Safe Robot Trajectory Control Using Probabilistic Movement Primitives and Control Barrier Functions - Davoodi, Mohammadreza; Iqbal, Asif; Cloud, Joseph M.; Beksi, William J.; Gans, Nicholas R. (Frontiers in Robotics and AI, 2022)
In this paper, we present a novel means of control design for probabilistic movement primitives (ProMPs). Our proposed approach makes use of control barrier functions and control Lyapunov functions defined by a ProMP distribution. Thus, a robot may move along a trajectory within the distribution while guaranteeing that the system state never leaves more than a desired distance from the distribution mean. The control employs feedback linearization to handle nonlinearities in the system dynamics and real-time quadratic programming to ensure a solution exists that satisfies all safety constraints while minimizing control effort. Furthermore, we highlight how the proposed method may allow a designer to emphasize certain safety objectives that are more important than the others. A series of simulations and experiments demonstrate the efficacy of our approach and show it can run in real time.