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, manipulationdriven 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 softwarexe2x80x99s dataflow from the control flow. Thereafter, we demonstrate those two simple, yet effective strategies to overcome the limitations along various case studies.

Benchmarking and Workload Analysis of Robot Dynamics Algorithms Rigid body dynamics calculations are needed for many tasks in robotics, including online control. While there currently exist several competing software implementations that are sufficient for use in traditional control approaches, emerging sophisticated motion control techniques such as nonlinear model predictive control demand orders of magnitude more frequent dynamics calculations. Current software solutions are not fast enough to meet that demand for complex robots. The goal of this work is to examine the performance of current dynamics software libraries in detail. In this paper, we (i) survey current stateoftheart software implementations of the key rigid body dynamics algorithms (RBDL, Pinocchio, RigidBodyDynamics.jl, and RobCoGen), (ii) establish a methodology for benchmarking these algorithms, and (iii) characterize their performance through real measurements taken on a modern hardware platform. With this analysis, we aim to provide direction for future improvements that will need to be made to enable emerging techniques for realtime robot motion control. To this end, we are also releasing our suite of benchmarks to enable others to help contribute to this important task.