制御バリア関数と学習による弾性関節ロボットの安全制御
Safe Learning-Based Control of Elastic Joint Robots via Control Barrier Functions
弾性関節ロボットの未知ダイナミクスをガウス過程回帰で学習し、制御バリア関数に基づく安全条件を二次錐計画でオンライン実行することで、安全性を保証する手法を提案した。
著者: Armin Lederer, Azra Begzadić, Neha Das, Sandra Hirche
分類: eess.SY, cs.RO, cs.SY
原文アブストラクト
Ensuring safety is of paramount importance in physical human-robot interaction applications. This requires both adherence to safety constraints defined on the system state, as well as guaranteeing compliant behavior of the robot. If the underlying dynamical system is known exactly, the former can be addressed with the help of control barrier functions. The incorporation of elastic actuators in the robot's mechanical design can address the latter requirement. However, this elasticity can increase the complexity of the resulting system, leading to unmodeled dynamics, such that control barrier functions cannot directly ensure safety. In this paper, we mitigate this issue by learning the unknown dynamics using Gaussian process regression. By employing the model in a feedback linearizing control law, the safety conditions resulting from control barrier functions can be robustified to take into account model errors, while remaining feasible. In order to enforce them on-line, we formulate the derived safety conditions in the form of a second-order cone program. We demonstrate our proposed approach with simulations on a two-degree-of-freedom planar robot with elastic joints.
