カルマンフィルタによるオンラインシステム同定を用いた四足歩行ロボットの適応モデルベース制御
Adaptive Model-Base Control of Quadrupeds via Online System Identification using Kalman Filter
カルマンフィルタで四足ロボットの質量と重心をオンライン推定し、ペイロード変化に適応するモデルベース制御を実現した。
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著者: Jonas Haack, Franek Stark, Shubham Vyas, Frank Kirchner, Shivesh Kumar
分類: cs.RO
原文アブストラクト
Many real-world applications require legged robots to be able to carry variable payloads. Model-based controllers such as model predictive control (MPC) have become the de facto standard in research for controlling these systems. However, most model-based control architectures use fixed plant models, which limits their applicability to different tasks. In this paper, we present a Kalman filter (KF) formulation for online identification of the mass and center of mass (COM) of a four-legged robot. We evaluate our method on a quadrupedal robot carrying various payloads and find that it is more robust to strong measurement noise than classical recursive least squares (RLS) methods. Moreover, it improves the tracking performance of the model-based controller with varying payloads when the model parameters are adjusted at runtime.
