Comparison of Multibody Dynamics Solver Performance: Synthetic Versus Realistic Data

Abstract

In the area of robotics simulation, multibody dynamics plays an important role in designing and controlling robots, especially when the robot contacts the environment. Contacts give rise to non-penetration and friction constraints, which are nonsmooth and nonlinear. One way to simulate such systems is through the use of a discrete-time multibody dynamics model in the form of a nonlinear complementarity problem (NCP), for which, finding a solution is known to be NP-hard [1]. In situations where analytical solutions don't exist, a suite of numerical solutions accessible through a benchmarking framework is useful to fairly evaluate performance of different computer algorithms. However, many algorithm designers don't have easy access to test data from physical simulators. Under such circumstances, randomized data are used to test the performance of solution algorithms.In this paper, we present our Benchmark Problems of Multibody Dynamics (BPMD) framework and database, with the data sets from different physics engines, as a benchmarking platform. Then we compare the performance of several solvers on synthetic data and simulation data, to show the superiority of testing solution algorithms with simulation data over testing with synthetic data. We will show that algorithm tested only on synthetic data often fail to solve problem obtained from physics simulations, to demonstrate the benefit of BPMD database.