel mechanisms are becoming increasingly popular as subsystems in various robots due to their superior stiffness, payload-to-weight ratio, and dynamic properties. The serial connection of parallel subsystems leads to series-parallel hybrid robots, which are more difficult to model and control than serial or tree-type systems. At the same time, Whole-Body Control (WBC) has become the method of choice in the control of robots with redundant degrees of freedom, e.g., legged robots. However, most state-of-the-art WBC frameworks can only deal with serial or tree-type robot topologies. In this paper, we describe a computationally efficient framework for Whole-Body Control of series-parallel hybrid robots subjected to a large number of holonomic constraints. In contrast to existing WBC frameworks, our approach describes the optimization problem in the actuation space of a series-parallel robot, which provides better exploitation of the feasible workspace, higher accuracy, and more transparent behavior near singularities. We evaluate the proposed framework on two different humanoids with series-parallel architecture and compare its performance to a WBC approach for tree-type robots.