Exploiting linearity in dynamics solvers for the design of composable robotic manipulation architectures

We investigate two major limiting factors in the design and implementation of modern dynamics solvers that interfere with their full utilization in versatile, manipulation-driven robotic software architectures. The first limitation originates from the design of those solvers which aims at computational efficiency while neglecting composability. Instead, we advocate to design the solvers in such a way that they exploit linearity in the equations of motion to fully decompose the state of a kinematic chain. This enables a versatile recomposition and more flexible applications. Secondly, we have observed that most implementations follow the programming principle of information hiding. Consequently, the internal state that is used to compute motion control commands is withheld from other parts of the software architecture. We tackle this problem by following a dataflow programming paradigm and separating the software's dataflow from the control flow. Thereafter, we demonstrate those two simple, yet effective strategies to overcome the limitations along various case studies.