Safe Robot Trajectory Control Using Probabilistic Movement Primitives and Control Barrier Functions

Frontiers in Robotics and AI

Joseph M. Cloud¹  William J. Beksi¹  Nicholas R. Gans²  Mohammadreza Davoodi²  Asif Iqbal² 

[1] Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, United States;The University of Texas at Arlington Research Institute, Fort Worth, TX, United States;

关键词: motion control;    movement primitives;    learning from demonstration;    robot safety;    nonlinear control;   

DOI  :  10.3389/frobt.2022.772228

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown