機械学習による制御バリア関数の合成
Synthesis of Control Barrier Functions Using a Supervised Machine Learning Approach
センサデータからサポートベクターマシンで制御バリア関数を学習し、未知環境でも安全性を保証する手法を提案。LiDAR搭載全方向移動ロボットのROSシミュレーションで検証した。
著者: Mohit Srinivasan, Amogh Dabholkar, Samuel Coogan, Patricio Vela
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Control barrier functions are mathematical constructs used to guarantee safety for robotic systems. When integrated as constraints in a quadratic programming optimization problem, instantaneous control synthesis with real-time performance demands can be achieved for robotics applications. Prevailing use has assumed full knowledge of the safety barrier functions, however there are cases where the safe regions must be estimated online from sensor measurements. In these cases, the corresponding barrier function must be synthesized online. This paper describes a learning framework for estimating control barrier functions from sensor data. Doing so affords system operation in unknown state space regions without compromising safety. Here, a support vector machine classifier provides the barrier function specification as determined by sets of safe and unsafe states obtained from sensor measurements. Theoretical safety guarantees are provided. Experimental ROS-based simulation results for an omnidirectional robot equipped with LiDAR demonstrate safe operation.
