Sampling-based Near Time-Optimal Trajectory Generation for Pneumatic Drives

When servo-pneumatic drives are applied in automation, their motion trajectories should be fast to maximize productivity. There occur nonlinear state-dependent jerk constraints because the pressure dynamics are not negligibly fast, the air mass flow through the valves is subject to pressure-dependent constraints, and the mechanics and pneumatics are coupled. The goal of this work is to generate near time-optimal trajectories for pneumatic drives, taking into account the aforementioned in a model-based way. To this end, first, the system dynamics and constraints are formulated using differential flatness such that they can be incorporated into trajectory generation frameworks. Then, the class of sampling-based near time-optimal path parametrization approaches, which build a tree of samples in the path parameter space, is chosen and extended to the present type of constraints. Results for various scenarios are discussed, compared to our previous work where nonlinear programming was applied, and validated in real-world experiments. The experimental outcomes demonstrate the applicability of the sampling-based algorithm to the present system.