ニューラルネット時相量子化ラグランジュ力学と蟻コロニー最適化による9リンク二足歩行ロボットの階段昇降サイクロイド軌道生成
Cycloidal Trajectory Realization on Staircase based on Neural Network Temporal Quantized Lagrange Dynamics (NNTQLD) with Ant Colony Optimization for a 9-Link Bipedal Robot
つま先付き9リンク二足歩行ロボットの階段昇降のため、階段寸法に応じたサイクロイド軌道計画と逆運動学をニューラルネットで解き、ラグランジュ力学に基づく動力学モデルと蟻コロニー最適化でPD制御器を調整して関節軌道誤差と消費エネルギーを最小化する手法を提案した論文。
著者: Gaurav Bhardwaj, Utkarsh A. Mishra, N. Sukavanam, R. Balasubramanian
分類: cs.RO, cs.NE, cs.SY, eess.SY
原文アブストラクト
In this paper, a novel optimal technique for joint angles trajectory tracking control with energy optimization for a biped robot with toe foot is proposed. For the task of climbing stairs by a 9-link biped model, a cycloid trajectory for swing phase is proposed in such a way that the cycloid variables depend on the staircase dimensions. Zero Moment Point(ZMP) criteria is taken for satisfying stability constraint. This paper mainly can be divided into 3 steps: 1) Planning stable cycloid trajectory for initial step and subsequent step for climbing upstairs and Inverse Kinematics using an unsupervised artificial neural network with knot shifting procedure for jerk minimization. 2) Modeling Dynamics for Toe foot biped model using Lagrange Dynamics along with contact modeling using spring-damper system followed by developing Neural Network Temporal Quantized Lagrange Dynamics which takes inverse kinematics output from neural network as its inputs. 3) Using Ant Colony Optimization to tune PD (Proportional Derivative) controller parameters and torso angle with the objective to minimize joint space trajectory errors and total energy consumed. Three cases with variable staircase dimensions have been taken and a brief comparison is done to verify the effectiveness of our proposed work Generated patterns have been simulated in MATLAB .
